任务描述：识别这种停车场图的 空车位 与 被占用车位
识别流程：预处理 -> 获得车位坐标的字典 -> 训练VGG网络进行二分类

img_process 图像预处理过程

1.select_rgb_white_yellow 过滤背景（得到mask）

inRange(图,min阈值,max阈值) 小于min(大于max)的为0,min-max的为255
dst = cv.bitwise_and(src1, src2[, dst[, mask]]
src1:图1 src2:图2 mask：图1和图2’与’操作的掩码输出图像

def select_rgb_white_yellow(self,image): # 过滤掉背景lower = np.uint8([120, 120, 120])upper = np.uint8([255, 255, 255])# lower_red和高于upper_red的部分分别变成0，lower_red～upper_red之间的值变成255,相当于过滤背景white_mask = cv2.inRange(image, lower, upper)self.cv_show('white_mask',white_mask)masked = cv2.bitwise_and(image, image, mask = white_mask)self.cv_show('masked',masked)return masked

2.convert_gray_scale # rgb转gray图

3.detect_edges # Canny检测

def convert_gray_scale(self,image):return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
def detect_edges(self,image, low_threshold=50, high_threshold=200):return cv2.Canny(image, low_threshold, high_threshold)

4.select_region # 针对当前任务手动指定区域

cv2.circle(img,中心点(x,y),半径r,color,粗细) 根据给定的圆心和半径等画圆画出指定点

5.filter_region # 基于指定点剔除掉不需要的地方

np.zeros_like(img) # 生成一个跟img数组一样大小的全0(黑)的数组
cv2.fillPoly(mask, vertices, 255) # 在mask上画多边形,由这vertices的点组成的,填充为白色
cv2.bitwise_and # 只在mask为255上才能留下来其他就过滤掉了

def filter_region(self,image, vertices):"""剔除掉不需要的地方"""mask = np.zeros_like(image)if len(mask.shape)==2:cv2.fillPoly(mask, vertices, 255)self.cv_show('mask', mask)    return cv2.bitwise_and(image, mask)def select_region(self,image):"""手动选择区域"""# first, define the polygon by verticesrows, cols = image.shape[:2]pt_1  = [cols*0.05, rows*0.90]pt_2 = [cols*0.05, rows*0.70]pt_3 = [cols*0.30, rows*0.55]pt_4 = [cols*0.6, rows*0.15]pt_5 = [cols*0.90, rows*0.15] pt_6 = [cols*0.90, rows*0.90]vertices = np.array([[pt_1, pt_2, pt_3, pt_4, pt_5, pt_6]], dtype=np.int32) point_img = image.copy()       point_img = cv2.cvtColor(point_img, cv2.COLOR_GRAY2RGB)for point in vertices[0]:cv2.circle(point_img, (point[0],point[1]), 10, (0,0,255), 4)self.cv_show('point_img',point_img)return self.filter_region(image, vertices)

6.hough_lines # 找直线

HoughLinesP函数是统计概率霍夫线变换函数，该函数能输出检测到的直线的端点 (x_{0}, y_{0}, x_{1}, y_{1})，
其函数原型为：HoughLinesP(image, rho, theta, threshold[, lines[, minLineLength[, maxLineGap]]]) -> lines
cv2.HoughLinesP(边缘检测后的二值图) 统计概率霍夫线变换函数

7.draw_lines # 过滤线

abs(y2-y1) <=1 不要斜线
abs(x2-x1) >=25 and abs(x2-x1) <= 55 长度太长的也不要

def hough_lines(self,image):# 输入的图像需要是边缘检测后的结果# minLineLengh(线的最短长度，比这个短的都被忽略)和MaxLineCap（两条直线之间的最大间隔，小于此值，认为是一条直线）# rho距离精度,theta角度精度,threshod超过设定阈值才被检测出线段return cv2.HoughLinesP(image, rho=0.1, theta=np.pi/10, threshold=15, minLineLength=9, maxLineGap=4)def draw_lines(self,image, lines, color=[255, 0, 0], thickness=2, make_copy=True):# 过滤霍夫变换检测到直线if make_copy:image = np.copy(image) cleaned = []for line in lines:for x1,y1,x2,y2 in line:if abs(y2-y1) <=1 and abs(x2-x1) >=25 and abs(x2-x1) <= 55:cleaned.append((x1,y1,x2,y2))cv2.line(image, (x1, y1), (x2, y2), color, thickness)print(" No lines detected: ", len(cleaned))return image

