# USAGE

# python yolo.py --image images/baggage_claim.jpg --yolo yolo-coco

"""

A Yolo image processor with a GUI front-end

The original code was command line driven. Now these parameters are collected via a GUI

old usage: yolo_video.py [-h] -i INPUT -o OUTPUT -y YOLO [-c CONFIDENCE]

[-t THRESHOLD]

"""

# import the necessary packages

import numpy as np

import argparse

import time

import cv2

import os

import PySimpleGUIQt as sg

layout = [

[sg.Text('YOLO')],

[sg.Text('Path to image'), sg.In(r'C:/Python/PycharmProjects/YoloObjectDetection/images/baggage_claim.jpg',size=(40,1), key='image'), sg.FileBrowse()],

[sg.Text('Yolo base path'), sg.In(r'yolo-coco',size=(40,1), key='yolo'), sg.FolderBrowse()],

[sg.Text('Confidence'), sg.Slider(range=(0,10),orientation='h', resolution=1, default_value=5, size=(15,15), key='confidence')],

[sg.Text('Threshold'), sg.Slider(range=(0,10), orientation='h', resolution=1, default_value=3, size=(15,15), key='threshold')],

[sg.OK(), sg.Cancel(), sg.Stretch()]

]

win = sg.Window('YOLO',

default_element_size=(14,1),

text_justification='right',

auto_size_text=False).Layout(layout)

event, values = win.Read()

args = values

win.Close()

# construct the argument parse and parse the arguments

# ap = argparse.ArgumentParser()

# ap.add_argument("-i", "--image", required=True,

# help="path to input image")

# ap.add_argument("-y", "--yolo", required=True,

# help="base path to YOLO directory")

# ap.add_argument("-c", "--confidence", type=float, default=0.5,

# help="minimum probability to filter weak detections")

# ap.add_argument("-t", "--threshold", type=float, default=0.3,

# help="threshold when applyong non-maxima suppression")

# args = vars(ap.parse_args())

# load the COCO class labels our YOLO model was trained on

args['threshold'] = float(args['threshold']/10)

args['confidence'] = float(args['confidence']/10)

labelsPath = os.path.sep.join([args["yolo"], "coco.names"])

LABELS = open(labelsPath).read().strip().split("\n")

# initialize a list of colors to represent each possible class label

np.random.seed(42)

COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),

dtype="uint8")

# derive the paths to the YOLO weights and model configuration

weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"])

configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"])

# load our YOLO object detector trained on COCO dataset (80 classes)

print("[INFO] loading YOLO from disk...")

net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)

# load our input image and grab its spatial dimensions

image = cv2.imread(args["image"])

(H, W) = image.shape[:2]

# determine only the *output* layer names that we need from YOLO

ln = net.getLayerNames()

ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# construct a blob from the input image and then perform a forward

# pass of the YOLO object detector, giving us our bounding boxes and

# associated probabilities

blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),

swapRB=True, crop=False)

net.setInput(blob)

start = time.time()

layerOutputs = net.forward(ln)

end = time.time()

# show timing information on YOLO

print("[INFO] YOLO took {:.6f} seconds".format(end - start))

# initialize our lists of detected bounding boxes, confidences, and

# class IDs, respectively

boxes = []

confidences = []

classIDs = []

# loop over each of the layer outputs

for output in layerOutputs:

# loop over each of the detections

for detection in output:

# extract the class ID and confidence (i.e., probability) of

# the current object detection

scores = detection[5:]

classID = np.argmax(scores)

confidence = scores[classID]

# filter out weak predictions by ensuring the detected

# probability is greater than the minimum probability

if confidence > args["confidence"]:

# scale the bounding box coordinates back relative to the

# size of the image, keeping in mind that YOLO actually

# returns the center (x, y)-coordinates of the bounding

# box followed by the boxes' width and height

box = detection[0:4] * np.array([W, H, W, H])

(centerX, centerY, width, height) = box.astype("int")

# use the center (x, y)-coordinates to derive the top and

# and left corner of the bounding box

x = int(centerX - (width / 2))

y = int(centerY - (height / 2))

# update our list of bounding box coordinates, confidences,

# and class IDs

boxes.append([x, y, int(width), int(height)])

confidences.append(float(confidence))

classIDs.append(classID)

# apply non-maxima suppression to suppress weak, overlapping bounding

# boxes

idxs = cv2.dnn.NMSBoxes(boxes, confidences, args["confidence"],

args["threshold"])

# ensure at least one detection exists

if len(idxs) > 0:

# loop over the indexes we are keeping

for i in idxs.flatten():

# extract the bounding box coordinates

(x, y) = (boxes[i][0], boxes[i][1])

(w, h) = (boxes[i][2], boxes[i][3])

# draw a bounding box rectangle and label on the image

color = [int(c) for c in COLORS[classIDs[i]]]

cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)

text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])

cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,

0.5, color, 2)

# show the output image

imgbytes = cv2.imencode('.png', image)[1].tobytes() # ditto

layout = [

[sg.Text('Yolo Output')],

[sg.Image(data=imgbytes)],

[sg.OK(), sg.Cancel()]

]

win = sg.Window('YOLO',

default_element_size=(14,1),

text_justification='right',

auto_size_text=False).Layout(layout)

event, values = win.Read()

win.Close()

# cv2.imshow("Image", image)

cv2.waitKey(0)