露天停车场车位识别

凯哥英语视频

1.现有资源梳理

多张停车位俯瞰照片

操作环境 win10-64位
代码语言 Python 3.6
运行环境 TensorFlow 2.1.0 + Keras 2.3.1 + h5py 2.8.0
(一开始h5py版本不对 , 各种报错)

2.实现方案规划

图片预处理

1. 保留主体背景过滤
2. 灰度转换
3. 去除噪点
4. 边缘检测

裁剪出需要处理的部分

1. 选取图片中前5个大轮廓
2. 裁剪出需要处理的部分

识别车位位置

1. 霍夫变换检测直线
2. 过滤不是车位的直线
3. 根据直线的(x,y)分列排序(相同的x为一列)
4. 根据x1,x2的平均值和y_max,y_min画出列的矩形
5. 处理多张图片,取最优作为位置参数
6. 裁剪出车位,作为训练数据集

识别车位空缺

1. 利用训练好的模型对裁剪的车位图片进行分类

3.代码实现

train.py

import numpy
import os
from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping
from keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.initializers import TruncatedNormal
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dropout
from keras.layers.core import Densefiles_train = 0
files_validation = 0cwd = os.getcwd()
folder = 'train_data/train'
for sub_folder in os.listdir(folder):path, dirs, files = next(os.walk(os.path.join(folder,sub_folder)))files_train += len(files)folder = 'train_data/test'
for sub_folder in os.listdir(folder):path, dirs, files = next(os.walk(os.path.join(folder,sub_folder)))files_validation += len(files)print(files_train,files_validation)img_width, img_height = 48, 48
train_data_dir = "train_data/train"
validation_data_dir = "train_data/test"
nb_train_samples = files_train
nb_validation_samples = files_validation
batch_size = 32
epochs = 15
num_classes = 2model = applications.VGG16(weights='imagenet', include_top=False, input_shape = (img_width, img_height, 3))for layer in model.layers[:10]:layer.trainable = Falsex = model.output
x = Flatten()(x)
predictions = Dense(num_classes, activation="softmax")(x)model_final = Model(input = model.input, output = predictions)model_final.compile(loss = "categorical_crossentropy", optimizer = optimizers.SGD(lr=0.0001, momentum=0.9), metrics=["accuracy"]) train_datagen = ImageDataGenerator(
rescale = 1./255,
horizontal_flip = True,
fill_mode = "nearest",
zoom_range = 0.1,
width_shift_range = 0.1,
height_shift_range=0.1,
rotation_range=5)test_datagen = ImageDataGenerator(
rescale = 1./255,
horizontal_flip = True,
fill_mode = "nearest",
zoom_range = 0.1,
width_shift_range = 0.1,
height_shift_range=0.1,
rotation_range=5)train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size = (img_height, img_width),
batch_size = batch_size,
class_mode = "categorical")validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size = (img_height, img_width),
class_mode = "categorical")checkpoint = ModelCheckpoint("car1.h5", monitor='val_acc', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)
early = EarlyStopping(monitor='val_acc', min_delta=0, patience=10, verbose=1, mode='auto')history_object = model_final.fit_generator(
train_generator,
samples_per_epoch = nb_train_samples,
epochs = epochs,
validation_data = validation_generator,
nb_val_samples = nb_validation_samples,
callbacks = [checkpoint, early])

park_test.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):# map函数 向某个函数中 执行某个数据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 = []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__':# 测试图片test_images = [plt.imread(path) for path in glob.glob('test_images/*.jpg')]# 训练好的模型weights_path = 'car1.h5'# 生成的视频video_name = 'parking_video.mp4'# 0-1转换class_dictionary = {}class_dictionary[0] = 'empty'class_dictionary[1] = 'occupied'# 类的调用park = Parking()park.show_images(test_images)final_spot_dict = img_process(test_images,park)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)

parking.py

import matplotlib.pyplot as plt
import cv2
import os, glob
import numpy as npclass Parking:  # parking 类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()# 预处理部分可以优化def select_rgb_white_yellow(self,image): #过滤掉背景lower = np.uint8([120, 120, 120])upper = np.uint8([255, 255, 255])# 低于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,相当于保留主体过滤背景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):# 灰度处理return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)def detect_edges(self,image, low_threshold=50, high_threshold=200):# 边缘检测--设置好最小阈值和最大阈值return cv2.Canny(image, low_threshold, high_threshold)def filter_region(self,image, vertices):"""结合坐标所生成的图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):"""手动选择停车位整体区域主观判断多边形点的比例进行裁剪"""# 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)# HoughLinesP中的P,加快速度def draw_lines(self,image, lines, color=[255, 0, 0], thickness=2, make_copy=True):# 过滤在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:  # 去除斜线 去除很长的线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)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)# 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()

所有代码连在一起就是完整的代码

项目打包链接

项目有点大,我就放在百度云了
项目打包下载
提取密码 520y

结语

帮我的小狐狸点个赞吧
别的也没啥说的 , 如果觉得可以 , 希望一键三连支持一下 !

ok，那就这样吧~

欢迎各位大佬留言吐槽,也可以深入交流~