8.identify_blocks # 区域划分

step 3: 指定行间距小于10的,划分为不同的列,共12簇

def identify_blocks(self,image, lines, make_copy=True):if make_copy:new_image = np.copy(image)#Step 1: 过滤部分直线cleaned = []for line in lines:for x1,y1,x2,y2 in line:if abs(y2-y1) <=1 and abs(x2-x1) >=25 and abs(x2-x1) <= 55:cleaned.append((x1,y1,x2,y2))#Step 2: 对直线按照x1进行排序import operatorlist1 = sorted(cleaned, key=operator.itemgetter(0, 1))#Step 3: 找到多个列，相当于每列是一排车clusters = {}dIndex = 0clus_dist = 10for i in range(len(list1) - 1):distance = abs(list1[i+1][0] - list1[i][0])if distance <= clus_dist:if not dIndex in clusters.keys(): clusters[dIndex] = []clusters[dIndex].append(list1[i])clusters[dIndex].append(list1[i + 1]) else:dIndex += 1#Step 4: 得到坐标rects = {}i = 0for key in clusters:all_list = clusters[key]cleaned = list(set(all_list))if len(cleaned) > 5:cleaned = sorted(cleaned, key=lambda tup: tup[1])avg_y1 = cleaned[0][1]avg_y2 = cleaned[-1][1]avg_x1 = 0avg_x2 = 0for tup in cleaned:avg_x1 += tup[0]avg_x2 += tup[2]avg_x1 = avg_x1/len(cleaned)avg_x2 = avg_x2/len(cleaned)rects[i] = (avg_x1, avg_y1, avg_x2, avg_y2)i += 1print("Num Parking Lanes: ", len(rects))#Step 5: 把列矩形画出来buff = 7for key in rects:tup_topLeft = (int(rects[key][0] - buff), int(rects[key][1]))tup_botRight = (int(rects[key][2] + buff), int(rects[key][3]))cv2.rectangle(new_image, tup_topLeft,tup_botRight,(0,255,0),3)return new_image, rects

9.draw_parking

根据上一步切分的列，得到坐标。根据纵坐标的间距不断切分停车位，车位间距gap为15.5
(y2-y1)/gap表示能停多少辆车

def draw_parking(self,image, rects, make_copy = True, color=[255, 0, 0], thickness=2, save = True):if make_copy:new_image = np.copy(image)gap = 15.5spot_dict = {} # 字典：一个车位对应一个位置tot_spots = 0# 微调adj_y1 = {0: 20, 1:-10, 2:0, 3:-11, 4:28, 5:5, 6:-15, 7:-15, 8:-10, 9:-30, 10:9, 11:-32}adj_y2 = {0: 30, 1: 50, 2:15, 3:10, 4:-15, 5:15, 6:15, 7:-20, 8:15, 9:15, 10:0, 11:30}adj_x1 = {0: -8, 1:-15, 2:-15, 3:-15, 4:-15, 5:-15, 6:-15, 7:-15, 8:-10, 9:-10, 10:-10, 11:0}adj_x2 = {0: 0, 1: 15, 2:15, 3:15, 4:15, 5:15, 6:15, 7:15, 8:10, 9:10, 10:10, 11:0}# 继续微调for key in rects:tup = rects[key]x1 = int(tup[0]+ adj_x1[key])x2 = int(tup[2]+ adj_x2[key])y1 = int(tup[1] + adj_y1[key])y2 = int(tup[3] + adj_y2[key])cv2.rectangle(new_image, (x1, y1),(x2,y2),(0,255,0),2)# (y2-y1)//gap表示能停多少辆车num_splits = int(abs(y2-y1)//gap)for i in range(0, num_splits+1):y = int(y1 + i*gap)cv2.line(new_image, (x1, y), (x2, y), color, thickness)if key > 0 and key < len(rects) -1 :        #竖直线x = int((x1 + x2)/2)cv2.line(new_image, (x, y1), (x, y2), color, thickness)# 计算数量if key == 0 or key == (len(rects) -1):tot_spots += num_splits +1else:tot_spots += 2*(num_splits +1)  # 双排的乘2# 字典对应好if key == 0 or key == (len(rects) -1):for i in range(0, num_splits+1):cur_len = len(spot_dict)y = int(y1 + i*gap)spot_dict[(x1, y, x2, y+gap)] = cur_len +1        else:for i in range(0, num_splits+1):cur_len = len(spot_dict)y = int(y1 + i*gap)x = int((x1 + x2)/2)spot_dict[(x1, y, x, y+gap)] = cur_len +1spot_dict[(x, y, x2, y+gap)] = cur_len +2   print("total parking spaces: ", tot_spots, cur_len)if save:filename = 'with_parking.jpg'cv2.imwrite(filename, new_image)return new_image, spot_dict

