diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml index edba43b..071fa48 100644 --- a/.github/workflows/pylint.yml +++ b/.github/workflows/pylint.yml @@ -17,7 +17,12 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - pip install pylint + pip install pylint autopep8 + - name: Fixing Indentation with AutoPEP8 + working-directory: ./src/MAGIST + run: | + autopep8 --in-place --recursive . - name: Analysing the code with pylint + working-directory: ./src/MAGIST run: | - pylint $(git ls-files '*.py') + pylint $(git ls-files '*.py') --disable=C0103 --fail-under=3 diff --git a/.gitignore b/.gitignore index 2c062ea..ba30966 100644 --- a/.gitignore +++ b/.gitignore @@ -178,3 +178,8 @@ testing/TF_Custom_Detector/cocoapi/PythonAPI/pycocotools/_mask.so testing/TF_Custom_Detector/cocoapi/PythonAPI/pycocotools/coco.pyc testing/TF_Custom_Detector/cocoapi/PythonAPI/pycocotools/cocoeval.pyc testing/TF_Custom_Detector/cocoapi/PythonAPI/pycocotools/mask.pyc + + +# Added by cargo + +/target diff --git a/.idea/MAGIST-Algorithm.iml b/.idea/MAGIST-Algorithm.iml index 2cb3f33..52e7d6d 100644 --- a/.idea/MAGIST-Algorithm.iml +++ b/.idea/MAGIST-Algorithm.iml @@ -2,11 +2,13 @@ + + - + diff --git a/.idea/misc.xml b/.idea/misc.xml index 34a91dc..58f07a9 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -1,4 +1,4 @@ - + \ No newline at end of file diff --git a/Cargo.lock b/Cargo.lock new file mode 100644 index 0000000..f222b4b --- /dev/null +++ b/Cargo.lock @@ -0,0 +1,14 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 3 + +[[package]] +name = "MAGIST-Algorithm" +version = "0.1.0" +dependencies = [ + "MAGIST_Rust", +] + +[[package]] +name = "MAGIST_Rust" +version = "0.1.0" diff --git a/Cargo.toml b/Cargo.toml new file mode 100644 index 0000000..832ff3c --- /dev/null +++ b/Cargo.toml @@ -0,0 +1,9 @@ +[package] +name = "MAGIST-Algorithm" +version = "0.1.0" +edition = "2021" + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +[dependencies] +MAGIST_Rust = { path="./src/MAGIST/MAGIST_Rust" } \ No newline at end of file diff --git a/README.md b/README.md index 00989c3..c8a7fde 100644 --- a/README.md +++ b/README.md @@ -10,14 +10,12 @@ Multi-Agent Generally Intelligent Simultaneous Training Algorithm for Project Ze *** -## Working Principal -Here is a flow diagram with the entire system drawn: -https://lucid.app/documents/embeddedchart/6d04c807-0b77-495e-9b84-3abf38f32630 +## Working Principle +![Drawing](https://user-images.githubusercontent.com/85193239/181803134-320f9493-aea7-4350-8b68-c7f7d62f8293.png) + ### Data -The data is the most important element as the entire intelligence works around it. That is why it is important for the -AI to process it and provide reasonable assumptions. There is another condition, however: the algorithm, in its finished -state, MUST be strictly Python code. This means no pre-trained models or presets. It must find its data and process it unsupervised. This structure is called a "semi-supervised" structure. Here, the data will assume the following structure: +The data is the most crucial element as the entire intelligence works around it. That is why the AI needs to process it and provide reasonable assumptions. There is another condition, however: the algorithm, in its finished state, MUST be strictly Python code. This means no pre-trained models or presets. It must find its data and process it unsupervised. Although this architecture seems hard-coded in the present state, as more functionality is added, it will be more intelligent and decisive. Here, the data will assume the following structure: ``` Object -> Common Associated Verbs, Synonyms, Events, Timestamps of usage, Nearby Objects, etc. @@ -133,15 +131,15 @@ pip3 install MAGIST-Algorithm-x.x.x-py3-none-any.whl ## Contributing Your contribution, monetary or programmatically, is crucial for the rapid development of the algorithm and its training. -Please consider contributing. Even the smallest change to my README will be greatly appreciated. +Please consider contributing. Even minute changes to our README will be greatly appreciated. ### Project Zeta -We are building a fully biomimetic robot dog to implement MAGIST into. This will serve as the gateway between the AI and the physical world. +We are building a fully biomimetic robot dog to implement MAGIST into. This will serve as the gateway between MAGIST and the physical world. -[![Readme Card](https://github-readme-stats.vercel.app/api/pin/?username=DeepShift-Labs&repo=Project-Zeta)](https://github.com/DeepShift-Labs/Project-Zeta) +[![Readme Card](https://github-readme-stats-git-masterrstaa-rickstaa.vercel.app/api/pin/?username=DeepShift-Labs&theme=transparent&repo=Project-Zeta)](https://github.com/DeepShift-Labs/Project-Zeta) *** # Disclaimer -Artificial Intelligence is a powerful field **meant only for research and study**, and it should be kept that way. Unethical use of the AI can have severe repercussions for society and the perpetrator. DeepShift Labs and any programs it develops are strictly for research purposes. Hence, all of our products, MAGIST included, are to be used **strictly** for research purposes. Misuse of this program can lead to heavy fines and prosecution. +Artificial Intelligence is a powerful field **meant only for research and study**, and it should be kept that way. The unethical use of MAGIST can have severe repercussions for society and the perpetrator. DeepShift Labs and any programs it develops are strictly for research purposes. Hence, all of our products, MAGIST included, are to be used **strictly** for research purposes. Misuse of this program can lead to heavy fines and prosecution. Furthermore, to retitle, rebrand, or redistribute without **explicitly** crediting DeepShift Labs is illegal. diff --git a/build_conventional/src/MAGIST/NLP/AudioTranscriber.py b/build_conventional/src/MAGIST/NLP/AudioTranscriber.py new file mode 100644 index 0000000..6b139de --- /dev/null +++ b/build_conventional/src/MAGIST/NLP/AudioTranscriber.py @@ -0,0 +1,82 @@ +"""This has classes to process audio files and microphone data and transcribe them. + +This main class uses the Google Speech API to transcribe audio files. It contains 2 main functions for microphone and +audio file processing respectively. +""" + +import speech_recognition as sr +import time +import pathlib + +from ..Utils.LogMaster.log_init import MainLogger + +class GoogleAudioTranscriber(): + """Google Audio Transcriber Class.""" + + def __init__(self, config): + """Initialize the Google Audio Transcriber and Microphone object. + + :param config: The config file(config.json). + """ + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("GoogleAudioTranscriber") # Create a script specific logging instance + + self.log.info("GoogleAudioTranscriber Recognizer Initialized Successfully") + + def microphone_listener(self): + """Listen to the microphone and transcribe the audio. + + :return: The transcription of the audio as a string. + """ + + self.r = sr.Recognizer() + self.m = sr.Microphone() + + with self.m as source: + self.log.info("GoogleAudioTranscriber Listening...") + audio = self.r.listen(source) + self.log.info("GoogleAudioTranscriber Listening Complete") + try: + start = time.time() + transcription = self.r.recognize_google(audio) + self.log.info("Transcribed Prediction: " + transcription) + end = time.time() + except sr.UnknownValueError: + self.log.warning("Google could not understand audio") + + self.log.info("Time taken: " + str(end - start)) + return transcription + + def file_transcriber(self, file): + """Transcribe an audio file. + + :param file: The file to be transcribed. + + :return: The transcription of the audio as a string. + """ + + self.r = sr.Recognizer() + self.m = sr.Microphone() + + file = pathlib.Path(file) + file = file.resolve() # Find absolute path from a relative one. + file = str(file) + + audio = sr.AudioFile(file) + + with audio as source: + self.log.info("GoogleAudioTranscriber Processing...") + audio = self.r.record(source) + self.log.info("GoogleAudioTranscriber Processing Complete") + try: + start = time.time() + transcription = self.r.recognize_google(audio) + self.log.info("Transcribed Prediction: " + transcription) + end = time.time() + except sr.UnknownValueError: + self.log.warning("Google could not understand audio") + + self.log.info("Time taken: " + str(end - start)) + + return transcription \ No newline at end of file diff --git a/build_conventional/src/MAGIST/NLP/SelfAttention.py b/build_conventional/src/MAGIST/NLP/SelfAttention.py new file mode 100644 index 0000000..d93b713 --- /dev/null +++ b/build_conventional/src/MAGIST/NLP/SelfAttention.py @@ -0,0 +1,246 @@ +"""Contains all necessary functions to train and use unsupervised text prioritization. + +This class has numerous functions to run the text prioritization algorithm. You can simply call the class instance to +run the entire algorithm. +""" + + +import tensorflow as tf +import numpy as np +from ..Utils.LogMaster.log_init import MainLogger + +class TextPreprocessing(): + def __init__(self, config): + """Initializes the TextPreprocessing class and config. + + :param config: The config file containing all necessary parameters(config.json). + """ + root_log = MainLogger(config) + self.log = root_log.StandardLogger("TextPreprocessing") # Create a script specific logging instance + + def __format_data(self, in_text): + """Formats the input text to be compatible with the model. + + :param in_text: The text(string) to be formatted. + + :return: The formatted text. + """ + punctuation = [".", ",", ";", ":", "!", "?", "\"", "'", "`", "~", "`", "´", "’", "‘", "“", "”", "„", "‟", "‹", "›", + "«", "»", "‹", "›", "‘", "’", "“", "”", "„", "‟", "‹", "›", "«", "»", "‹", "›", "‘", "’", "“", "”", + "„", + "‟", "‹", "›", "«", "»", "‹", "›", "‘", "’", "“", "”", "„", "‟", "‹", "›", "«", "»", "‹", "›", "‘", + "’", + "“", "”", "„", "‟", "‹", "›", "«", "»", "‹", "›", "‘", "’", "“", "”", "„", "‟", "‹", "›", "«", "»", + "‹", + "›", "‘", "’", "“", "”", "„", "‟", "‹", "›", "«", "»", "‹", "›", "‘", "’", "“", "”", "„", "‟", "‹", + "›", + "«", "»", "‹", "›", "‘", "’", "“", "”", "„", "‟", "‹", "›", "«", "»", "‹", "›", "‘", "’", "“", "”", + "„", + "‟", "‹", "›", "«", "»", "‹", "›", "‘", "’"] + def __remove_puctuation(text): + """Removes punctuation from the text. + + :param text: The text to be formatted. + + :return: The formatted text. + """ + for i in punctuation: + text = text.replace(i, "") + return text + + def __lowercase(text): + """Converts the text to lowercase. + + :param text: The text to be formatted. + + :return: The formatted text. + """ + return text.lower() + + in_text = __remove_puctuation(in_text) + in_text = __lowercase(in_text) + return in_text + + def __tokenize(self, input_text): + """Splits the input text into tokens. + + :param input_text: The text to be split. + + :return: The tokens. + """ + input_text = self.__format_data(input_text) + return input_text.split(" ") + + def split_positional_encodings(self, input_text): + """Splits the input text into positional encodings. + + :param input_text: The text to be split. + + :return: The positional encodings. + """ + input_text = self.__format_data(input_text) + split_text = self.__tokenize(input_text) + + position_dict = [] + count = 1 + for s in split_text: + position_dict.append([count, s]) + count += 1 + + return position_dict + + def vectorize_text(self, raw_input_text): + """Vectorizes the input text. + + :param raw_input_text: The text to be vectorized. + + :return: The vectorized text. + """ + input_text = self.__format_data(raw_input_text) + self.vectorize_layer = tf.keras.layers.TextVectorization() + self.vectorize_layer.adapt([raw_input_text]) + vec = self.vectorize_layer(raw_input_text) + + return np.array(vec) + + def __positional_embedding_function(self, n, scale=0.1, scalar=1): + """Computes the positional embedding. + + :param n: The position to be computed. + :param scale: The scale of the positional embedding function. + :param scalar: The scalar multiplier to the output of the positional embedding. + + :return: The positional embedding. + """ + x = np.multiply(n, scale) + part1 = np.sin(np.multiply(n, x)) + part2 = np.sin(np.divide(1, x)) + return np.multiply(np.abs(np.multiply(part1, part2)), scalar) + + def positional_embedding(self, split_positional_encodings, vectorized_text, scalar=3): + """Computes the positional embedding. + + :param split_positional_encodings: The positional encodings to be computed. + :param vectorized_text: The vectorized text. + :param scalar: The scalar multiplier to the output of the positional embedding. + + :return: The positional embedding. + """ + position_computed = [] + for i in split_positional_encodings: + pos = self.