车位检测

• 安装依赖

pip install keras==2.0.6
pip install tensorflow

• 安装tensorflow报错

Found existing installation: wrapt 1.10.11
ERROR: Cannot uninstall 'wrapt'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.

• 解决方案

pip install wrapt --ignore-installed
pip install tensorflow

• 报错1

Ignore above cudart dlerror if you do not have a GPU set up on your machine.
Traceback (most recent call last):
  File "C:\Users\ychen\Notebook\park\train.py", line 11, in <module>
    from keras.layers.normalization import BatchNormalization
ImportError: cannot import name 'BatchNormalization'

• 解决方案

# from keras.layers.normalization import BatchNormalization
from keras.layers.normalization.batch_normalization_v1 import BatchNormalization

• 报错2

    model = applications.VGG16(weights='imagenet', include_top=False, input_shape = (img_width, img_height, 3))
AttributeError: module 'keras.applications' has no attribute 'VGG16'

• 解决方案

# from keras import applications
from tensorflow.keras import applications

• 报错3

TypeError: ('Keyword argument not understood:', 'input')

• 解决方案

# model_final = Model(input = model.input, output = predictions)
model_final = Model(inputs = model.input, outputs = predictions)

• 报错4

AttributeError: module 'keras.optimizers' has no attribute 'SGD'

• 解决方案

# optimizer = optimizers.SGD(lr=0.0001, momentum=0.9), 
optimizer = optimizers.gradient_descent_v2.SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True),

• 报错5

TypeError: fit_generator() got an unexpected keyword argument 'samples_per_epoch'

• 识别模型构建

import numpy
import os
# from keras import applications
from tensorflow.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.normalization.batch_normalization_v1 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 Dense
 
files_train = 0
files_validation = 0
 
cwd = 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 = 2
 
model = applications.VGG16(weights='imagenet', include_top=False, input_shape = (img_width, img_height, 3))
 
for layer in model.layers[:10]:
    layer.trainable = False
 
x = 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])

• 执行后生成car1.h5

• 识别代码

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 = []
    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_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)
    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)

• 封装方法

点击查看代码

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
            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()