save_images_for_cnn 保存所有切割出来的图片

非必须的步骤，主要是要获得车位坐标的字典

def save_images_for_cnn(self,image, spot_dict, folder_name ='cnn_data'):for spot in spot_dict.keys():(x1, y1, x2, y2) = spot(x1, y1, x2, y2) = (int(x1), int(y1), int(x2), int(y2))#裁剪spot_img = image[y1:y2, x1:x2]spot_img = cv2.resize(spot_img, (0,0), fx=2.0, fy=2.0) spot_id = spot_dict[spot]filename = 'spot' + str(spot_id) +'.jpg'print(spot_img.shape, filename, (x1,x2,y1,y2))cv2.imwrite(os.path.join(folder_name, filename), spot_img)

主函数

final_spot_dict 是img_process函数 return的车位坐标字典

if __name__ == '__main__':test_images = [plt.imread(path) for path in glob.glob('test_images/*.jpg')]weights_path = 'car1.h5'video_name = 'parking_video.mp4'class_dictionary = {}class_dictionary[0] = 'empty'class_dictionary[1] = 'occupied'park = Parking()    # 实例化Parking对象park.show_images(test_images)final_spot_dict = img_process(test_images,park) # 图像处理model = keras_model(weights_path)img_test(test_images,final_spot_dict,model,class_dictionary)video_test(video_name,final_spot_dict,model,class_dictionary)

其中 h5文件 是已训练好的二分类车位的权重，调用即可进行分类
关于深度学习的知识就不赘述了

附：完整代码

park.py

from __future__ import division
import matplotlib.pyplot as plt
import cv2
import os, glob
import numpy as np
from PIL import Image
from keras.applications.imagenet_utils import preprocess_input
from keras.models import load_model
from keras.preprocessing import image
from Parking import Parking
import pickle
cwd = os.getcwd()def img_process(test_images,park):white_yellow_images = list(map(park.select_rgb_white_yellow, test_images))park.show_images(white_yellow_images)gray_images = list(map(park.convert_gray_scale, white_yellow_images))park.show_images(gray_images)edge_images = list(map(lambda image: park.detect_edges(image), gray_images))park.show_images(edge_images)roi_images = list(map(park.select_region, edge_images))park.show_images(roi_images)list_of_lines = list(map(park.hough_lines, roi_images))line_images = []for image, lines in zip(test_images, list_of_lines):line_images.append(park.draw_lines(image, lines)) park.show_images(line_images)rect_images = []rect_coords = []    # 区域置空for image, lines in zip(test_images, list_of_lines):new_image, rects = park.identify_blocks(image, lines)rect_images.append(new_image)rect_coords.append(rects)park.show_images(rect_images)delineated = []spot_pos = []for image, rects in zip(test_images, rect_coords):new_image, spot_dict = park.draw_parking(image, rects)delineated.append(new_image)spot_pos.append(spot_dict)park.show_images(delineated)final_spot_dict = spot_pos[1]print(len(final_spot_dict))with open('spot_dict.pickle', 'wb') as handle:pickle.dump(final_spot_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)# park.save_images_for_cnn(test_images[0],final_spot_dict)return final_spot_dict
def keras_model(weights_path):    model = load_model(weights_path)return model
def img_test(test_images,final_spot_dict,model,class_dictionary):for i in range (len(test_images)):predicted_images = park.predict_on_image(test_images[i],final_spot_dict,model,class_dictionary)
def video_test(video_name,final_spot_dict,model,class_dictionary):name = video_namecap = cv2.VideoCapture(name)park.predict_on_video(name,final_spot_dict,model,class_dictionary,ret=True)if __name__ == '__main__':test_images = [plt.imread(path) for path in glob.glob('test_images/*.jpg')]weights_path = 'car1.h5'video_name = 'parking_video.mp4'class_dictionary = {}class_dictionary[0] = 'empty'class_dictionary[1] = 'occupied'park = Parking()    # 实例化Parking对象park.show_images(test_images)final_spot_dict = img_process(test_images,park) # 图像处理model = keras_model(weights_path)img_test(test_images,final_spot_dict,model,class_dictionary)# video_test(video_name,final_spot_dict,model,class_dictionary)123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384

