mask = cv2.inRange(image, lower, upper) output = cv2.bitwise_and(image, image, mask = mask) # show the images cv2.imshow("images", np.hstack([image, output])) cv2.waitKey(0) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 我们开始在第2行上遍历boundaries的上限和...
color = self.detectColor(roi) result.append(color) return np.sum(result) else: return 0 def detectColor(self, image): hsv_img = cv2.cvtColor(image,cv2.COLOR_BGR2HSV) red_min = np.array([0,43,46]) red_max = np.array([10,255,255]) red_min2 = np.array([156,43,46]) red_m...
cv2.imshow("image_lines", image) # 统计概率霍夫线变换 def line_detect_possible_demo(image): gray = cv2.cvtColor(image, cv2.COLOR_BGRA2GRAY) edges = cv2.Canny(gray, 50, 150, apertureSize=3) lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 100, minLineLength=50, maxLineGap=10) fo...
right,bottom,left=face_location # 结报操作,得到每张人脸的四个位置信息print("已识别到人脸部位,限速区域为:top{}, right{}, bottom{}, left{}".format(top,right,bottom,left))# face_image=image[top:bottom,left:right]# pil_image=Image.fromarray(face_image)# pil_image.show()start=(left,top...
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)第四步：加载人脸识别模型 加载人脸检测模型很简单，OpenCV已经帮我们封装好了，只需要在cv2.CascadeClassifier()中传递一个模型位置即可。face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')这里，我们将模型文件复制到程序文件所在的同一文件夹...
rgb_image = cv2.cvtColor(img_restore, cv2.COLOR_BGR2RGB) # rgb_image = img_restore.copy() unique, counts = np.unique(rgb_image.reshape(-1, 3), axis=0, return_counts=True) r, g, b = unique[np.argmax(counts)] # 平均RGB值 ...
检测颜色defdetect_color(image,color):# gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 灰度gs=cv2.GaussianBlur(image,(5,5),0)# 高斯模糊hsv=cv2.cvtColor(gs,cv2.COLOR_BGR2HSV)# HSVerode_hsv=cv2.erode(hsv,None,iterations=2)# 腐蚀inRange_hsv=cv2.inRange(erode_hsv,color_dist[color][...
image=get_file_content('D:\\before.jpg') """ 如果有可选参数 """ options={} """ 带参数调用图像主体检测 """ ret=client.objectDetect(image, options) print(ret)#会输出四个值,但和python里的不同 # cv2.rectangle(image, 左上角坐标, 右下角坐标, color, 线条粗度) ...
(img,cv2.COLOR_BGR2GRAY)# 检测结果# 调整图片尺寸 scaleFactor = 1.2# 最小尺寸 minSize = (45,45)# 最大尺寸 maxSize = (80,80)detections = face_detector.detectMultiScale(img_gray,scaleFactor =1.1,minNeighbors =8)# ,minSize = (150,150) ,maxSize= (200,200)# 解析结果color = (0,255...
image_in_gray_scale = cv2.cvtColor(group_of_people_image,cv2.COLOR_BGR2GRAY)faces = frontal_face_classifier.detectMultiScale(image=image_in_gray_scale,scaleFactor=1.3, minNeighbors=6)for (x_axis, y_axis, weight,height) in faces:cv2.rectangle(group_of_people_image,(x_axis, y_axis), ...