mbnet.py

import cv2
import numpy as np 
#import time 


#video_path = 'D:/OfficeWork/VS_code_exp/exp/video_1.mp4'
#image_path = 'D:/OfficeWork/VS_code_exp/exp/test.jpg.jpg'

def load_model():
    model= cv2.dnn.readNet(model='frozen_inference_graph.pb',
                           config='ssd_mobilenet_v2_coco_2018_03_29.pbtxt.txt',
                           framework='TensorFlow')
    with open('object_detection_classes_coco.txt', 'r') as f:
        class_names = f.read().split('\n')
    COLORS = np.random.uniform(0, 255, size=(len(class_names), 3))
    return model, class_names, COLORS   

def load_img(img_path):
    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 detect_objects(img, net):			
	blob = cv2.dnn.blobFromImage(img, size=(300, 300), mean=(104, 117, 123), swapRB=True)
	net.setInput(blob)
	outputs = net.forward()
	#print (outputs)
	return blob, outputs

def get_box_dimensions(outputs, height, width):
	boxes = []
	class_ids = []
 
	for detect in outputs[0,0,:,:]:
		scores = detect[2]
		class_id = detect[1]
		if scores > 0.3:
			center_x = int(detect[0] * width)
			center_y = int(detect[1] * height)
			w = int(detect[5] * width)
			h = int(detect[6] * height)
			x = int((detect[3] * width))
			y = int((detect[4] * height))
			boxes.append([x, y, w, h])
			class_ids.append(class_id)
	return boxes, class_ids

def draw_labels(boxes, colors, class_ids, classes, img): 
	font = cv2.FONT_HERSHEY_PLAIN
	model, classes, colors = load_model()
	for i in range(len(boxes)):
		x, y, w, h = boxes[i]
		label = classes[int(class_ids[0])-1]
		color = colors[i]
		cv2.rectangle(img, (x,y), (w,h), color, 5)
		cv2.putText(img, label, (x, y - 5), font, 5, color, 5)
	return img

def image_detect(img_path): 
	model, classes, colors = load_model()
	image, height, width, channels = load_img(img_path)
	blob, outputs = detect_objects(image, model)
	boxes, class_ids = get_box_dimensions(outputs, height, width)
	image1 = draw_labels(boxes, colors, class_ids, classes, image)
	return image1
	

#def start_video(video_path):
	model, classes, colors = load_model()
	cap = cv2.VideoCapture(video_path)
	while True:
		_, frame = cap.read()
		height, width, channels = frame.shape
		blob, outputs = detect_objects(frame, model)
		boxes, class_ids = get_box_dimensions(outputs, height, width)
		frame=draw_labels(boxes, colors, class_ids, classes, frame)
		yield cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

cv2.destroyAllWindows()