Parking.py

import matplotlib.pyplot as plt
import cv2
import os, glob
import numpy as np
# 要用的函数封装在Parking中了
class Parking:def show_images(self, images, cmap=None):cols = 2rows = (len(images)+1)//colsplt.figure(figsize=(15, 12))for i, image in enumerate(images):plt.subplot(rows, cols, i+1)cmap = 'gray' if len(image.shape)==2 else cmapplt.imshow(image, cmap=cmap)plt.xticks([])plt.yticks([])plt.tight_layout(pad=0, h_pad=0, w_pad=0)plt.show()def cv_show(self,name,img):cv2.imshow(name, img)cv2.waitKey(0)cv2.destroyAllWindows()def select_rgb_white_yellow(self,image): # 过滤掉背景lower = np.uint8([120, 120, 120])upper = np.uint8([255, 255, 255])# lower_red和高于upper_red的部分分别变成0，lower_red～upper_red之间的值变成255,相当于过滤背景white_mask = cv2.inRange(image, lower, upper)self.cv_show('white_mask',white_mask)masked = cv2.bitwise_and(image, image, mask = white_mask)self.cv_show('masked',masked)return maskeddef convert_gray_scale(self,image):return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)def detect_edges(self,image, low_threshold=50, high_threshold=200):return cv2.Canny(image, low_threshold, high_threshold)def filter_region(self,image, vertices):"""剔除掉不需要的地方"""mask = np.zeros_like(image)if len(mask.shape)==2:cv2.fillPoly(mask, vertices, 255)self.cv_show('mask', mask)    return cv2.bitwise_and(image, mask)def select_region(self,image):"""手动选择区域"""# first, define the polygon by verticesrows, cols = image.shape[:2]pt_1  = [cols*0.05, rows*0.90]pt_2 = [cols*0.05, rows*0.70]pt_3 = [cols*0.30, rows*0.55]pt_4 = [cols*0.6, rows*0.15]pt_5 = [cols*0.90, rows*0.15] pt_6 = [cols*0.90, rows*0.90]vertices = np.array([[pt_1, pt_2, pt_3, pt_4, pt_5, pt_6]], dtype=np.int32) point_img = image.copy()       point_img = cv2.cvtColor(point_img, cv2.COLOR_GRAY2RGB)for point in vertices[0]:cv2.circle(point_img, (point[0],point[1]), 10, (0,0,255), 4)self.cv_show('point_img',point_img)return self.filter_region(image, vertices)def hough_lines(self,image):# 输入的图像需要是边缘检测后的结果# minLineLengh(线的最短长度，比这个短的都被忽略)和MaxLineCap（两条直线之间的最大间隔，小于此值，认为是一条直线）# rho距离精度,theta角度精度,threshod超过设定阈值才被检测出线段return cv2.HoughLinesP(image, rho=0.1, theta=np.pi/10, threshold=15, minLineLength=9, maxLineGap=4)def draw_lines(self,image, lines, color=[255, 0, 0], thickness=2, make_copy=True):# 过滤霍夫变换检测到直线if make_copy:image = np.copy(image) cleaned = []for line in lines:for x1,y1,x2,y2 in line:if abs(y2-y1) <=1 and abs(x2-x1) >=25 and abs(x2-x1) <= 55:cleaned.append((x1,y1,x2,y2))cv2.line(image, (x1, y1), (x2, y2), color, thickness)print(" No lines detected: ", len(cleaned))return imagedef identify_blocks(self,image, lines, make_copy=True):if make_copy:new_image = np.copy(image)#Step 1: 过滤部分直线cleaned = []for line in lines:for x1,y1,x2,y2 in line:if abs(y2-y1) <=1 and abs(x2-x1) >=25 and abs(x2-x1) <= 55:cleaned.append((x1,y1,x2,y2))#Step 2: 对直线按照x1进行排序import operatorlist1 = sorted(cleaned, key=operator.itemgetter(0, 