yolo.py

import cv2
import numpy as np 
import time
#from combined_thresh import combined_thresh
#from perspective_transform import perspective_transform
#from Line import Line
#from line_fit import line_fit, tune_fit, final_viz, calc_curve, calc_vehicle_offset
import pickle


#Load yolo
def load_yolo():
	net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
	net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
	net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

	classes = [] 
	with open("coco.names", "r") as f:
		classes = [line.strip() for line in f.readlines()] 
	
	output_layers = [layer_name for layer_name in net.getUnconnectedOutLayersNames()]
	colors = np.random.uniform(0, 255, size=(len(classes), 3))
	return net, classes, colors, output_layers

def load_image(img_path):
	# image loading
	img = cv2.imread(img_path)
	img = cv2.resize(img, None, fx=0.4, fy=0.4)
	height, width, channels = img.shape
	return img, height, width, channels

def start_webcam():
	cap = cv2.VideoCapture(0)

	return cap


def display_blob(blob):
	'''
		Three images each for RED, GREEN, BLUE channel
	'''
	for b in blob:
		for n, imgb in enumerate(b):
			cv2.imshow(str(n), imgb)

def detect_objects(img, net, outputLayers):			
	blob = cv2.dnn.blobFromImage(img, scalefactor=0.00392, size=(320, 320), mean=(0, 0, 0), swapRB=True, crop=False)
	net.setInput(blob)
	outputs = net.forward(outputLayers)
	return blob, outputs

def get_box_dimensions(outputs, height, width):
	boxes = []
	confs = []
	class_ids = []
	for output in outputs:
		for detect in output:
			scores = detect[5:]
			class_id = np.argmax(scores)
			conf = scores[class_id]
			if conf > 0.3:
				center_x = int(detect[0] * width)
				center_y = int(detect[1] * height)
				w = int(detect[2] * width)
				h = int(detect[3] * height)
				x = int(center_x - w/2)
				y = int(center_y - h / 2)
				boxes.append([x, y, w, h])
				confs.append(float(conf))
				class_ids.append(class_id)
	return boxes, confs, class_ids
			
def draw_labels(boxes, confs, colors, class_ids, classes, img): 
	indexes = cv2.dnn.NMSBoxes(boxes, confs, 0.5, 0.4)
	font = cv2.FONT_HERSHEY_PLAIN
	for i in range(len(boxes)):
		if i in indexes:
			x, y, w, h = boxes[i]
			label = str(classes[class_ids[i]])
			color = colors[i]
			cv2.rectangle(img, (x,y), (x+w, y+h), color, 2)
			cv2.putText(img, label, (x, y - 5), font, 1, color, 1)
	cv2.imshow("Image", img)


def start_video(video_path):
	model, classes, colors, output_layers = load_yolo()
	cap = cv2.VideoCapture(video_path)
	i = 0
	while True:
		_, frame = cap.read()
		if i%5 == 0:			
			height, width, channels = frame.shape
			blob, outputs = detect_objects(frame, model, output_layers)
			boxes, confs, class_ids = get_box_dimensions(outputs, height, width)
			#frame = annotate_image(frame)
			draw_labels(boxes, confs, colors, class_ids, classes, frame)
			key = cv2.waitKey(1)
		i = i+1
		if key == 27:
			break
	cap.release()



if __name__ == '__main__':
	video_path = "project_video.mp4"
	start_video(video_path)
	cv2.destroyAllWindows()