__positional_embedding_function(i[0], scalar=scalar) + position_computed.append(pos) + + final_position_embedding = [] + + for i in range(len(position_computed)): + final_position_embedding.append(int(vectorized_text[i]) + position_computed[i]) + + return final_position_embedding + + def SelfAttention(self, value, query, key, in_len): + """Computes the self attention. + + :param value: The value to be computed. + :param query: The query to be computed. + :param key: The key to be computed. + :param in_len: The length of the input. + + Note: The value, query, and key must be the same length and are the same thing usually for self-attention. + """ + value_mod = tf.keras.models.Sequential() + value_mod.add(tf.keras.layers.Dense(in_len, use_bias=False)) + + key_mod = tf.keras.models.Sequential() + key_mod.add(tf.keras.layers.Dense(in_len, use_bias=False)) + + query_mod = tf.keras.models.Sequential() + query_mod.add(tf.keras.layers.Dense(in_len, use_bias=False)) + + # value = value_mod.predict(value) + # key = key_mod.predict(key) + # query = query_mod.predict(query) + + value = np.array(value).astype(np.float64) + key = np.array(key).astype(np.float64) + query = np.array(query).astype(np.float64) + + value = np.squeeze(value) + key = np.squeeze(key) + query = np.squeeze(query) + + val_mag = np.sqrt(np.dot(value, value)) + key_mag = np.sqrt(np.dot(key, key)) + query_mag = np.sqrt(np.dot(query, query)) + + matmul = key_mag * query_mag + theta = np.dot(value, query) / matmul + + # theta = np.arccos(theta) + + softmax = 1 / ((np.e) ** (-theta) + 1) + + out = softmax * value + + return out + + def compute_theshold(self, attention_weights): + """Computes the threshold for relevant and irrelevant text. + + :param attention_weights: The attention weights to be computed. + + :return: The threshold(int). + """ + threshold = 0 + for b in attention_weights: + if b != 0: + threshold += b + return threshold / len(attention_weights) + + def print_results(self, attention_weights, threshold, vectorized_text, show_only_important=False): + """Prints the results. + """ + for i in range(len(attention_weights)): + stat = "" + if attention_weights[i] > threshold: + stat = "Good" + elif attention_weights[i] == threshold: + stat = "Meh" + elif attention_weights[i] < threshold: + stat = "Not" + if show_only_important: + if attention_weights[i] >= threshold: + self.log.info(f"{attention_weights[i]:.2f}, {self.vectorize_layer.get_vocabulary()[vectorized_text[i]]}, {stat}") + else: + self.log.info( + f"{attention_weights[i]:.2f}, {self.vectorize_layer.get_vocabulary()[vectorized_text[i]]}, {stat}") + + def __call__(self, input_text): + """Computes everything and returns the importance matrix. + + :param input_text: The text to be computed. + + :return: The simplified importance matrix. + """ + s = self.split_positional_encodings(input_text) + vec = self.vectorize_text(input_text) + + out_fin = self.positional_embedding(s, vec) + + attention_weights = self.SelfAttention(out_fin, out_fin, out_fin, len(out_fin)) + threshold = self.compute_theshold(attention_weights) + + final_output_array = [] + + for i in range(len(attention_weights)): + stat = "" + if attention_weights[i] > threshold: + stat = "Good" + elif attention_weights[i] == threshold: + stat = "Meh" + elif attention_weights[i] < threshold: + stat = "Not" + + final_output_array.append([attention_weights[i], self.vectorize_layer.get_vocabulary()[vec[i]], stat]) + + return final_output_array diff --git a/build_conventional/src/MAGIST/NLP/WordScraper.py b/build_conventional/src/MAGIST/NLP/WordScraper.py new file mode 100644 index 0000000..11165ef --- /dev/null +++ b/build_conventional/src/MAGIST/NLP/WordScraper.py @@ -0,0 +1,107 @@ +import requests, json + +from ..Utils.LogMaster.log_init import MainLogger + + +class ClassDeprecated(Exception): + pass + + +class UrbanDictionary(): + def __init__(self, config): + """Initialize the Urban Dictionary API. + :param config: The config file(config.json). + """ + + raise ClassDeprecated("This class is deprecated") + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("UrbanDictionary") + + self.url = "https://mashape-community-urban-dictionary.p.rapidapi.com/define" + + def define(self, word): + """Define a word. + :param word: The word to be defined. + :return: The definition of the word. + """ + + querystring = {"term": word} + + headers = { + "X-RapidAPI-Key": "xxx", + "X-RapidAPI-Host": "mashape-community-urban-dictionary.p.rapidapi.com" + } + + response = requests.request("GET", self.url, headers=headers, params=querystring) + + json_data = json.loads(response.text) + + definition = json_data["list"][0]["definition"] + + self.log.info(f"Definition of {word}: " + definition) + + return definition + +class DicitonaryAPIDev(): + def __init__(self, config): + """Initialize the Dictionary API.. + + :param config: The config file(config.json). + """ + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("DisctionaryAPI") + + self.url = "https://api.dictionaryapi.dev/api/v2/entries/en/" + + def define(self, word): + """Define a word. + + :param word: The word to be defined. + + :return: The definition of the word. + """ + + querystring = {"term": word} + + self.url = self.url + word + + response = requests.request("GET", self.url, params=querystring) + + json_data = json.loads(response.text) + + definition = json_data + try: + definition = definition[0]["meanings"][0]["definitions"][0]["definition"] + except KeyError: + definition = "No definition found" + + self.log.info(f"Definition of {word}: " + definition) + + self.url = "https://api.dictionaryapi.dev/api/v2/entries/en/" + + return definition + + + +class FullDictionarySearch(): + def __init__(self, config): + """Initialize the Dictionary API.. + + :param config: The config file(config.json). + """ + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("FullDictionarySearch") + + self.dictdev = DicitonaryAPIDev(config) + + def define(self, word): + definition = self.dictdev.define(word) + + if definition == "No definition found": + self.log.info("No definition found in DictionaryAPI.dev.") + definition = None + + return definition \ No newline at end of file diff --git a/build_conventional/src/MAGIST/NLP/__init__.py b/build_conventional/src/MAGIST/NLP/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/NeuralDB/ElasticSearch.py b/build_conventional/src/MAGIST/NeuralDB/ElasticSearch.py new file mode 100644 index 0000000..3b2fc64 --- /dev/null +++ b/build_conventional/src/MAGIST/NeuralDB/ElasticSearch.py @@ -0,0 +1,248 @@ +import json +import requests +import pathlib + +from ..Utils.LogMaster.log_init import MainLogger + + +class ESDB(): + def __init__(self, config, es_uri, queries_file, schema_file, auto_check_server=True): + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("NeuralDB - ElasticSearchClient") # Create a script specific logging instance + + self.es_uri = es_uri + + schema_file = pathlib.Path(schema_file) + schema_file = schema_file.resolve() # Find absolute path from a relative one. + queries_file = pathlib.Path(queries_file) + queries_file = queries_file.resolve() # Find absolute path from a relative one. + + self.schema_file = f = open(schema_file, 'r') + self.schema_file_data = json.load(self.schema_file) + self.queries_file = f = open(queries_file, 'r') + self.queries_file_data = json.load(self.queries_file) + + self.log.debug(f"ElasticSearch Client initialized with {self.es_uri}. Config files: {self.schema_file} and {self.queries_file} parsed!") + + if auto_check_server: + self.__check_es_status() + + def __check_es_status(self): + es_status = requests.get(self.es_uri, timeout=10) + es_status = json.dumps(str(es_status)) + if "200" not in str(es_status): + raise RuntimeError(f"ElasticSearch Server is unreachable!") + else: + self.log.info(f"ElasticSearch Server is reachable!") + return True + + def create_index(self, index_name, schema_name): + available_schemas = ['object_db_schema', 'word_db_schema'] + success_status = "" + + try: + specific_schema = self.schema_file_data[schema_name] + except KeyError: + self.log.error(f"Schema not found from available schemas: {available_schemas}") + return + + # print(json.dumps(specific_schema, indent=2)) + + schema_uri = self.es_uri + "/" + index_name + + schema_stat = requests.put(schema_uri, json=specific_schema) + + schema_stat = json.dumps(str(schema_stat)) + + check_stat = requests.get(schema_uri + "/_settings") + check_stat = json.dumps(str(check_stat)) + + if "200" in str(schema_stat) and "200" in str(check_stat): + self.log.info(f"Index {index_name} with {schema_name} schema successfully created and verified!") + elif "200" in str(schema_stat) and "200" not in str(check_stat): + self.log.error( + f'Error creating index {index_name} with {schema_name} schema! Perhaps request was incorrectly formed or ' + f'ElasticSearch Server is unreachable.') + elif "200" not in str(schema_stat) and "200" in str(check_stat): + self.log.warning(f'Error creating index {index_name} with {schema_name} schema! The schema named {schema_name} likely ' + f'already exists.') + else: + self.log.error( + f'Error creating index {index_name} with {schema_name} schema! Perhaps request was incorrectly formed or ' + f'ElasticSearch Server is unreachable.') + + #////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + + def add_doc(es_uri, index_name, data_type, data, update="add"): + data_type_valid = ['object_db_schema', 'word_db_schema'] + if data_type not in data_type_valid: + raise ValueError(f"Data type {data_type} not found in available data types: {data_type_valid}") + + update_valid = ["add", "concatenate", "overwrite", "blind"] + if update not in update_valid: + raise ValueError(f"Data type {data_type} not found in available data types: {update_valid}") + + success_status = "" + + if data_type == 'object_db_schema': + index_check = requests.get(es_uri + "/" + index_name) + index_check = json.dumps(str(index_check)) + if "200" not in str(index_check): + raise RuntimeError(f"Index {index_name} not found!") + + try: + name = data['name'] + description = data['description'] + users = data['users'] + related_objects = data['related_objects'] + locations = data['locations'] + except KeyError: + raise RuntimeError("Improperly formatted data. Data MUST be in the following format: {name: str, " + "description: str, users: list, related_objects: list, locations: list}") + + queries_file = open('queries.json', 'r') + queries = json.load(queries_file) + + queries["object_exists"]["query"]["query_string"]["query"] = name + + object_exists = requests.post(es_uri + "/" + index_name + "/_search", json=queries["object_exists"]) + object_exists_simple = json.dumps(str(object_exists)) + object_exists_full = json.loads(str(object_exists.text)) + + print(object_exists_full["hits"]["total"]["value"]) + + if "200" in object_exists_simple and object_exists_full["hits"]["total"]["value"] > 0 and update != "add": + print(f"Object {name} already exists in index {index_name}!") + if object_exists_full["hits"]["total"]["value"] > 1: + raise RuntimeError("Search for existing objects failed and returned more than one result.") + + hit = object_exists_full["hits"]["hits"][0] + hit_id = hit["_id"] + hit_source = hit["_source"] + + print(type(hit_source["users"])) + + if update == "concatenate" or update == "blind": + hit_source["name"] = name + hit_source["description"] += description + hit_source["users"] += users + hit_source["related_objects"] += related_objects + hit_source["locations"] += locations + elif update == "overwrite": + hit_source["name"] = name + hit_source["description"] = description + hit_source["users"] = users + hit_source["related_objects"] = related_objects + hit_source["locations"] = locations + + hit_source = json.dumps(hit_source) + print(hit_source) + hit_source = """{"doc":""" + hit_source + "}" + print(hit_source) + hit_source = json.loads(hit_source) + print(hit_source) + + update_uri = es_uri + "/" + index_name + "/_update/" + hit_id + update_stat = requests.post(update_uri, json=hit_source) + print(update_stat.text) + + + elif "200" in object_exists_simple and object_exists_full["hits"]["total"][ + "value"] == 0 and update == "add": + print(f"Object {name} does not exist in index {index_name}! Proceeding to add object...") + + data_uri = es_uri + "/" + index_name + "/_doc" + data_stat = requests.post(data_uri, json=data) + data_stat = json.dumps(str(data_stat)) + + print(data_stat) + + if "201" in str(data_stat): + print(f"Object {name} successfully added to index {index_name}!") + else: + print(f"Error adding object {name} to index {index_name}!") + else: + print(f"Error checking if object {name} exists in index {index_name}!") + + elif data_type == 'word_db_schema': + index_check = requests.get(es_uri + "/" + index_name) + index_check = json.dumps(str(index_check)) + if "200" not in str(index_check): + raise RuntimeError(f"Index {index_name} not found!") + + try: + word = data['word'] + definition = data['definition'] + users = data['users'] + related_words = data['related_words'] + related_objects = data['related_objects'] + locations = data['locations'] + except KeyError: + raise RuntimeError("Improperly formatted data. Data MUST be in the following format: {name: str, " + "description: str, users: list, related_objects: list, locations: list}") + + queries_file = open('queries.json', 'r') + queries = json.load(queries_file) + + queries["word_exists"]["query"]["query_string"]["query"] = word + + word_exists = requests.post(es_uri + "/" + index_name + "/_search", json=queries["word_exists"]) + word_exists_simple = json.dumps(str(word_exists)) + word_exists_full = json.loads(str(word_exists.text)) + + print(word_exists_full["hits"]["total"]["value"]) + + if "200" in word_exists_simple and word_exists_full["hits"]["total"]["value"] > 0 and update != "add": + print(f"Object {word} already exists in index {index_name}!") + if word_exists_full["hits"]["total"]["value"] > 1: + raise RuntimeError("Search for existing objects failed and returned more than one result.") + + hit = word_exists_full["hits"]["hits"][0] + hit_id = hit["_id"] + hit_source = hit["_source"] + + print(type(hit_source["users"])) + + if update == "concatenate" or update == "blind": + hit_source["word"] += word + hit_source["description"] += definition + hit_source["users"] += users + hit_source["related_objects"] += related_objects + hit_source["related_words"] += related_words + hit_source["locations"] += locations + elif update == "overwrite": + hit_source["word"] = word + hit_source["description"] = definition + hit_source["users"] = users + hit_source["related_objects"] = related_objects + hit_source["related_words"] = related_words + hit_source["locations"] = locations + + hit_source = json.dumps(hit_source) + print(hit_source) + hit_source = """{"doc":""" + hit_source + "}" + print(hit_source) + hit_source = json.loads(hit_source) + print(hit_source) + + update_uri = es_uri + "/" + index_name + "/_update/" + hit_id + update_stat = requests.post(update_uri, json=hit_source) + print(update_stat.text) + + + elif "200" in word_exists_simple and word_exists_full["hits"]["total"]["value"] == 0 and update == "add": + print(f"Object {word} does not exist in index {index_name}! Proceeding to add object...") + + data_uri = es_uri + "/" + index_name + "/_doc" + data_stat = requests.post(data_uri, json=data) + data_stat = json.dumps(str(data_stat)) + + print(data_stat) + + if "201" in str(data_stat): + print(f"Object {word} successfully added to index {index_name}!") + else: + print(f"Error adding object {word} to index {index_name}!") + else: + print(f"Error checking if object {word} exists in index {index_name}!") diff --git a/build_conventional/src/MAGIST/NeuralDB/MongoUtils.py b/build_conventional/src/MAGIST/NeuralDB/MongoUtils.py new file mode 100644 index 0000000..bf4427d --- /dev/null +++ b/build_conventional/src/MAGIST/NeuralDB/MongoUtils.py @@ -0,0 +1,92 @@ +"""Mongo Utils file that manages and initializes the MongoDB connection. + +This file contains the main Admin functions and the rest is done using the PrimaryNeuralDB file. +""" + +import json +import os +import pathlib + +import pymongo as mongo + +from ..Utils.LogMaster.log_init import MainLogger + + +class AdminUtils(): + """Class that manages the MongoDB connection.""" + + def __init__(self, config): + """Initialize the AdminUtils class. + + :param config: The config file(string). + """ + root_log = MainLogger(config) + self.log = root_log.StandardLogger("MongoAdminUtils") # Create a script specific logging instance + + self.log.info("Firing up MongoDB Neural Database! Standby...") + + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['system_administration']: + try: + self.passcode = i["sudo_password"] + except KeyError: + pass + + for j in config['neural_db']: + try: + self.mgsocket = j["mongo_socket"] + except KeyError: + pass + + def initialize_neuraldb(self): + """Initialize the MongoDB connection. + + :return: The MongoDB client. + """ + + command = 'systemctl start mongod' + p = os.system('echo %s|sudo -S %s' % (self.passcode, command)) + self.log.info("NeuralDB Launched Successfully! Attempting to connect to local socket...") + + self.db_client = mongo.MongoClient(self.mgsocket) + + if self.db_client: + self.log.info("Mongo client linked successfully. Local DB Agent is running.") + else: + self.log.error("Mongo client failed to connect. The Mongo socket URL could be incorrect. It should look " + "something like this: mongodb://localhost:27017/") + + return self.db_client + + def stop_db(self): + """Stop the MongoDB connection. + """ + + self.log.info("Shutting down MongoDB Neural Database! Standby...") + command = 'systemctl stop mongod' + p = os.system('echo %s|sudo -S %s' % (self.passcode, command)) + self.log.info("NeuralDB Closed Successfully!") + + def restart_db(self): + """Restart the MongoDB connection. + """ + + self.log.info("Restarting MongoDB Neural Database! Standby...") + + command = 'systemctl restart mongod' + p = os.system('echo %s|sudo -S %s' % (self.passcode, command)) + self.log.info("NeuralDB Re-Launched Successfully! Attempting to connect to local socket...") + + self.db_client = mongo.MongoClient(self.mgsocket) + + if self.db_client: + self.log.info("Mongo client linked successfully. Local DB Agent is running.") + else: + self.log.error("Mongo client failed to connect. The Mongo socket URL could be incorrect. It should look " + "something like this: mongodb://localhost:27017/") + + return self.db_client diff --git a/build_conventional/src/MAGIST/NeuralDB/PrimaryNeuralDB.py b/build_conventional/src/MAGIST/NeuralDB/PrimaryNeuralDB.py new file mode 100644 index 0000000..42b8e78 --- /dev/null +++ b/build_conventional/src/MAGIST/NeuralDB/PrimaryNeuralDB.py @@ -0,0 +1,426 @@ +"""Main NeuralDB updating and querying class + +This class contains functions necessary to add information to the NeuralDB as well as query it for information upon +request. This requires the instantiated client from MongoUtils. +""" + +import json +import pathlib +import time +import re + +from ..Utils.LogMaster.log_init import MainLogger + + +class NeuralDB(): + """Main NeuralDB class""" + def __init__(self, config, db_client): + """Initialize NeuralDB class, parse config.json, and receive MongoDB client + + :param config: The config(config.json) file as a string. + :param db_client: The MongoDB client from MongoUtils. + """ + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("NeuralDB") # Create a script specific logging instance + self.client = db_client + + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['neural_db']: + try: + self.db_string = i["db_search_zone"] + except KeyError: + pass + + def recreate_db(self): + """Recreate the databases and collections + """ + + self.log.warning("NeuralDB is about to reset and recreate all Databases and tables. Proceeding in 5 seconds...") + + for i in range(5): + self.log.warning("{}...".format(5 - i)) + time.sleep(1) + self.log.warning("Resetting and recreating all databases and tables...") + + self.dbs = [] + self.collections = [] + + for d in self.db_string: + if d == "vision": + self.vision = self.client['VisionDB'] + + self.obj_desc = self.vision["ObjectDesc"] + self.obj_location = self.vision["ObjectLocation"] + self.obj_obj_relation = self.vision["ObjectObjectRelation"] + self.obj_users = self.vision["ObjectUsers"] + + self.log.info("Vision database is included in NeuralDB search.") + + self.dbs.append(self.vision) + + self.collections.append(self.obj_desc) + self.collections.append(self.obj_location) + self.collections.append(self.obj_obj_relation) + self.collections.append(self.obj_users) + if d == "nlp": + self.nlp = self.client["NLP"] + + self.word_desc = self.nlp["WordDesc"] + self.word_location = self.nlp["WordLocation"] + + self.log.info("NLP database is included in NeuralDB search.") + + self.dbs.append(self.nlp) + + self.collections.append(self.word_desc) + self.collections.append(self.word_location) + if d == "common": + self.common = self.client["Common"] + + self.word_obj_relation = self.common["WordObjectRelation"] + + self.log.info("Common database is included in NeuralDB search.") + + self.dbs.append(self.common) + + self.collections.append(self.word_obj_relation) + + try: + if self.vision is None: + self.log.warning("Vision database was not included from NeuralDB search.") + except AttributeError: + self.log.warning("Vision database was not included from NeuralDB search.") + + try: + if self.nlp is None: + self.log.warning("NLP database was not included from NeuralDB search.") + except AttributeError: + self.log.warning("NLP database was not included from NeuralDB search.") + + try: + if self.common is None: + self.log.warning("Common database was not included from NeuralDB search.") + except AttributeError: + self.log.warning("Common database was not included from NeuralDB search.") + + def insert_obj_desc(self, obj_name, obj_desc): + """Insert object and its description into the Vision database. + + :param obj_name: The name of the object(string). + :param obj_desc: The description of the object(string). + """ + + self.log.info(f"Inserting object description: {obj_name} - {obj_desc}") + self.obj_desc.insert_one({"obj_name": obj_name, "obj_desc": obj_desc}) + def insert_obj_location(self, obj_name, obj_location): + """Insert object and its location into the Vision database. + + :param obj_name: The name of the object(string). + :param obj_location: The location of the object(string). + """ + self.log.info(f"Inserting object location: {obj_name} - {obj_location}") + self.obj_location.insert_one({"obj_name": obj_name, "obj_location": obj_location}) + def insert_obj_obj_relation(self, obj_name, second_obj_name): + """Insert object and its relation to another object into the Vision database. + + :param obj_name: The name of the object(string). + :param second_obj_name: The name of the second object(string). + """ + + self.log.info(f"Inserting object object relation: {obj_name} - {second_obj_name}") + self.obj_obj_relation.insert_one({"obj_name": obj_name, "second_obj_name": second_obj_name}) + def insert_obj_users(self, obj_name, user_name): + """Insert object and its users into the Vision database. + + :param obj_name: The name of the object(string). + :param user_name: The name of the user(string). + """ + + self.log.info(f"Inserting object users: {obj_name} - {user_name}") + self.obj_users.insert_one({"obj_name": obj_name, "user_name": user_name}) + + def insert_word_desc(self, word_name, word_desc): + """Insert word and its description into the NLP database. + + :param word_name: The name of the word(string). + :param word_desc: The description of the word(string). + """ + if word_desc != None: + self.log.info(f"Inserting word description: {word_name} - {word_desc}") + self.word_desc.insert_one({"word_name": word_name, "word_desc": word_desc}) + def insert_word_location(self, word_name, word_location): + """Insert word and its location into the NLP database. + + :param word_name: The name of the word(string). + :param word_location: The location of the word(string). + """ + + self.log.info(f"Inserting word location: {word_name} - {word_location}") + self.word_location.insert_one({"word_name": word_name, "word_location": word_location}) + def insert_word_obj_relation(self, word_name, obj_name): + """Insert word and its relation to an object into the NLP database. + + :param word_name: The name of the word(string). + :param obj_name: The name of the object(string). + """ + + self.log.info(f"Inserting word object relation: {word_name} - {obj_name}") + self.word_obj_relation.insert_one({"word_name": word_name, "word_relation": obj_name}) + + def search_obj_details(self, obj_name): + """Search for object all details in the Vision database. + + :param obj_name: The name of the object(string). + + :return: A dictionary containing the object details. + """ + + data = [] + self.log.info(f"Searching object details: {obj_name}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"obj_name": re.compile(rf"\b{obj_name}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + def search_obj_desc(self, keyword): + """Search for object descriptions by keyword in the Vision database. + + :param keyword: The keyword to search for(string). + + :return: A dictionary containing the object descriptions. + """ + + data = [] + self.log.info(f"Searching object details by keyword: {keyword}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"obj_desc" : re.compile(rf"\b{keyword}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + def search_obj_location(self, location): + """Search for object locations by location in the Vision database. + + :param location: The location to search for(string). + + :return: A dictionary containing the object locations. + """ + + data = [] + self.log.info(f"Searching object details by location: {location}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"obj_location" : re.compile(rf"\b{location}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + def search_obj_user(self, user): + """Search for object users by user in the Vision database. + + :param user: The user to search for(string). + + :return: A dictionary containing the object users. + """ + + data = [] + self.log.info(f"Searching object details by user: {user}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"user_name" : re.compile(rf"\b{user}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + + + + + def search_word_details(self, word): + """Search for word details in the NLP database. + + :param word: The word to search for(string). + + :return: A dictionary containing the word details. + """ + + data = [] + self.log.info(f"Searching word details: {word}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"word_name" : re.compile(rf"\b{word}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + def search_word_desc(self, keyword): + """Search for word descriptions by keyword in the NLP database. + + :param keyword: The keyword to search for(string). + + :return: A dictionary containing the word descriptions. + """ + + data = [] + self.log.info(f"Searching word details by keyword: {keyword}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"word_desc" : re.compile(rf"\b{keyword}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + def search_word_location(self, location): + """Search for word locations by location in the NLP database. + + :param location: The location to search for(string). + + :return: A dictionary containing the word locations. + """ + + data = [] + self.log.info(f"Searching word details by location: {location}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"word_location" : re.compile(rf"\b{location}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + def search_word_relation(self, relation): + """Search for word relations by relation in the NLP database. + + :param relation: The relation to search for(string). + + :return: A dictionary containing the word relations. + """ + + data = [] + self.log.info(f"Searching word details by relation: {relation}") + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + + for i in d.list_collection_names(): + self.log.info(f" ===> Collection: {i}") + for j in self.vision[i].find({"word_relation" : re.compile(rf"\b{relation}\b", re.IGNORECASE)}): + self.log.info(f" ===> {j}") + data.append(j) + return data + + + def search_entire_db(self, term): + """Search for entire database by term in the NLP database. + + :param term: The term to search for(string). + + :return: A dictionary containing the entire database. + """ + + self._locals_search = locals() + + results = [] + + self.log.info(f"Searching entire database for: {term}") + + final_results = [] + + for d in self.dbs: + self.log.info(f"===> Database: {d.name}") + for c in d.list_collection_names(): + self.log.info(f" ===> Collection: {c}") + exec(f"db_col_search = self.client.