1))#Step 3: 找到多个列，相当于每列是一排车clusters = {}dIndex = 0clus_dist = 10for i in range(len(list1) - 1):distance = abs(list1[i+1][0] - list1[i][0])if distance <= clus_dist:if not dIndex in clusters.keys(): clusters[dIndex] = []clusters[dIndex].append(list1[i])clusters[dIndex].append(list1[i + 1]) else:dIndex += 1#Step 4: 得到坐标rects = {}i = 0for key in clusters:all_list = clusters[key]cleaned = list(set(all_list))if len(cleaned) > 5:cleaned = sorted(cleaned, key=lambda tup: tup[1])avg_y1 = cleaned[0][1]avg_y2 = cleaned[-1][1]avg_x1 = 0avg_x2 = 0for tup in cleaned:avg_x1 += tup[0]avg_x2 += tup[2]avg_x1 = avg_x1/len(cleaned)avg_x2 = avg_x2/len(cleaned)rects[i] = (avg_x1, avg_y1, avg_x2, avg_y2)i += 1print("Num Parking Lanes: ", len(rects))#Step 5: 把列矩形画出来buff = 7for key in rects:tup_topLeft = (int(rects[key][0] - buff), int(rects[key][1]))tup_botRight = (int(rects[key][2] + buff), int(rects[key][3]))cv2.rectangle(new_image, tup_topLeft,tup_botRight,(0,255,0),3)return new_image, rectsdef draw_parking(self,image, rects, make_copy = True, color=[255, 0, 0], thickness=2, save = True):if make_copy:new_image = np.copy(image)gap = 15.5spot_dict = {} # 字典：一个车位对应一个位置tot_spots = 0# 微调adj_y1 = {0: 20, 1:-10, 2:0, 3:-11, 4:28, 5:5, 6:-15, 7:-15, 8:-10, 9:-30, 10:9, 11:-32}adj_y2 = {0: 30, 1: 50, 2:15, 3:10, 4:-15, 5:15, 6:15, 7:-20, 8:15, 9:15, 10:0, 11:30}adj_x1 = {0: -8, 1:-15, 2:-15, 3:-15, 4:-15, 5:-15, 6:-15, 7:-15, 8:-10, 9:-10, 10:-10, 11:0}adj_x2 = {0: 0, 1: 15, 2:15, 3:15, 4:15, 5:15, 6:15, 7:15, 8:10, 9:10, 10:10, 11:0}# for key in rects:tup = rects[key]x1 = int(tup[0]+ adj_x1[key])x2 = int(tup[2]+ adj_x2[key])y1 = int(tup[1] + adj_y1[key])y2 = int(tup[3] + adj_y2[key])cv2.rectangle(new_image, (x1, y1),(x2,y2),(0,255,0),2)# (y2-y1)//gap表示能停多少辆车num_splits = int(abs(y2-y1)//gap)for i in range(0, num_splits+1):y = int(y1 + i*gap)cv2.line(new_image, (x1, y), (x2, y), color, thickness)if key > 0 and key < len(rects) -1 :        #竖直线x = int((x1 + x2)/2)cv2.line(new_image, (x, y1), (x, y2), color, thickness)# 计算数量if key == 0 or key == (len(rects) -1):tot_spots += num_splits +1else:tot_spots += 2*(num_splits +1)  # 双排的乘2# 字典对应好if key == 0 or key == (len(rects) -1):for i in range(0, num_splits+1):cur_len = len(spot_dict)y = int(y1 + i*gap)spot_dict[(x1, y, x2, y+gap)] = cur_len +1        else:for i in range(0, num_splits+1):cur_len = len(spot_dict)y = int(y1 + i*gap)x = int((x1 + x2)/2)spot_dict[(x1, y, x, y+gap)] = cur_len +1spot_dict[(x, y, x2, y+gap)] = cur_len +2   print("total parking spaces: ", tot_spots, cur_len)if save:filename = 'with_parking.jpg'cv2.imwrite(filename, new_image)return new_image, spot_dictdef assign_spots_map(self,image, spot_dict, make_copy = True, color=[255, 0, 0], thickness=2):if make_copy:new_image = np.copy(image)for spot in spot_dict.keys():(x1, y1, x2, y2) = spotcv2.rectangle(new_image, (int(x1),int(y1)), (int(x2),int(y2)), color, thickness)return