Preface

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Author: machine learning and statistics

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At present, computer vision (CV), natural language processing (NLP) and speech recognition are the three hot directions of artificial intelligence, while object detection in computer vision is widely used, such as automatic driving, video monitoring, industrial quality inspection, medical diagnosis and other scenes.

Here is our complete code implementation (debugged):

 1 import numpy as np
 2 import cv2
 3 font = cv2.FONT_HERSHEY_SIMPLEX
 4 lower_green = np.array([35, 110, 106])  # Green range low threshold
 5 upper_green = np.array([77, 255, 255])  # Green range high threshold
 6 lower_red = np.array([0, 127, 128])  # Red range low threshold
 7 upper_red = np.array([10, 255, 255])  # Red range high threshold
 8 #Need more colors, you can go to Baidu HSV Threshold!
 9 # cap = cv2.VideoCapture('1.mp4')  # Open video file
10 cap = cv2.VideoCapture(0)#open USB Camera
11 if (cap.isOpened()):  # Video opened successfully
12     flag = 1
13 else:
14     flag = 0
15 num = 0
16 if (flag):
17     while (True):
18         ret, frame = cap.read()  # Read a frame
19        
20         if ret == False:  # Failed to read frame
21             break
22         hsv_img = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
23         mask_green = cv2.inRange(hsv_img, lower_green, upper_green)  # Delete selection according to color range
24         mask_red = cv2.inRange(hsv_img, lower_red, upper_red) 
25  # Delete selection according to color range
26         mask_green = cv2.medianBlur(mask_green, 7)  # median filtering
27         mask_red = cv2.medianBlur(mask_red, 7)  # median filtering
28         mask = cv2.bitwise_or(mask_green, mask_red)
29         mask_green, contours, hierarchy = cv2.findContours(mask_green, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
30         mask_red, contours2, hierarchy2 = cv2.findContours(mask_red, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
31 ​
32         for cnt in contours:
33             (x, y, w, h) = cv2.boundingRect(cnt)
34             cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 255), 2)
35             cv2.putText(frame, "Green", (x, y - 5), font, 0.7, (0, 255, 0), 2)
36 ​
37         for cnt2 in contours2:
38             (x2, y2, w2, h2) = cv2.boundingRect(cnt2)
39             cv2.rectangle(frame, (x2, y2), (x2 + w2, y2 + h2), (0, 255, 255), 2)
40             cv2.putText(frame, "Red", (x2, y2 - 5), font, 0.7, (0, 0, 255), 2)
41         num = num + 1
42         cv2.imshow("dection", frame)
43         cv2.imwrite("imgs/%d.jpg"%num, frame)
44         if cv2.waitKey(20) & 0xFF == 27:
45             break
46 cv2.waitKey(0)
47 cv2.destroyAllWindows()

As shown in the figure, we will detect the red area

Final rendering:

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