{d.name}.{c}", self._locals_search) + db_col_search = self._locals_search['db_col_search'] + + cursor = d[c].find({}) + keys = list(cursor.next().keys()) + + for key in keys: + self.log.info(f" ===> Key: {c}") + search = db_col_search.find({key : re.compile(rf"\b{term}\b", re.IGNORECASE)}) + results.append(search) + try: + a = search.next() + self.log.info(f" ===> Found: {a}") + final_results.append(a) + except: + self.log.info(f" ===> Found: None") + + return final_results + + + + def remove_duplicates(self): + """Remove duplicates from the Vision database. + """ + + self._locals = locals() + print(self.vision.ObjectDesc) + for d in self.dbs: + for i in d.list_collection_names(): + exec(f"db_col = self.client.{d.name}.{i}", self._locals) + db_col = self._locals['db_col'] + + repeated_val = "" + + if "vision" in d.name.lower(): + repeated_val = "obj_name" + print("vision") + if "nlp" in d.name.lower(): + repeated_val = "word_name" + print("nlp") + + replic = db_col.aggregate([ # Cursor with all duplicated documents + {'$group': { + '_id': {repeated_val: f'${repeated_val}'}, # Duplicated field + 'uniqueIDs': {'$addToSet': '$_id'}, + 'total': {'$sum': 1} + } + }, + {'$match': { + 'total': {'$gt': 1} # Holds how many duplicates for each group, if you need it. + } + } + ]) + # Result is a list of lists of ObjectsIds + for i in replic: + for idx, j in enumerate(i['uniqueIDs']): # It holds the ids of all duplicates + if idx != 0: # Jump over first element to keep it + db_col.delete_one({'_id': j}) + diff --git a/build_conventional/src/MAGIST/NeuralDB/__init__.py b/build_conventional/src/MAGIST/NeuralDB/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/TaskManagment/ThreadedQueue.py b/build_conventional/src/MAGIST/TaskManagment/ThreadedQueue.py new file mode 100644 index 0000000..5d879b0 --- /dev/null +++ b/build_conventional/src/MAGIST/TaskManagment/ThreadedQueue.py @@ -0,0 +1,123 @@ +"""Threaded Queue contains all necessary functions for a priority queue. + +The queue class contains methods to add, execute, thread, and process tasks in a queue based on a priority system. +""" + +import queue +import threading +import uuid +import numpy as np +import pathlib +import json + +from ..Utils.LogMaster.log_init import MainLogger + + +class MainPriorityQueue(): + """Main Priority Queue Class.""" + + def __init__(self, config): + """Initialize the queue. + + :param config: The config file(config.json). + """ + + self.q = queue.PriorityQueue() + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("QueueController") # Create a script specific logging instance + + self.function_returns = [] + + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['task_management']: + try: + self.worker_threads = i["num_of_worker_threads"] + except KeyError: + pass + + def __worker(self): + """The worker thread. This actually executes the tasks in the queue. + """ + + while True: + items = self.q.get() + items = items[1] + func = items[0] + args = items[1:] + # print(f'Working on {args}') + + last_item = args[-1] # name + second_last_item = args[-2] # priority + third_last_item = args[-3] # guid + + args = args[:len(args) - 3] + + self.log.info(f'Received task: {last_item} with priority {second_last_item}. Unique ID assigned: {third_last_item}. Executing...') + [*returns] = [func(*args)] + self.log.info(f'Finished {last_item} successfully.') + self.q.task_done() + returns = np.array(returns) + self.function_returns.append([third_last_item, returns]) + # self.function_returns = np.squeeze(self.function_returns) + + def put_queue(self, function, *args, name="Unnamed", priority=None): + """Add a task to the queue. + + :param function: The function to be executed. NOTE: This must be in the form of Class.put_queue(function...), + not Class.put_queue(function()...). + :param args: The arguments to be passed to the function. This can be any number of args. + :param name: The name of the task. + :param priority: The priority of the task. + + :return: The unique ID of the task. + """ + + args = list(args) + + self.guid = uuid.uuid4() + + for i in args: + if i == name or i == priority: + raise ValueError( + "Name or priority cannot be used as argument. Please use priority= and name= in the function call.") + + self.q.put((priority, (function, *args, self.guid, priority, name))) + + return self.guid + + def detach_thread(self): + """Detach the thread from the main thread. + """ + + # Turn-on the worker thread. + for i in range(self.worker_threads): + threading.Thread(target=self.__worker, daemon=True).start() + self.log.info("Thread created and daemonized. Queue started...") + + def join_thread(self): + """Join the queue thread with main. + """ + + self.log.info("Attempting to join main thread...") + self.q.join() + self.log.info("Queue merge finished.") + + def search_results(self, query): + """Search the results for a specific task by ID. + + :param query: The unique ID of the task. NOTE: This must be a UUID object. + + :return: The results of the task. + """ + + r = self.function_returns + r = np.array(r) + r = r[r[:, 0] == query] + r = np.squeeze(r) + + return np.squeeze(r[1]) diff --git a/build_conventional/src/MAGIST/TaskManagment/__init__.py b/build_conventional/src/MAGIST/TaskManagment/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/Utils/LogMaster/__init__.py b/build_conventional/src/MAGIST/Utils/LogMaster/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/Utils/LogMaster/log_init.py b/build_conventional/src/MAGIST/Utils/LogMaster/log_init.py new file mode 100644 index 0000000..db4db52 --- /dev/null +++ b/build_conventional/src/MAGIST/Utils/LogMaster/log_init.py @@ -0,0 +1,94 @@ +"""Establish major logger functions for individual scripts. + +MainLogger is the main class containing 1 main function that provides a unique logging instance to each script. +""" + +import logging +import json +import os, pathlib + +class CustomFormatter(logging.Formatter): + + blue = '\033[34m' + green = '\033[92m' + grey = "\x1b[38;20m" + yellow = "\x1b[33;20m" + red = "\x1b[31;20m" + bold_red = "\x1b[31;1m" + reset = "\x1b[0m" + format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" + + FORMATS = { + logging.DEBUG: blue + format + reset, + logging.INFO: green + format + reset, + logging.WARNING: yellow + format + reset, + logging.ERROR: red + format + reset, + logging.CRITICAL: bold_red + format + reset + } + + def format(self, record): + log_fmt = self.FORMATS.get(record.levelno) + formatter = logging.Formatter(log_fmt) + return formatter.format(record) + +class MainLogger(): + # Logging Class + + def __init__(self, config): + """Initialize class and parse config + + :param config: A relative or absolute path to master config JSON file. + """ + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['paths']: + try: + self.log_dir = i["log_dir"] + except KeyError: + pass + for j in config['basic_variables']: + try: + self.verbose = j["verbose"] + except KeyError: + pass + + self.log_dir = pathlib.Path(self.log_dir) + self.log_dir = self.log_dir.resolve() # Find absolute path from a relative one. + self.log_dir = str(self.log_dir) + + def StandardLogger(self, name): + logger = logging.getLogger(name) + if not self.verbose: # Enable verbose depending on flag set by the config file. + logger.setLevel(logging.WARNING) + else: + logger.setLevel(logging.DEBUG) + # create file handler which logs even debug messages + try: + fh = logging.FileHandler(os.path.join(self.log_dir, 'complete.log')) + except FileNotFoundError: + os.makedirs(self.log_dir) + fh = logging.FileHandler(os.path.join(self.log_dir, 'complete.log')) + + # create console handler with a higher log level + error = logging.StreamHandler() + + error.setFormatter(CustomFormatter()) + + # create formatter and add it to the handlers + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + fh.setFormatter(formatter) + # error.setFormatter(formatter) + + # add the handlers to the logger + logger.addHandler(fh) + logger.addHandler(error) + + logger.info(f"{name}'s LogMaster Instance Initialized Successfully ===> {os.path.join(self.log_dir, 'complete.log')}") + + return logger + + + diff --git a/build_conventional/src/MAGIST/Utils/WebScraper/__init__.py b/build_conventional/src/MAGIST/Utils/WebScraper/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/Utils/WebScraper/google.py b/build_conventional/src/MAGIST/Utils/WebScraper/google.py new file mode 100644 index 0000000..763e705 --- /dev/null +++ b/build_conventional/src/MAGIST/Utils/WebScraper/google.py @@ -0,0 +1,195 @@ +"""Provides basic functions for Google Reverse Image Search and scraping. + +GoogleScraper is the main class containing 2 functions: reverse_image_search and download_raw_img_dataset. The function +reverse_image_search takes a given image path and uses a Google API as well as some scraping to find the name of the +object. The function download_raw_img_dataset takes a given keyword and downloads a given quantity of images from Google +images. +""" + +import requests +from bs4 import BeautifulSoup +from selenium import webdriver +from selenium.webdriver.firefox.options import Options +from google_images_search import GoogleImagesSearch +import os +import pathlib, json +from googleapiclient.errors import HttpError + +from ..LogMaster.log_init import MainLogger + + +class GoogleScraper: + """Main Google Images scraping and downloading tool.""" + + def __init__(self, config): + """Initializes the class and authenticates Google Search API with credentials and parses config file. It also + initializes the logger. + + :param config: A relative or absolute path to master config JSON file. + :param dev_api_key DEPRECATED: API key acquired from Google Search API webpage. + :param project_cx_id DEPRECATED: The Search Engine ID provided by Google per Google Developer Project. + + Note: The CX ID is hard to find. To find it, first go to: http://www.google.com/cse/manage/all. Select your + project and the ID will be called: "Search engine ID". Go to this StackOverflow question and PyPi Post for more + info: https://stackoverflow.com/questions/6562125/getting-a-cx-id-for-custom-search-google-api-python & + https://pypi.org/project/Google-Images-Search/ + """ + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("GoogleScraper") # Create a script specific logging instance + + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['api_authentication']: + try: + google_conf = i["google"] + for j in google_conf: + try: + self.dev_api_key = j["api_key"] + except KeyError: + pass + try: + self.project_cx_id = j["project_cx"] + except KeyError: + pass + try: + self.GIS_verbose = j["GIS_downloader_verbose"] + except KeyError: + pass + except KeyError: + pass + + def __my_progressbar(self, url, progress): + """Defines custom progressbar to visualize the download process for the image downloader. + + :param url: The URL from which the downloader is currently downloading the image from. + :param progress: The percentage of progress in downloading the file. + :return: None + """ + self.log.info(url + ' ' + str(progress) + '%') + + # t = tqdm(total=100, desc=url) + # t.update(progress) + + # try: + # if(progress == 1): + # t = tqdm(total=100, desc=url) + # else: + # t.update(progress) + # except: + # pass + + def reverse_image_search(self, image_path): + """Takes a given image path and finds the object name using Google Reverse Image Search and scraping. + + :param image_path: Relative or absolute image path. + :return: Object name(String) + """ + filePath = image_path + + filePath = pathlib.Path(filePath) + filePath = filePath.resolve() # Find the absolute path from relative one. + filePath = str(filePath) + + searchUrl = 'http://www.google.com/searchbyimage/upload' + headers = { + 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'} # Change header to ensure that Google Search still functions + multipart = {'encoded_image': (filePath, open(filePath, 'rb')), 'image_content': ''} + + response = requests.post(searchUrl, files=multipart, allow_redirects=False) + fetchUrl = response.headers['Location'] + + options = Options() + options.add_argument("--disable-extensions") + options.add_argument("--disable-gpu") + options.add_argument("--no-sandbox") # linux