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# coding=utf-8
import matplotlib.pyplot as plt
import cv2
import os, glob
import numpy as npclass Parking:# 显示图片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_yello(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_sacle(self,image):return cv2.cvtColor(image,cv2.COLOR_RGB2GRAY)# 检测边缘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)# 手动选择区域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_line(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):# 过滤霍夫变换检测得到直线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))# >>> b=operator.itemgetter(1,0)   //定义函数b，获取对象的第1个域和第0个的值# >>> b(a) # (2, 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))#裁剪图像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.key():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) # 图像融合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()

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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_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()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)

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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])