第一步：去除背景
第二步：进行灰度化
第三步：使用cv2.canny进行边缘检测
第四步：进行图像区域的选择
第五步：使用霍夫曼进行直线检测
第六步：对删选出的直线进行画图操作
第七步：找出每一列车的x1, y1, x2, y2
第八步：根据gap间隔，找出每一列车所在的(x1, y1, x2, y2)
第九步：使用keras获得的car1.h5权重参数，使用model.predict进行预测操作， 对图片进行预测， 进行画图操作
第十步：对视频进行预测
主函数：part_test
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): # 第一步：去除背景 white_yellow_images = list(map(park.select_rgb_white_yellow, test_images)) park.show_images(white_yellow_images) #第二步：进行灰度化 gray_images = list(map(park.convert_gray_scale, white_yellow_images)) park.show_images(gray_images) # 第三步：进行边缘检测 edge_images = list(map(lambda image: park.detect_edges(image), gray_images)) park.show_images(edge_images) # 第四步：筛选图像区域 roi_images = list(map(park.select_region, edge_images)) park.show_images(roi_images) # 第五步：使用hough检测图像中的直线信息 list_of_lines = list(map(park.hough_lines, roi_images)) # 第六步：对直线进行筛选并进行画图操作 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) # 第七步:找出每一列车的(x1, y1, x2, y2) 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) # 找出每一列框中每一列车对应的位置 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)) # 将图片的位置信息进行储存 with open('spot_dict.pickle', 'wb') as handle: pickle.dump(final_spot_dict, handle, protocol=pickle.HIGHEST_PROTOCOL) # 将图片进行保存 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_name cap = 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() park.show_images(test_images) final_spot_dict = img_process(test_images,park) # 使用权重参数构建模型 model = keras_model(weights_path) # 第九步：使用model进行模型的预测 img_test(test_images,final_spot_dict,model,class_dictionary) # 第十步:对视频进行预测 video_test(video_name,final_spot_dict,model,class_dictionary)
调用函数
import matplotlib.pyplot as plt import cv2 import os, glob import numpy as np class Parking: def show_images(self, images, cmap=None): cols = 2 rows = (len(images)+1)//cols plt.figure(figsize=(15, 12)) for i, image in enumerate(images): plt.subplot(rows, cols, i+1) cmap = 'gray' if len(image.shape)==2 else cmap plt.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 masked 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) 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 vertices rows, 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 image 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 operator list1 = 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 = 0 clus_dist = 10 for 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 = 0 for 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 = 0 avg_x2 = 0 for 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 += 1 print("Num Parking Lanes: ", len(rects)) #Step 5: 把列矩形画出来 buff = 7 for 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 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 = 15.5 spot_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) 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 self.cv_show('new_image', new_image) if key == 0 or key == (len(rects) -1): tot_spots += num_splits +1 else: tot_spots += 2*(num_splits +1) # 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 +1 spot_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 def 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) = spot cv2.rectangle(new_image, (int(x1),int(y1)), (int(x2),int(y2)), color, thickness) return new_image 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) def make_prediction(self,image,model,class_dictionary): #预处理 img = image/255. #转换成4D tensor image = 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 label def 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 = 0 all_spots = 0 for 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 += 1 cv2.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 = False if save: filename = 'with_marking.jpg' cv2.imwrite(filename, new_image) self.cv_show('new_image',new_image) return new_image def predict_on_video(self,video_name,final_spot_dict, model,class_dictionary,ret=True): cap = cv2.VideoCapture(video_name) count = 0 while ret: ret, image = cap.read() count += 1 if count == 5: count = 0 new_image = np.copy(image) overlay = np.copy(image) cnt_empty = 0 all_spots = 0 color = [0, 255, 0] alpha=0.5 for 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 += 1 cv2.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'): break cv2.destroyAllWindows() cap.release()