only + options.add_argument("--headless") + options.headless = True # also works + nav = webdriver.Firefox(options=options) + nav.get(fetchUrl) + self.log.info("Selenium reverse search complete.") + + try: + soup = BeautifulSoup(nav.page_source, 'html.parser') + link = soup.find_all("a", {"class": "fKDtNb"})[0] + link = str(link) + start = link.find(">") + len(">") + end = link.find(" Loading data...") + train, test = self.load_data() + self.log.info("Automated Trainer --> Data loaded successfully.") + self.log.info("Automated Trainer --> Building model...") + self.compile_model() + self.log.info("Automated Trainer --> Model built successfully.") + self.log.info("Automated Trainer --> Setting up callbacks...") + self.callbacks_init() + self.log.info("Automated Trainer --> Callbacks setup successfully.") + self.log.info("Automated Trainer --> Training model...") + self.train() + self.log.info("Automated Trainer --> Training completed successfully.") + + +class MAGIST_CNN_Predictor(): + def __init__(self, config): + """Initializes the predictor and config. + + :param config: A dictionary containing the config.json. + """ + root_log = MainLogger(config) + self.log = root_log.StandardLogger("MAGIST_Lite_Predictor") # Create a script specific logging instance + + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['tf_lite_detector']: + try: + self.TF_ckpt_path = i["TF_ckpt_path"] + except KeyError: + pass + try: + self.export_path = i["export_full_model"] + except KeyError: + pass + try: + self.input_image_size = i["input_image_size"] + except KeyError: + pass + try: + self.grayscale = i["grayscale"] + except KeyError: + pass + + self.export_path = pathlib.Path(self.export_path) + self.export_path = self.export_path.resolve() # Find absolute path from a relative one. + self.export_path = str(self.export_path) + + self.TF_ckpt_path = pathlib.Path(self.TF_ckpt_path) + self.TF_ckpt_path = self.TF_ckpt_path.resolve() # Find absolute path from a relative one. + self.TF_ckpt_path = str(self.TF_ckpt_path) + + self.imported = tf.keras.models.load_model(self.export_path, compile=False) + self.log.info("Model imported from {}.".format(self.export_path)) + + # latest = tf.train.latest_checkpoint(self.TF_ckpt_path) + + # self.imported.load_weights(latest) + self.imported.summary() + + def __load(self, filename): + """Loads a file from the given filename. + + :param filename: The filename to load. + + :return: The loaded image file as np.array. + """ + np_image = Image.open(filename) + np_image = np.array(np_image).astype('float32') / 255 + if self.grayscale: + np_image = transform.resize(np_image, (self.input_image_size[0], self.input_image_size[1], 1)) + else: + np_image = transform.resize(np_image, (self.input_image_size[0], self.input_image_size[1], 3)) + np_image = np.expand_dims(np_image, axis=0) + return np_image + + def img_prediction(self, img_path): + """Predicts the class of the given image. + + :param img_path: The path to the image. + + :return: The softmax array of predictions and the id of the prediction from class names. + """ + img_path = pathlib.Path(img_path) + img_path = img_path.resolve() # Find absolute path from a relative one. + img_path = str(img_path) + + image = self.__load(img_path) + p = self.imported.predict(image) + + p_id = np.array(p) + p_id = np.squeeze(p) + max = p_id.max() + id = np.where(p_id == max) + + return p, id + + def predict_from_batch_data(self, in_batch_ds): + """Predicts the class of the given batch of images. + + :param in_batch_ds: The batch of images. + + :return: The softmax array of predictions and the id of the prediction from class names. + """ + test_ds = in_batch_ds + + img, label = next(iter(test_ds)) + # print(len(img)) + self.log.info("Predicting on batch of {} images.".format(len(img))) + + ids = [] + for i in img: + i = np.array(i) + i = np.expand_dims(i, axis=0) + p = self.imported.predict(i) + p = np.array(p) + p = np.squeeze(p) + max = p.max() + id = np.where(p == max) + ids.append(id[0]) + return np.array(label), np.squeeze(np.array(ids)) diff --git a/build_conventional/src/MAGIST/Vision/FullySupervisedModels/__init__.py b/build_conventional/src/MAGIST/Vision/FullySupervisedModels/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/Vision/UnsupervisedModels/__init__.py b/build_conventional/src/MAGIST/Vision/UnsupervisedModels/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/Vision/UnsupervisedModels/img_cluster.py b/build_conventional/src/MAGIST/Vision/UnsupervisedModels/img_cluster.py new file mode 100644 index 0000000..863431b --- /dev/null +++ b/build_conventional/src/MAGIST/Vision/UnsupervisedModels/img_cluster.py @@ -0,0 +1,112 @@ +""" + +""" + +import pandas as pd +import matplotlib.pyplot as plt +from skimage.io import imread, imshow, imsave +from skimage.transform import resize +from sklearn.cluster import KMeans +from skimage.util import img_as_uint, img_as_ubyte +from ...Utils.LogMaster.log_init import MainLogger +import pathlib, json + + +class RoughCluster(): + def __init__(self, config): + """Initialize the class, logger module and parse config.json. + + :param config: A relative or absolute path to master config JSON file. + """ + root_log = MainLogger(config) + self.log = root_log.StandardLogger("UnsupervisedClustering") # Create a script specific logging instance + + config = pathlib.Path(config) + config = config.resolve() # Find absolute path from a relative one. + f = open(config) + config = json.load(f) + + for i in config['basic_variables']: + try: + self.matplot = i["enable_matplot_display"] + except: + pass + + def unsupervised_clusters(self, n_of_clusters, img_location, img_size, masked_img_dir): + """Make, color, and crop unsupervised clusters. + + :param n_of_clusters: Number of expected objects. + :param img_location: Location of input image. + :param img_size: Resized shape of the image in pixels. This is represented as a tuple (length, height). Note: + This is NOT the current size of the image(it can be though), but rather the size it will be scaled down to for + efficient processing. + :param masked_img_dir: Location of the exported image directories. + :return: None + """ + + def image_to_pandas(image): + """ + + :param image: Location of input image. + + :return: Array of masked image locations. + """ + df = pd.DataFrame([image[:, :, 0].flatten(), + image[:, :, 1].flatten(), + image[:, :, 2].flatten()]).T + df.columns = ['Red_Channel', 'Green_Channel', 'Blue_Channel'] + return df + + img_location = pathlib.Path(img_location) + img_location = img_location.resolve() # Find the absolute path from relative one. + img_location = str(img_location) + + masked_img_dir = pathlib.Path(masked_img_dir) + masked_img_dir = masked_img_dir.resolve() # Find the absolute path from relative one. + masked_img_dir = str(masked_img_dir) + + img = imread(img_location) + img = resize(img, img_size) + plt.figure(num=None, figsize=(8, 6), dpi=80) + if (self.matplot): + imshow(img) + + self.log.info("Input image resized and configured for clustering computation.") + + df_img = image_to_pandas(img) + + kmeans = KMeans(n_clusters=n_of_clusters, random_state=0).fit(df_img) + self.log.info("Image clustering complete!") + + result = kmeans.labels_.reshape(img.shape[0], img.shape[1]) + if (self.matplot): + imshow(result, cmap='viridis') + plt.show() + + fig, axes = plt.subplots(1, n_of_clusters, figsize=(15, 12)) + + clustered_img = [] + + for n, ax in enumerate(axes.flatten()): + img2 = imread(img_location) + img2 = resize(img2, img_size) + img2[:, :, 0] = img2[:, :, 0] * (result == [n]) # Disabling pixels of certain type + img2[:, :, 1] = img2[:, :, 1] * (result == [n]) # Disabling pixels of certain type + img2[:, :, 2] = img2[:, :, 2] * (result == [n]) # Disabling pixels of certain type + unit_img = img_as_ubyte(img2) + try: + imsave(f'{masked_img_dir}/masked{n}.jpg', unit_img) + except FileNotFoundError: + pathlib.Path(masked_img_dir).mkdir(parents=True, exist_ok=True) + imsave(f'{masked_img_dir}/masked{n}.jpg', unit_img) + clustered_img.append(f'{masked_img_dir}/masked{n}.jpg') + ax.imshow(img2) + ax.set_axis_off() + fig.tight_layout() + if (self.matplot): + plt.show() + + return clustered_img + +# unsupervised_clusters(3, 'test.jpg', (540, 480), "./Masks") +# unsupervised_clusters(2, 'masked2.jpg', (540, 480), ".") diff --git a/build_conventional/src/MAGIST/Vision/__init__.py b/build_conventional/src/MAGIST/Vision/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/build_conventional/src/MAGIST/__init__.py b/build_conventional/src/MAGIST/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/environments/environment.yaml b/environments/environment.yaml new file mode 100644 index 0000000..9daf1f8 --- /dev/null +++ b/environments/environment.yaml @@ -0,0 +1,8 @@ +name: MAGIST-Algorithm + +channels: + - default + - conda-forge + +dependencies: + - python=3.10.4 \ No newline at end of file diff --git a/requirements/System Packages b/environments/requirements/System Packages similarity index 100% rename from requirements/System Packages rename to environments/requirements/System Packages diff --git a/mkdocs.yml b/mkdocs.yml deleted file mode 100644 index ecdb8ac..0000000 --- a/mkdocs.yml +++ /dev/null @@ -1,82 +0,0 @@ -INHERIT: site_settings.yml - - -################### NO CHANGES BEYOND THIS POINT ################ -# MkDocs Wiki: https://github.com/mkdocs/mkdocs/wiki -docs_dir: "docs" # Default is "docs" -site_dir: "site" # Default is "site" -use_directory_urls: true # Default is true. - - - -theme: - #Material Theme used here: https://github.com/squidfunk/mkdocs-material - # Material Theme Detailed Reference: https://squidfunk.github.io/mkdocs-material/reference/abbreviations/ - #Other Themes: https://github.com/mkdocs/mkdocs/wiki/MkDocs-Themes - name: material - language: en -# logo: assets/favicon.png - favicon: assets/favicon.png - palette: - - scheme: default - primary: blue - toggle: - icon: material/toggle-switch - name: Switch to dark mode - - scheme: slate - primary: blue - toggle: - icon: material/toggle-switch-off-outline - name: Switch to light mode - features: - - navigation.instant - - search.suggest - - search.highlight -# - navigation.tabs -# - navigation.sections - - navigation.top - - content.code.annotate -# - navigation.expand # This will expand navigation on left - - content.tooltips - - navigation.instant - - navigation.tracking - - -plugins: - - search: - lang: en - prebuild_index: true - indexing: full #Default is full. 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diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..72a4c2e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,32 @@ +[tool.poetry] +name = "MAGIST-Algorithm" +version = "0.1.0" +description = "" +authors = ["DragonflyRobotics "] +license = "GNU GPL v3.0" + +[tool.poetry.dependencies] +python = ">=3.10,<3.11" +beautifulsoup4 = "^4.11.1" +Google-Images-Search = "^1.4.3" +matplotlib = "^3.5.2" +numpy = "^1.23.1" +pandas = "^1.4.3" +Pillow-PIL = "^0.1dev" +pymongo = "^4.2.0" +requests = "^2.31.0" +urllib3 = "^1.26.11" +scikit-image = "^0.19.3" +scikit-learn = "^1.1.2" +selenium = "^4.4.0" +SpeechRecognition-ForkedVersion = "^3.9.2" +tensorflow = "^2.11.1" +tqdm = "^4.64.0" +wikipedia = "^1.4.0" +opencv-python = "^4.6.0" + +[tool.poetry.dev-dependencies] + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..e69de29 diff --git a/requirements/requirements.txt b/requirements/requirements.txt deleted file mode 100644 index 0950b7d..0000000 --- a/requirements/requirements.txt +++ /dev/null @@ -1,16 +0,0 @@ -beautifulsoup4==4.11.1 -google_api_python_client==2.51.0 -Google_Images_Search==1.4.2 -matplotlib==3.5.1 -numpy==1.22.3 -pandas==1.4.1 -Pillow==9.1.1 -pymongo==3.12.3 -requests==2.24.0 -scikit_image==0.19.2 -scikit_learn==1.1.1 -selenium==4.2.0 -skimage==0.0 -SpeechRecognition==3.8.1 -tensorflow==2.9.1 -tqdm==4.63.0 diff --git a/site_settings.yml b/site_settings.yml deleted file mode 100644 index 5a55771..0000000 --- a/site_settings.yml +++ /dev/null @@ -1,46 +0,0 @@ -site_name: MAGIST Algorithm Documentation - DeepShift Labs -repo_url: https://github.com/DeepShift-Labs/MAGIST-Algorithm -edit_uri: "edit/main/docs" # No need to make changes here if you are using github -site_description: "MAGIST Algorithm Documentation - DeepShift Labs" -site_author: Krishna Shah -dev_addr: 127.0.0.1:8011 - -nav: - - "Read Me": README.md - - "Getting Started": - - "Installation": "Getting Started/1 - Initial Steps.md" - - "Process Overview": "Getting Started/2 - Process Overview.md" - - "Next Steps": "Getting Started/3 - Next Steps.md" - - "Configuration": - - "Setup Config File": "Configuration/1 - Setup Config File.md" - - "MAGIST Vision": - - "Basics": "MAGIST Vision/1 - Basics.md" - - "K-Means Clustering": "MAGIST Vision/2 - K-Means Clustering.md" - - "Detection Data Management": "MAGIST Vision/3 - Detection Data Management.md" -# - "SOLID Principles": "General/SOLID Principles.md" -# - Markdown: -# - Markdown Syntax: Markdown/Markdown Syntax.md -# - MkDocks Material Syntax: Markdown/MkDocs Material Syntax.md -# - Git: -# - Concepts: Git/Concepts.md -# - "Reset, Revert & Rebase": Git/Reset-Revert-Rebase.md -# - Troubleshoot: Git/Troubleshoot.md -# - Java: -# - Concepts: General/Java/General.md -# - Windows: -# - General: General/Windows/General.md -# - Files and Folders: General/Windows/Files and Folders.md -# - Enterprise Architect: -# - The Disciplined Agile Framework : Enterprise Architect/DA Framework.md -# - Event Sourcing: Enterprise Architect/Event Sourcing.md -# - Project Management: -# - Scrum: Project Management/Scrum.md -# - ELK Stack: -# - ElasticSearch: -# - Basics: ElasticSearch-LogStash-Kibana/EalsticSearch/Basic.md -# - Installation: ElasticSearch-LogStash-Kibana/EalsticSearch/Installation.md -# - Logging: ElasticSearch-LogStash-Kibana/EalsticSearch/Logging.md -# - Querying: -# - Prep Work: ElasticSearch-LogStash-Kibana/EalsticSearch/Querying/Prep Work.md -# - Full-Text Queries: ElasticSearch-LogStash-Kibana/EalsticSearch/Querying/Full-Text Queries.md -# - Term Queries: ElasticSearch-LogStash-Kibana/EalsticSearch/Querying/Term Queries.md diff --git a/src/MAGIST/MAGIST_Rust/Cargo.lock b/src/MAGIST/MAGIST_Rust/Cargo.lock new file mode 100644 index 0000000..160d6c2 --- /dev/null +++ b/src/MAGIST/MAGIST_Rust/Cargo.lock @@ -0,0 +1,7 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 3 + +[[package]] +name = "MAGIST-Rust" +version = "0.1.0" diff --git a/src/MAGIST/MAGIST_Rust/Cargo.toml b/src/MAGIST/MAGIST_Rust/Cargo.toml new file mode 100644 index 0000000..696b4b1 --- /dev/null +++ b/src/MAGIST/MAGIST_Rust/Cargo.toml @@ -0,0 +1,8 @@ +[package] +name = "MAGIST_Rust" +version = "0.1.0" +edition = "2021" + +# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html + +[dependencies] diff --git a/src/MAGIST/MAGIST_Rust/src/lib.rs b/src/MAGIST/MAGIST_Rust/src/lib.rs new file mode 100644 index 0000000..7d12d9a --- /dev/null +++ b/src/MAGIST/MAGIST_Rust/src/lib.rs @@ -0,0 +1,14 @@ +pub fn add(left: usize, right: usize) -> usize { + left + right +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn it_works() { + let result = add(2, 2); + assert_eq!