# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import os

import sys

__dir__ = os.path.dirname(os.path.abspath(__file__))

sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))

import copy

import cv2

import numpy as np

from python.predict_rec import RecPredictor

from python.predict_det import DetPredictor

from vector_search import Graph_Index

from utils import logger

from utils import config

from utils.get_image_list import get_image_list

from utils.draw_bbox import draw_bbox_results

class SystemPredictor(object):

def __init__(self, config):

self.config = config

self.rec_predictor = RecPredictor(config)

self.det_predictor = DetPredictor(config)

assert 'IndexProcess' in config.keys(), "Index config not found ... "

self.return_k = self.config['IndexProcess']['return_k']

self.search_budget = self.config['IndexProcess']['search_budget']

self.Searcher = Graph_Index(

dist_type=config['IndexProcess']['dist_type'])

self.Searcher.load(config['IndexProcess']['index_path'])

def append_self(self, results, shape):

results.append({

"class_id": 0,

"score": 1.0,

"bbox":

np.array([0, 0, shape[1], shape[0]]), # xmin, ymin, xmax, ymax

"label_name": "foreground",

})

return results

def nms_to_rec_results(self, results, thresh=0.1):

filtered_results = []

x1 = np.array([r["bbox"][0] for r in results]).astype("float32")

y1 = np.array([r["bbox"][1] for r in results]).astype("float32")

x2 = np.array([r["bbox"][2] for r in results]).astype("float32")

y2 = np.array([r["bbox"][3] for r in results]).astype("float32")

scores = np.array([r["rec_scores"] for r in results])

areas = (x2 - x1 + 1) * (y2 - y1 + 1)

order = scores.argsort()[::-1]

while order.size > 0:

i = order[0]

xx1 = np.maximum(x1[i], x1[order[1:]])

yy1 = np.maximum(y1[i], y1[order[1:]])

xx2 = np.minimum(x2[i], x2[order[1:]])

yy2 = np.minimum(y2[i], y2[order[1:]])

w = np.maximum(0.0, xx2 - xx1 + 1)

h = np.maximum(0.0, yy2 - yy1 + 1)

inter = w * h

ovr = inter / (areas[i] + areas[order[1:]] - inter)

inds = np.where(ovr <= thresh)[0]

order = order[inds + 1]

filtered_results.append(results[i])

return filtered_results

def predict(self, img):

output = []

# st1: get all detection results

results = self.det_predictor.predict(img)

# st2: add the whole image for recognition to improve recall

results = self.append_self(results, img.shape)

# st3: recognition process, use score_thres to ensure accuracy

for result in results:

preds = {}

xmin, ymin, xmax, ymax = result["bbox"].astype("int")

crop_img = img[ymin:ymax, xmin:xmax, :].copy()

rec_results = self.rec_predictor.predict(crop_img)

preds["bbox"] = [xmin, ymin, xmax, ymax]

scores, docs = self.Searcher.search(

query=rec_results,

return_k=self.return_k,

search_budget=self.search_budget)

# just top-1 result will be returned for the final

if scores[0] >= self.config["IndexProcess"]["score_thres"]:

preds["rec_docs"] = docs[0]

preds["rec_scores"] = scores[0]

output.append(preds)

# st5: nms to the final results to avoid fetching duplicate results

output = self.nms_to_rec_results(

output, self.config["Global"]["rec_nms_thresold"])

return output

def main(config):

system_predictor = SystemPredictor(config)

image_list = get_image_list(config["Global"]["infer_imgs"])

assert config["Global"]["batch_size"] == 1

for idx, image_file in enumerate(image_list):

img = cv2.imread(image_file)[:, :, ::-1]

output = system_predictor.predict(img)

draw_bbox_results(img, output, image_file)

print(output)

return

if __name__ == "__main__":

args = config.parse_args()

config = config.get_config(args.config, overrides=args.override, show=True)

main(config)