OpenCV计算机视觉实战（Python）| 14、项目实战：停车场车位识别

简介

本节为《OpenCV计算机视觉实战（Python）》版第14讲，项目实战：停车场车位识别，的总结。

总结

1、项目介绍

统计：

1. 有多少停车位
2. 哪些个停车位被占据，哪些个停车位没有被占据
   

2、步骤

1. 选取区域：人工选择停车场所在的位置，只保存停车场所在的区域图像
2. 预处理：灰度、边缘、霍夫
3. 微调：针对实际的项目，由于背景是没变的，当结果不正确时，可以人为给定数据对结果进行微调
4. 分类：以簇的形式，将每一列的停车场数据保存，保存每一列矩阵的坐标点
5. 分割：以实际项目选取合适的值，将每一列分割成一个个小块，每一个小块代表一个停车场的位置
6. 深度训练：以空停车位做分类训练，判断停车场某个停车位上有没有车；

3、程序

Parking.py:

class Parking:def cv_show(name, image):cv2.imshow(name, image)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 = img.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):#阈值越大，直线越少return cv2.HoughLinesP(image, rho=0.1, theta=np.pi/10, threshold=15, minLineLength=0, maxLineGap=4)def draw_lines(self, image, lines, color=[255, 0, 0], thickness=2, make_cope=True):# 过滤霍夫变换坚持到到直线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)# step1: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))# Step2:对直线按照x1进行排序import operatorlist1 = sorted(cleaned, key=operator.itemgetter(0,1))# Step3: 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# Step4: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))# Step5:得到坐标rects = {}i = 0for key in clusters:all_list=cluseters[kye]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))# Step5: 把列矩形给画出来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 = {}adj_x2 = {}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)num_splits = int(abs(y2-y1)//gap)for in 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)/2cv.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)# 字典对应好if key ==9 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 + 1else: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 + 2print('total parking spaces:', tot_spots, cur_len)if save:filename = 'with_parking.jpg'cv2.imwrite(filename, new_image)return new_image, splt_dictdef 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)

train.py:（神经网络训练文件，此处略去）

import numpy as np
...

main.py:（主程序）

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 image
from keras.preprocessing import image
from Parking import Parking
import picklecwd = 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(lien_images)rect_images = []rect_coords = []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, class_dictionary)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[0] = 'empty'class_dictionary[1] = 'occupied'park = Parking()park.show_images(test_images)final_spot_dict = img_process(test_images, park)# 本文所附录出的程序代码仅到img_process处，往下的这些代码没有给出，感兴趣的可以自行看课程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)