(result, 4); + } +} diff --git a/src/MAGIST/NLP/RNN_Fast_Response.py b/src/MAGIST/NLP/RNN_Fast_Response.py new file mode 100644 index 0000000..46e6795 --- /dev/null +++ b/src/MAGIST/NLP/RNN_Fast_Response.py @@ -0,0 +1,12 @@ +from MAGIST.NeuralDB.MongoUtils import AdminUtils +from MAGIST.NeuralDB.PrimaryNeuralDB import NeuralDB + + +mongo_admin = AdminUtils("config/config.json") +client = mongo_admin.initialize_neuraldb() +neural_db = NeuralDB("config/config.json", client) + +neural_db.recreate_db() + + +neural_db.search_entire_db("") \ No newline at end of file diff --git a/src/MAGIST/NeuralDB/ElasticSearch.py b/src/MAGIST/NeuralDB/ElasticSearch.py new file mode 100644 index 0000000..287b714 --- /dev/null +++ b/src/MAGIST/NeuralDB/ElasticSearch.py @@ -0,0 +1,332 @@ +import json +import requests +import pathlib + +from ..Utils.LogMaster.log_init import MainLogger + + +class ESDB(): + def __init__(self, config, es_uri, queries_file, schema_file, auto_check_server=True): + + root_log = MainLogger(config) + self.log = root_log.StandardLogger("NeuralDB - ElasticSearchClient") # Create a script specific logging instance + + self.es_uri = es_uri + + schema_file = pathlib.Path(schema_file) + schema_file = schema_file.resolve() # Find absolute path from a relative one. + queries_file = pathlib.Path(queries_file) + queries_file = queries_file.resolve() # Find absolute path from a relative one. + + + self.schema_file = f = open(schema_file, 'r') + self.schema_file_data = json.load(self.schema_file) + self.queries_file = f = open(queries_file, 'r') + self.queries_file_data = json.load(self.queries_file) + + self.log.debug(f"ElasticSearch Client initialized with {self.es_uri}. Config files: {self.schema_file} and {self.queries_file} parsed!") + + if auto_check_server: + self.__check_es_status() + + def __check_es_status(self): + es_status = requests.get(self.es_uri, timeout=10) + es_status = json.dumps(str(es_status)) + if "200" not in str(es_status): + raise RuntimeError(f"ElasticSearch Server is unreachable!") + else: + self.log.info(f"ElasticSearch Server is reachable!") + return True + + def create_index(self, index_name, schema_name): + available_schemas = ['object_db_schema', 'word_db_schema'] + success_status = "" + + try: + specific_schema = self.schema_file_data[schema_name] + except KeyError: + self.log.error(f"Schema not found from available schemas: {available_schemas}") + return + + # print(json.dumps(specific_schema, indent=2)) + + schema_uri = self.es_uri + "/" + index_name + + schema_stat = requests.put(schema_uri, json=specific_schema) + + schema_stat = json.dumps(str(schema_stat)) + + check_stat = requests.get(schema_uri + "/_settings") + check_stat = json.dumps(str(check_stat)) + + if "200" in str(schema_stat) and "200" in str(check_stat): + self.log.info(f"Index {index_name} with {schema_name} schema successfully created and verified!") + elif "200" in str(schema_stat) and "200" not in str(check_stat): + self.log.error( + f'Error creating index {index_name} with {schema_name} schema! Perhaps request was incorrectly formed or ' + f'ElasticSearch Server is unreachable.') + elif "200" not in str(schema_stat) and "200" in str(check_stat): + self.log.warning(f'Error creating index {index_name} with {schema_name} schema! The schema named {schema_name} likely ' + f'already exists.') + else: + self.log.error( + f'Error creating index {index_name} with {schema_name} schema! Perhaps request was incorrectly formed or ' + f'ElasticSearch Server is unreachable.') + + #////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// + + def add_doc(self, index_name, data_type, data, update="add"): + es_uri = self.es_uri + data_type_valid = ['object_db_schema', 'word_db_schema'] + if data_type not in data_type_valid: + raise ValueError(f"Data type {data_type} not found in available data types: {data_type_valid}") + + update_valid = ["add", "concatenate", "overwrite", "blind"] + if update not in update_valid: + raise ValueError(f"Data type {data_type} not found in available data types: {update_valid}") + + success_status = "" + + if data_type == 'object_db_schema': + index_check = requests.get(es_uri + "/" + index_name) + index_check = json.dumps(str(index_check)) + if "200" not in str(index_check): + raise RuntimeError(f"Index {index_name} not found!") + + try: + name = data['name'] + description = data['description'] + users = data['users'] + related_objects = data['related_objects'] + locations = data['locations'] + except KeyError: + raise RuntimeError("Improperly formatted data. Data MUST be in the following format: {name: str, " + "description: str, users: list, related_objects: list, locations: list}") + + # queries_file = open('queries.json', 'r') + queries = self.queries_file_data + + queries["object_exists"]["query"]["query_string"]["query"] = name + + object_exists = requests.post(es_uri + "/" + index_name + "/_search", json=queries["object_exists"]) + object_exists_simple = json.dumps(str(object_exists)) + object_exists_full = json.loads(str(object_exists.text)) + + print(object_exists_full["hits"]["total"]["value"]) + + if "200" in object_exists_simple and object_exists_full["hits"]["total"]["value"] > 0 and update != "add": + print(f"Object {name} already exists in index {index_name}!") + if object_exists_full["hits"]["total"]["value"] > 1: + raise RuntimeError("Search for existing objects failed and returned more than one result.") + + hit = object_exists_full["hits"]["hits"][0] + hit_id = hit["_id"] + hit_source = hit["_source"] + + print(type(hit_source["users"])) + + if update == "concatenate" or update == "blind": + hit_source["name"] = name + hit_source["description"] += description + hit_source["users"] += users + hit_source["related_objects"] += related_objects + hit_source["locations"] += locations + elif update == "overwrite": + hit_source["name"] = name + hit_source["description"] = description + hit_source["users"] = users + hit_source["related_objects"] = related_objects + hit_source["locations"] = locations + + hit_source = json.dumps(hit_source) + print(hit_source) + hit_source = """{"doc":""" + hit_source + "}" + print(hit_source) + hit_source = json.loads(hit_source) + print(hit_source) + + update_uri = es_uri + "/" + index_name + "/_update/" + hit_id + update_stat = requests.post(update_uri, json=hit_source) + print(update_stat.text) + + + elif "200" in object_exists_simple and object_exists_full["hits"]["total"][ + "value"] == 0 and update == "add": + print(f"Object {name} does not exist in index {index_name}! Proceeding to add object...") + + data_uri = es_uri + "/" + index_name + "/_doc" + data_stat = requests.post(data_uri, json=data) + data_stat = json.dumps(str(data_stat)) + + print(data_stat) + + if "201" in str(data_stat): + print(f"Object {name} successfully added to index {index_name}!") + else: + print(f"Error adding object {name} to index {index_name}!") + else: + print(f"Error checking if object {name} exists in index {index_name}!") + + elif data_type == 'word_db_schema': + index_check = requests.get(es_uri + "/" + index_name) + index_check = json.dumps(str(index_check)) + if "200" not in str(index_check): + raise RuntimeError(f"Index {index_name} not found!") + + try: + word = data['word'] + definition = data['definition'] + users = data['users'] + related_words = data['related_words'] + related_objects = data['related_objects'] + locations = data['locations'] + except KeyError: + raise RuntimeError("Improperly formatted data. Data MUST be in the following format: {name: str, " + "description: str, users: list, related_objects: list, locations: list}") + + # queries_file = open('queries.json', 'r') + queries = self.queries_file_data + + queries["word_exists"]["query"]["query_string"]["query"] = word + + word_exists = requests.post(es_uri + "/" + index_name + "/_search", json=queries["word_exists"]) + word_exists_simple = json.dumps(str(word_exists)) + word_exists_full = json.loads(str(word_exists.text)) + + print(word_exists_full["hits"]["total"]["value"]) + + if "200" in word_exists_simple and word_exists_full["hits"]["total"]["value"] > 0 and update != "add": + print(f"Object {word} already exists in index {index_name}!") + if word_exists_full["hits"]["total"]["value"] > 1: + raise RuntimeError("Search for existing objects failed and returned more than one result.") + + hit = word_exists_full["hits"]["hits"][0] + hit_id = hit["_id"] + hit_source = hit["_source"] + + print(type(hit_source["users"])) + + if update == "concatenate" or update == "blind": + hit_source["word"] += word + hit_source["description"] += definition + hit_source["users"] += users + hit_source["related_objects"] += related_objects + hit_source["related_words"] += related_words + hit_source["locations"] += locations + elif update == "overwrite": + hit_source["word"] = word + hit_source["description"] = definition + hit_source["users"] = users + hit_source["related_objects"] = related_objects + hit_source["related_words"] = related_words + hit_source["locations"] = locations + + hit_source = json.dumps(hit_source) + print(hit_source) + hit_source = """{"doc":""" + hit_source + "}" + print(hit_source) + hit_source = json.loads(hit_source) + print(hit_source) + + update_uri = es_uri + "/" + index_name + "/_update/" + hit_id + update_stat = requests.post(update_uri, json=hit_source) + print(update_stat.text) + + + elif "200" in word_exists_simple and word_exists_full["hits"]["total"]["value"] == 0 and update == "add": + print(f"Object {word} does not exist in index {index_name}! Proceeding to add object...") + + data_uri = es_uri + "/" + index_name + "/_doc" + data_stat = requests.post(data_uri, json=data) + data_stat = json.dumps(str(data_stat)) + + print(data_stat) + + if "201" in str(data_stat): + print(f"Object {word} successfully added to index {index_name}!") + else: + print(f"Error adding object {word} to index {index_name}!") + else: + print(f"Error checking if object {word} exists in index {index_name}!") + + def format_object_data(self, name, description="", locations=[], related_objects=[], users=[]): + if type(name) is not str: + raise ValueError("The 'name' parameter MUST be of type String.") + if type(description) is not str: + raise ValueError("The 'description' parameter MUST be of type String.") + if type(locations) is not list: + raise ValueError("The 'locations' parameter MUST be of type List.") + if type(related_objects) is not list: + raise ValueError("The 'related_objects' parameter MUST be of type List.") + if type(users) is not list: + raise ValueError("The 'users' parameter MUST be of type List.") + + + + data = { + "name": name, + "description": description, + "locations": locations, + "related_objects": related_objects, + "users": users + } + + return data + + def format_word_data(self, word, definition="", locations=[], related_objects=[], users=[], related_words=[]): + if type(word) is not str: + raise ValueError("The 'word' parameter MUST be of type String.") + if type(definition) is not str: + raise ValueError("The 'definition' parameter MUST be of type String.") + if type(locations) is not list: + raise ValueError("The 'locations' parameter MUST be of type List.") + if type(related_objects) is not list: + raise ValueError("The 'related_objects' parameter MUST be of type List.") + if type(users) is not list: + raise ValueError("The 'users' parameter MUST be of type List.") + if type(related_words) is not list: + raise ValueError("The 'related_words' parameter MUST be of type List.") + + data = { + "word": word, + "definition": definition, + "locations": locations, + "related_objects": related_objects, + "related_words": related_words, + "users": users + } + + return data + + def search(self, index_name, index_type, keyword): + data_type_valid = ['object_db_schema', 'word_db_schema'] + if index_type not in data_type_valid: + raise ValueError(f"Data type {index_type} not found in available data types: {data_type_valid}") + + queries = self.queries_file_data + + self.log.info(f"Searching for '{keyword}' in '{index_name}' index (of type '{index_type}')...") + + if index_type == "object_db_schema": + queries["object_full"]["query"]["multi_match"]["query"] = keyword + + object_full = requests.post(self.es_uri + "/" + index_name + "/_search", json=queries["object_full"]) + object_full = json.loads(str(object_full.text)) + return object_full + elif index_type == "word_db_schema": + queries["word_full"]["query"]["multi_match"]["query"] = keyword + + word_full = requests.post(self.es_uri + "/" + index_name + "/_search", json=queries["word_full"]) + word_full = json.loads(str(word_full.text)) + return word_full + + def delete_all_indices(self): + + indices_resp = requests.get(self.es_uri + "/_cat/indices/?format=json") + json_indicies = json.loads(indices_resp.text) + indicies = [] + for j in json_indicies: + indicies.append(j["index"]) + + for i in indicies: + del_resp = requests.delete(self.es_uri + "/" + str(i)) diff --git a/src/MAGIST/Utils/LogMaster/log_init.py b/src/MAGIST/Utils/LogMaster/log_init.py index 051eb3f..db4db52 100644 --- a/src/MAGIST/Utils/LogMaster/log_init.py +++ b/src/MAGIST/Utils/LogMaster/log_init.py @@ -7,6 +7,30 @@ import json import os, pathlib +class CustomFormatter(logging.Formatter): + + blue = '\033[34m' + green = '\033[92m' + grey = "\x1b[38;20m" + yellow = "\x1b[33;20m" + red = "\x1b[31;20m" + bold_red = "\x1b[31;1m" + reset = "\x1b[0m" + format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" + + FORMATS = { + logging.DEBUG: blue + format + reset, + logging.INFO: green + format + reset, + logging.WARNING: yellow + format + reset, + logging.ERROR: red + format + reset, + logging.CRITICAL: bold_red + format + reset + } + + def format(self, record): + log_fmt = self.FORMATS.get(record.levelno) + formatter = logging.Formatter(log_fmt) + return formatter.format(record) + class MainLogger(): # Logging Class @@ -51,10 +75,13 @@ def StandardLogger(self, name): # create console handler with a higher log level error = logging.StreamHandler() + error.setFormatter(CustomFormatter()) + # create formatter and add it to the handlers formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') fh.setFormatter(formatter) - error.setFormatter(formatter) + # error.setFormatter(formatter) + # add the handlers to the logger logger.addHandler(fh) logger.addHandler(error) diff --git a/src/config/config.json b/src/config/config.json new file mode 100644 index 0000000..f61a2cd --- /dev/null +++ b/src/config/config.json @@ -0,0 +1,39 @@ +{ + "api_authentication": [ + {"google": [ + {"api_key": "AIzaSyBJchpTJ0-Xq2d8dF-x_HLqcVL8x5l2DlI"}, + {"project_cx": "47ab711e86cb1ca1b"}, + {"GIS_downloader_verbose": 0} + ]} + ], + "system_administration": [ + {"sudo_password": "Hanuman@77"} + ], + "task_management": [ + {"num_of_worker_threads": 4} + ], + "paths": [ + {"log_dir": "Logs"} + ], + "basic_variables": [ + {"verbose": 1}, + {"enable_matplot_display": 1} + ], + "tf_lite_detector": [ + {"data_path": "Data"}, + {"TensorBoard_log_dir": "Tensorflow/TensorBoard"}, + {"TF_ckpt_path": "Tensorflow/tf_ckpt"}, + {"input_image_size": [50, 50]}, + {"grayscale": 0}, + {"epochs": 10}, + {"batch_size": 32}, + {"seed": 42}, + {"validation_split": 0.2}, + {"export_full_model": "Tensorflow/Full_TF_Model"} + ], + "neural_db": [ + {"elastic_socket": "http://localhost:9200"}, + {"mongo_socket": "mongodb://localhost:27017/"}, + {"db_search_zone": ["vision", "nlp", "common"]} + ] +} \ No newline at end of file diff --git a/src/config/queries.json b/src/config/queries.json new file mode 100644 index 0000000..80f1086 --- /dev/null +++ b/src/config/queries.json @@ -0,0 +1,36 @@ +{ + "object_exists": { + "query": { + "query_string": { + "query" : "", + "fields": ["name"], + "analyzer": "keyword" + } + } + }, + "object_full": { + "query": { + "multi_match": { + "query" : "", + "analyzer": "main_analyzer" + } + } + }, + "word_exists": { + "query": { + "query_string": { + "query" : "", + "fields": ["name"], + "analyzer": "keyword" + } + } + }, + "word_full": { + "query": { + "multi_match": { + "query" : "", + "analyzer": "main_analyzer" + } + } + } +} \ No newline at end of file diff --git a/src/config/schema.json b/src/config/schema.json new file mode 100644 index 0000000..d614e01 --- /dev/null +++ b/src/config/schema.json @@ -0,0 +1,61 @@ +{ + "object_db_schema": { + "settings": { + "analysis": { + "analyzer": { + "main_analyzer": { + "type": "fingerprint", + "stopwords": "_english_" + } + } + }, + "similarity": { + "main_similarity": { + "type": "LMJelinekMercer", + "lambda": 0.5 + } + } + }, + + "mappings": { + "properties": { + "name": { "type": "keyword" }, + "description": { "type": "text" }, + "users": { "type": "text" }, + "related_objects": { "type": "text" }, + "location": { "type": "text" } + } + } + }, + "word_db_schema": { + "settings": { + "analysis": { + "analyzer": { + "main_analyzer": { + "type": "fingerprint", + "stopwords": "_english_" + } + } + }, + "similarity": { + "main_similarity": { + "type": "LMJelinekMercer", + "lambda": 0.5 + } + } + }, + + "mappings": { + "properties": { + "word": { "type": "text" }, + "definition": { "type": "text" }, + "users": { "type": "text" }, + "related_words": { "type": "text" }, + "related_objects": { "type": "text" }, + "location": { "type": "text" } + } + } + } +} + + diff --git a/src/config/schema_nested.json b/src/config/schema_nested.json new file mode 100644 index 0000000..303cb71 --- /dev/null +++ b/src/config/schema_nested.json @@ -0,0 +1,117 @@ +{ + "object_db_schema": { + "mappings": { + "properties": { + "Object": { + "type": "nested", + "properties": { + "name": { + "type": "nested", + "properties": { + "description": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "users": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "related_objects": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "location": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + } + } + } + } + } + } + } + }, + "word_db_schema": { + "mappings": { + "properties": { + "Word": { + "type": "nested", + "properties": { + "name": { + "type": "nested", + "properties": { + "definition": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "users": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "related_words": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "related_objects": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "location": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + } + } + } + } + } + } + } + } +} + + diff --git a/src/config/test.py b/src/config/test.py new file mode 100644 index 0000000..5c7fc2b --- /dev/null +++ b/src/config/test.py @@ -0,0 +1,57 @@ +import json + + +def tester(path, key): + f = open(path) + + config = json.load(f) + + item_checker = False + + for item in config: + if item == key: + print(item) + item_checker = True + for individual_value in config[item]: #Don't do this, you need to key the item itself. + if individual_value == key: + print(individual_value) + item_checker = True + for sub_key in individual_value: + x = individual_value[sub_key] + + if type(x) == list: + + my_list = [] + + for sub_val in individual_value[sub_key]: + if type(sub_val) == dict: + for x in sub_val: + if x == key: + print(sub_val[x]) + item_checker = True + else: + if sub_key == key: + my_list.append(sub_val) + + if len(my_list) > 0: + print(my_list[::1]) + item_checker = True + else: + pass + else: + if sub_key == key: + print(x) + item_checker = True + + return item_checker + + +dict_key = "project_cx" + +try: + if tester("config.json", dict_key): + pass + else: + print("Sorry, key not found :(") +except FileNotFoundError: + print("Error! Wrong file path / file not found :(") diff --git a/src/main.py b/src/main.py index 588d309..4296ace 100644 --- a/src/main.py +++ b/src/main.py @@ -1,119 +1,115 @@ from MAGIST.Vision.UnsupervisedModels.img_cluster import RoughCluster -import numpy as np - -cluster = RoughCluster("config.json") - -imgs = cluster.unsupervised_clusters(3, "2.jpg", (200, 200), "Clusters") - - from MAGIST.Utils.WebScraper.google import GoogleScraper - -scraper = GoogleScraper("config.json") - -labels = [] - -for i in imgs: - label = scraper.reverse_image_search(i) - labels.append(label) - -labels = np.unique(np.array(labels)) - from MAGIST.TaskManagment.ThreadedQueue import MainPriorityQueue - -queue = MainPriorityQueue("config.json") -queue.detach_thread() - -priority = 1 - - -for l in labels: - queue.put_queue(scraper.download_raw_img_dataset, l, 10, "Data/", name=f"Downloading {l}", priority=priority) - priority += 1 - - -from MAGIST.Vision.DetectionDataManager.image_slicer import ImageSlicer - -slicer = ImageSlicer("config.json") - - - -for l in labels: - path = os.path.join("Data", l) - slicer.image_integrity_verification(path, delete_invalid=True) - slicer.resizer((500, 500), path) - coordinates = slicer.coordinate_compute((500, 500), (100, 100)) - slicer.crop_segments(coordinates, path, "Sliced", l) - - - - -from MAGIST.Vision.FullySupervisedModels.MAGIST_Lite_Detector import MAGIST_CNN - -cnn = MAGIST_CNN("config.json") - -queue.put_queue(cnn, name="MAGIST_CNN_Trainer", priority=10) - from MAGIST.Utils.WebScraper.wikipedia import WikipediaScraper -from MAGIST.NeuralDB.MongoUtils import AdminUtils - -mongo_admin = AdminUtils("config.json") -client = mongo_admin.initialize_neuraldb() - -from MAGIST.NeuralDB.PrimaryNeuralDB import NeuralDB - -neural_db = NeuralDB("config.json", client) -neural_db.recreate_db() - -wiki = WikipediaScraper("config.json") - -for l in labels: - description = wiki.get_summary(l) - neural_db.insert_obj_desc(l, description) - -neural_db.remove_duplicates() - -from MAGIST.NLP.AudioTranscriber import GoogleAudioTranscriber -transcriber = GoogleAudioTranscriber("config.json") +use_esdb = True -text = transcriber.microphone_listener() +if use_esdb: + from MAGIST.NeuralDB.ElasticSearch import ESDB +elif not use_esdb: + from MAGIST.NeuralDB.MongoUtils import AdminUtils + from MAGIST.NeuralDB.PrimaryNeuralDB import NeuralDB -from MAGIST.NLP.SelfAttention import TextPreprocessing - -selfattention = TextPreprocessing("config.json") - -selected = [] -for i in selfattention.__call__(text): - if i[2] == "Good": - selected.append(i[1]) - - -search_res = [] -unused_terms = [] -for i in selected: - res = neural_db.search_entire_db(i) - if res != []: - search_res.append(res) - else: - unused_terms.append(i) - -search_res = np.array(search_res) -search_res = np.squeeze(search_res) - -try: - print("=====================================================================================================") - print(search_res) - print("=====================================================================================================") - print(unused_terms) -except IndexError: - print("No results found") - -from MAGIST.NLP.WordScraper import FullDictionarySearch - -dict = FullDictionarySearch("config.json") - -for i in unused_terms: - definition = dict.define(i) - neural_db.insert_word_desc(i, definition) - -neural_db.remove_duplicates() \ No newline at end of file +import numpy as np +from os import walk +from tqdm import tqdm + +filenames = next(walk("inputs"), (None, None, []))[2] # [] if no file + +cluster = RoughCluster("config/config.json") +scraper = GoogleScraper("config/config.json") +queue = MainPriorityQueue("config/config.json") + +if use_esdb: + elastic = ESDB("config/config.json", "http://localhost:9200", "config/queries.json", "config/schema.json") + elastic.delete_all_indices() + elastic.create_index("object_db", "object_db_schema") + elastic.create_index("word_db", "word_db_schema") +elif not use_esdb: + mongo_admin = AdminUtils("config/config.json") + client = mongo_admin.initialize_neuraldb() + neural_db = NeuralDB("config/config.json", client) + + neural_db.recreate_db() + +wiki = WikipediaScraper("config/config.json") + +for f in tqdm(filenames): + try: + imgs = cluster.unsupervised_clusters( + 3, f"inputs/{f}", (200, 200), "Clusters") + + labels = [] + + for i in imgs: + label = scraper.reverse_image_search(i) + labels.append(label) + + labels = np.unique(np.array(labels)) + + queue.detach_thread() + + priority = 1 + + # for l in labels: + # scraper.download_raw_img_dataset(l, 10, "Data/") + + # from MAGIST.Vision.DetectionDataManager.image_slicer import ImageSlicer + # + # slicer = ImageSlicer("config/config.json") + # + # counter = 0 + # for l in labels: + # path = os.path.join("Data", l) + # file = os.listdir(path) + # + # for f in file: + # full_path = os.path.join(path, f) + # os.system(f"mv '{full_path}' '{os.path.join(path, f'Frame{str(counter).zfill(2)}.jpg')}'") + # counter += 1 + # + # counter = 0 + # for l in labels: + # path = os.path.join("Data", l) + # os.listdir(path) + # + # + # for l in labels: + # path = os.path.join("Data", l) + # + # slicer.image_integrity_verification(path, delete_invalid=True) + # slicer.resizer((500, 500), path) + # coordinates = slicer.coordinate_compute((500, 500), (100, 100)) + # slicer.crop_segments(coordinates, path, "Sliced", l) + # + # + # + # + # from MAGIST.Vision.FullySupervisedModels.MAGIST_Lite_Detector import MAGIST_CNN + # + # cnn = MAGIST_CNN("config/config.json") + # + # queue.put_queue(cnn, name="MAGIST_CNN_Trainer", priority=10) + + all_obj_data = [] + + for l in labels: + description = wiki.get_summary(l) + if use_esdb: + all_obj_data.append([l, description]) + elif not use_esdb: + neural_db.insert_obj_desc(l, description) + + if use_esdb: + for d in all_obj_data: + temp_data = elastic.format_object_data(str(d[0]), str(d[1])) + elastic.add_doc("object_db", "object_db_schema", temp_data) + if not use_esdb: + neural_db.remove_duplicates() + queue.join_thread() + + except Exception as e: + print("=============================BIG ERROR: SKIP IMAGE=============================") + print(e) + pass diff --git a/src/main.rs b/src/main.rs new file mode 100644 index 0000000..3b5ed75 --- /dev/null +++ b/src/main.rs @@ -0,0 +1,4 @@ +fn main() { + let out = MAGIST_Rust::add(2, 3); + println!