new_imagedef save_images_for_cnn(self,image, spot_dict, folder_name ='cnn_data'):for spot in spot_dict.keys():(x1, y1, x2, y2) = spot(x1, y1, x2, y2) = (int(x1), int(y1), int(x2), int(y2))#裁剪spot_img = image[y1:y2, x1:x2]spot_img = cv2.resize(spot_img, (0,0), fx=2.0, fy=2.0) spot_id = spot_dict[spot]filename = 'spot' + str(spot_id) +'.jpg'print(spot_img.shape, filename, (x1,x2,y1,y2))cv2.imwrite(os.path.join(folder_name, filename), spot_img)def make_prediction(self,image,model,class_dictionary):#预处理img = image/255.#转换成4D tensorimage = np.expand_dims(img, axis=0)# 用训练好的模型进行训练class_predicted = model.predict(image)inID = np.argmax(class_predicted[0])label = class_dictionary[inID]return labeldef predict_on_image(self,image, spot_dict , model,class_dictionary,make_copy=True, color = [0, 255, 0], alpha=0.5):if make_copy:new_image = np.copy(image)overlay = np.copy(image)self.cv_show('new_image',new_image)cnt_empty = 0all_spots = 0for spot in spot_dict.keys():all_spots += 1(x1, y1, x2, y2) = spot(x1, y1, x2, y2) = (int(x1), int(y1), int(x2), int(y2))spot_img = image[y1:y2, x1:x2]spot_img = cv2.resize(spot_img, (48, 48)) label = self.make_prediction(spot_img,model,class_dictionary)if label == 'empty':cv2.rectangle(overlay, (int(x1),int(y1)), (int(x2),int(y2)), color, -1)cnt_empty += 1cv2.addWeighted(overlay, alpha, new_image, 1 - alpha, 0, new_image)cv2.putText(new_image, "Available: %d spots" %cnt_empty, (30, 95),cv2.FONT_HERSHEY_SIMPLEX,0.7, (255, 255, 255), 2)cv2.putText(new_image, "Total: %d spots" %all_spots, (30, 125),cv2.FONT_HERSHEY_SIMPLEX,0.7, (255, 255, 255), 2)save = Falseif save:filename = 'with_marking.jpg'cv2.imwrite(filename, new_image)self.cv_show('new_image',new_image)return new_imagedef predict_on_video(self,video_name,final_spot_dict, model,class_dictionary,ret=True):   cap = cv2.VideoCapture(video_name)count = 0while ret:ret, image = cap.read()count += 1if count == 5:count = 0new_image = np.copy(image)overlay = np.copy(image)cnt_empty = 0all_spots = 0color = [0, 255, 0] alpha=0.5for spot in final_spot_dict.keys():all_spots += 1(x1, y1, x2, y2) = spot(x1, y1, x2, y2) = (int(x1), int(y1), int(x2), int(y2))spot_img = image[y1:y2, x1:x2]spot_img = cv2.resize(spot_img, (48,48)) label = self.make_prediction(spot_img,model,class_dictionary)if label == 'empty':cv2.rectangle(overlay, (int(x1),int(y1)), (int(x2),int(y2)), color, -1)cnt_empty += 1cv2.addWeighted(overlay, alpha, new_image, 1 - alpha, 0, new_image)cv2.putText(new_image, "Available: %d spots" %cnt_empty, (30, 95),cv2.FONT_HERSHEY_SIMPLEX,0.7, (255, 255, 255), 2)cv2.putText(new_image, "Total: %d spots" %all_spots, (30, 125),cv2.FONT_HERSHEY_SIMPLEX,0.7, (255, 255, 255), 2)cv2.imshow('frame', new_image)if cv2.waitKey(10) & 0xFF == ord('q'):breakcv2.destroyAllWindows()cap.release()

Open cv还是很强的，源码获取记得加下交流群哦：1136192749

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