("{}", out); +} \ No newline at end of file diff --git a/src/main_hmi.py b/src/main_hmi.py new file mode 100644 index 0000000..55a0506 --- /dev/null +++ b/src/main_hmi.py @@ -0,0 +1,139 @@ +from MAGIST.NLP.WordScraper import FullDictionarySearch +from MAGIST.NLP.SelfAttention import TextPreprocessing +from MAGIST.NLP.AudioTranscriber import GoogleAudioTranscriber +from MAGIST.Vision.UnsupervisedModels.img_cluster import RoughCluster +import numpy as np +import os +from os import walk +from tqdm import tqdm + +filenames = next(walk("inputs"), (None, None, []))[2] # [] if no file + +cluster = RoughCluster("config/config.json") + +# for f in tqdm(filenames): +# try: +# imgs = cluster.unsupervised_clusters( +# 3, f"inputs/{f}", (200, 200), "Clusters") +# +# from MAGIST.Utils.WebScraper.google import GoogleScraper +# +# scraper = GoogleScraper("config/config.json") +# +# labels = [] +# +# for i in imgs: +# label = scraper.reverse_image_search(i) +# labels.append(label) +# +# labels = np.unique(np.array(labels)) +# +# from MAGIST.TaskManagment.ThreadedQueue import MainPriorityQueue +# +# queue = MainPriorityQueue("config/config.json") +# queue.detach_thread() +# +# priority = 1 +# +# for l in labels: +# scraper.download_raw_img_dataset(l, 10, "Data/") +# +# from MAGIST.Vision.DetectionDataManager.image_slicer import ImageSlicer +# +# slicer = ImageSlicer("config/config.json") +# +# counter = 0 +# for l in labels: +# path = os.path.join("Data", l) +# file = os.listdir(path) +# +# for f in file: +# full_path = os.path.join(path, f) +# os.system( +# f"mv '{full_path}' '{os.path.join(path, f'Frame{str(counter).zfill(2)}.jpg')}'") +# counter += 1 +# +# counter = 0 +# for l in labels: +# path = os.path.join("Data", l) +# os.listdir(path) +# +# for l in labels: +# path = os.path.join("Data", l) +# +# slicer.image_integrity_verification(path, delete_invalid=True) +# slicer.resizer((500, 500), path) +# coordinates = slicer.coordinate_compute((500, 500), (100, 100)) +# slicer.crop_segments(coordinates, path, "Sliced", l) +# +# from MAGIST.Vision.FullySupervisedModels.MAGIST_Lite_Detector import MAGIST_CNN +# +# cnn = MAGIST_CNN("config/config.json") +# +# queue.put_queue(cnn, name="MAGIST_CNN_Trainer", priority=10) +# +# from MAGIST.Utils.WebScraper.wikipedia import WikipediaScraper +# from MAGIST.NeuralDB.MongoUtils import AdminUtils +# +# mongo_admin = AdminUtils("config/config.json") +# client = mongo_admin.initialize_neuraldb() +# +# from MAGIST.NeuralDB.PrimaryNeuralDB import NeuralDB +# +# neural_db = NeuralDB("config/config.json", client) +# neural_db.recreate_db() +# +# wiki = WikipediaScraper("config/config.json") +# +# for l in labels: +# description = wiki.get_summary(l) +# neural_db.insert_obj_desc(l, description) +# +# neural_db.remove_duplicates() +# queue.join_thread() +# except BaseException: +# print("=============================BIG ERROR: SKIP IMAGE=============================") +# pass + + +transcriber = GoogleAudioTranscriber("config/config.json") + +text = transcriber.microphone_listener() + + +selfattention = TextPreprocessing("config/config.json") + +selected = [] +for i in selfattention.__call__(text): + if i[2] == "Good": + selected.append(i[1]) + + +search_res = [] +unused_terms = [] +for i in selected: + res = neural_db.search_entire_db(i) + if res != []: + search_res.append(res) + else: + unused_terms.append(i) + +search_res = np.array(search_res) +search_res = np.squeeze(search_res) + +try: + print("=====================================================================================================") + print(search_res) + print("=====================================================================================================") + print(unused_terms) +except IndexError: + print("No results found") + + +dict = FullDictionarySearch("config/config.json") + +for i in unused_terms: + definition = dict.define(i) + neural_db.insert_word_desc(i, definition) + +neural_db.remove_duplicates() diff --git a/src/query.py b/src/query.py new file mode 100644 index 0000000..f271eeb --- /dev/null +++ b/src/query.py @@ -0,0 +1,10 @@ +try: + from googlesearch import search +except ImportError: + print("No module named 'google' found") + +# to search +query = "A computer science portal" + +for j in search(query, num=10, stop=10, pause=2): + print(j) \ No newline at end of file diff --git a/src/test.py b/src/test.py new file mode 100644 index 0000000..04c1501 --- /dev/null +++ b/src/test.py @@ -0,0 +1,117 @@ +import os + +import numpy +import tensorflow as tf +import tensorflow_addons as tfa +print("TensorFlow version:", tf.__version__) + +from tensorflow.keras.layers import Dense, Flatten, Conv2D +import pathlib + +# tf.compat.v1.disable_eager_execution() +AUTOTUNE = tf.data.AUTOTUNE + +dirs = os.listdir(str(pathlib.Path("Data").resolve())) + + +train_ds_arr = [] +print(dirs) + + +BATCH_SIZE = 32 + +class SiameseNet(tf.keras.models.Model): + def __init__(self): + super(SiameseNet, self).__init__() + self.conv1 = Conv2D(64, 3, activation='relu') + self.conv2 = Conv2D(32, 3, activation='relu') + self.conv3 = Conv2D(64, 3, activation='relu') + self.flatten = Flatten() + self.d1 = Dense(128, activation='relu') + self.d2 = Dense(64, activation='relu') + self.d3 = Dense(32, ) + + def call(self, x1, x2): + # x1 = self.conv1(x1) + # x1 = self.conv2(x1) + x1 = self.conv3(x1) + x1 = self.flatten(x1) + x1 = self.d1(x1) + x1 = self.d2(x1) + x1 = self.d3(x1) + + # x2 = self.conv1(x2) + # x2 = self.conv2(x2) + x2 = self.conv3(x2) + x2 = self.flatten(x2) + x2 = self.d1(x2) + x2 = self.d2(x2) + x2 = self.d3(x2) + return x1, x2 + +snn = SiameseNet() +optimizer = tf.keras.optimizers.Adam() +# loss = tf.keras.losses.contrastive_loss() + +def get_label(file_path): + # Convert the path to a list of path components + parts = tf.strings.split(file_path, os.path.sep) + # The second to last is the class-directory + one_hot = parts[-2] == dirs + # Integer encode the label + return tf.argmax(one_hot) + +def decode_img(img): + # Convert the compressed string to a 3D uint8 tensor + img = tf.io.decode_jpeg(img, channels=3) + # Resize the image to the desired size + return tf.image.resize(img, [100, 100]) + +def process_path(file_path): + label = get_label(file_path) + # Load the raw data from the file as a string + img = tf.io.read_file(file_path) + img = decode_img(img) + return img, label + +for d in dirs: + data_dir = pathlib.Path(os.path.join("Data", d)).with_suffix('') + x = tf.data.Dataset.list_files(str(data_dir/'*'), shuffle=False) + + x = x.map(process_path, num_parallel_calls=AUTOTUNE) + train_ds_arr.append(x) + +class ContrastiveLoss(tf.keras.losses.Loss): + def __init__(self, margin=1.0): + super().__init__() + self.margin = margin + + def forward(self, x0, x1, y): + # euclidian distance + part1 = (1-y) * tf.pow(tf.subtract(x0, x1), 2) + part2 = y * tf.maximum(0.0, (self.margin-tf.pow(tf.subtract(x0, x1), 2))) + return part1 + part2 + +snn.compile(optimizer=optimizer, loss=ContrastiveLoss, metrics=["accuracy"]) +lossModel = tfa.losses.ContrastiveLoss()#ContrastiveLoss() + +train_loss = tf.keras.metrics.Mean(name='train_loss') +train_accuracy = tfa.losses.ContrastiveLoss() + +@tf.function +def train(): + for _ in range(10): + for (img1, label1), (img2, label2) in zip(train_ds_arr[0].take(BATCH_SIZE), train_ds_arr[1].take(BATCH_SIZE)): + img1 = tf.expand_dims(img1, axis=0) / 255.0 + img2 = tf.expand_dims(img2, axis=0) / 255.0 + with tf.GradientTape() 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b/testing/Rust-Python-MicroAPI/test_microapi/compute.rs new file mode 100644 index 0000000..53bd3d5 --- /dev/null +++ b/testing/Rust-Python-MicroAPI/test_microapi/compute.rs @@ -0,0 +1,26 @@ +use std::time::{Duration, Instant}; + + +fn compute(n: f32, scale: f32, scalar: f32) -> f32 { + let x = n * scale; + let part1 = (n * x).sin(); + let part2 = (1.0/x).sin(); + ((part1 * part2).abs()) * scalar +} + +fn main() { + let mut values = vec![]; + + for i in 1..30 { + let start = Instant::now(); + let out = compute(50.0, 0.1, 1.0); + let duration = start.elapsed(); + + values.push(duration); + + + println!("Time elapsed in expensive_function() is: {:.10?}", duration); + println!("Computed Values is: {:.50?}", out); + } + println!("{:.10?}", values) +} diff --git a/testing/Rust-Python-MicroAPI/test_microapi/src/main.rs b/testing/Rust-Python-MicroAPI/test_microapi/src/main.rs new file mode 100644 index 0000000..1b4432e --- /dev/null +++ b/testing/Rust-Python-MicroAPI/test_microapi/src/main.rs @@ -0,0 +1,80 @@ +extern crate xml_rpc; + +use xml_rpc::{Fault, Server}; +use std::net::{IpAddr, Ipv4Addr, SocketAddr}; +use serde::{Serialize, Deserialize}; + +// #[derive(Clone, Debug, Serialize, Deserialize)] +// struct MoveRightParams { +// pub point: Point, +// pub m: i64, +// } +// +// +// #[derive(Clone, Debug, Serialize, Deserialize)] +// struct Point { +// pub x: i64, +// pub y: i64, +// } +// +// fn move_right(mut p: MoveRightParams) -> Result { +// p.point.x += p.m; +// +// Ok(p.point) +// } + +#[derive(Clone, Debug, Serialize, Deserialize)] +struct PosEmbeddingInputs { + pub n: f32, + pub scale: f32, + pub scalar: f32 +} + +fn compute(inputs: PosEmbeddingInputs) -> Result { + let n = inputs.n; + let scale = inputs.scale; + let scalar = inputs.scalar; + let x = n * scale; + let part1 = (n * x).sin(); + let part2 = (1.0/x).sin(); + Ok(((part1 * part2).abs()) * scalar) +} + +fn main() { + let socket = SocketAddr::new(IpAddr::V4(Ipv4Addr::new(127, 0, 0, 1)), 8080); + let mut server = Server::new(); + + server.register_simple("pos_embedding", &compute); + + let bound_server = server.bind(&socket).unwrap(); + + println!("Running server"); + bound_server.run(); +} +// +// use std::time::{Duration, Instant}; +// use rust_decimal::Decimal; +// +// fn compute(n: Decimal, scale: Decimal, scalar: Decimal) -> Decimal { +// let x = n * scale; +// let part1 = (n * x).sin(); +// let part2 = (Decimal::new(10, 1)/x).sin(); +// ((part1 * part2).abs()) * scalar +// +// } +// +// fn main() { +// let mut values = vec![]; +// +// for i in 1..30 { +// let start = Instant::now(); +// let out = compute(Decimal::new(50, 0), Decimal::new(01, 1), Decimal::new(1, 0)); +// let duration = start.elapsed(); +// +// values.push(duration); +// +// +// println!("Time elapsed in expensive_function() is: {:?}", duration); +// } +// println!("{:?}", values) +// } diff --git a/testing/Rust-Python-MicroAPI/test_microapi/src/slithery.py b/testing/Rust-Python-MicroAPI/test_microapi/src/slithery.py new file mode 100644 index 0000000..de380c2 --- /dev/null +++ b/testing/Rust-Python-MicroAPI/test_microapi/src/slithery.py @@ -0,0 +1,49 @@ +# import xmlrpc.client +# import time +# +# proxy = xmlrpc.client.ServerProxy("http://127.0.0.1:8080") +# +# # print(proxy.system.listMethods()) +# +# +# params = {"point": {"x": 5, "y": 5}, "m": 2} +# +# params = {"n": 5.0, "scale": 10.0, "scalar": 1.0} +# +# start = time.time() +# move_right = proxy.pos_embedding(params) +# end = time.time() +# +# print(move_right) +# print("Time consumed in working: ",(end - start)) + + + +import time +import numpy as np + +def __positional_embedding_function(n, scale=0.1, scalar=1): + """Computes the positional embedding. + + :param n: The position to be computed. + :param scale: The scale of the positional embedding function. + :param scalar: The scalar multiplier to the output of the positional embedding. + + :return: The positional embedding. + """ + x = np.multiply(n, scale) + part1 = np.sin(np.multiply(n, x)) + part2 = np.sin(np.divide(1, x)) + return np.multiply(np.abs(np.multiply(part1, part2)), scalar) + +values = [] + +for i in range(10): + start = time.time() + out = __positional_embedding_function(50) + end = time.time() + print("Time consumed in working: ",(end - start)*1000000000) + print(out) + values.append((end - start)*1000000000) + +print(values) \ No newline at end of file diff --git a/testing/StereoClustering/main.py b/testing/StereoClustering/main.py new file mode 100644 index 0000000..c58e4de --- /dev/null +++ b/testing/StereoClustering/main.py @@ -0,0 +1,27 @@ +import cv2 +import numpy as np + +image = cv2.imread('disp0.jpg') + +kernel1 = np.array([[-1, 0, 1], + [-1, 0, 1], + [-1, 0, 1]]) + +# kernel1 = np.array([[2, 4, 5, 4, 2], +# [4, 9, 12, 9, 4], +# [5, 12, 15, 12, 5], +# [4, 9, 12, 9, 4], +# [2, 4, 5, 4, 2]]) + + + +image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + +identity = cv2.filter2D(src=image, ddepth=-1, kernel=kernel1) + +cv2.imshow('Original', image) +cv2.imshow('Identity', identity) + +cv2.waitKey() +# cv2.imwrite('identity.jpg', identity) +cv2.destroyAllWindows()