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北京建设协会网站,网站建设 律师,沈阳网下载,wordpress 数据字典一、项目简介停车场车位识别是计算机视觉在智能交通领域的典型应用#xff0c;核心目标是基于监控视频或图像#xff0c;自动检测车位位置、判断车位占用状态#xff08;空 / 占用#xff09;#xff0c;并实时统计空闲车位数量。该项目可解决传统停车场找车位效率低、交通…一、项目简介停车场车位识别是计算机视觉在智能交通领域的典型应用核心目标是基于监控视频或图像自动检测车位位置、判断车位占用状态空 / 占用并实时统计空闲车位数量。该项目可解决传统停车场找车位效率低、交通拥堵等问题为智能停车引导系统提供核心技术支撑。项目整体流程无需依赖预设车位坐标完全基于原始图像 / 视频实现 “车位区域自动划分 占用状态智能判断”融合了 OpenCV 图像处理、霍夫直线检测、深度学习分类等关键技术兼具实用性和学习价值。二、核心原理项目核心逻辑可拆解为 “车位区域定位” 和 “占用状态识别” 两大模块流程环环相扣从图像预处理到模型预测形成完整闭环一模块 1车位区域自动定位核心目标从复杂的停车场图像中筛选出有效的停车区域精准划分每个车位的坐标范围无需人工标注。实现步骤图像预处理与背景过滤颜色阈值过滤使用cv2.inRange()函数设定颜色阈值保留图像中车位线等关键区域过滤无关背景如建筑物、车道。灰度化与边缘检测将彩色图转为灰度图通过 Canny 边缘检测算法提取图像边缘突出车位线的轮廓特征。区域筛选手动指定停车场核心区域的顶点坐标通过cv2.fillPoly()生成掩码mask仅保留核心区域的边缘信息进一步剔除干扰。霍夫直线检测与筛选霍夫变换Hough Line Transform对边缘检测后的图像执行概率霍夫直线检测cv2.HoughLinesP()检测所有直线即车位线。直线过滤根据车位线的特征筛选有效直线 —— 过滤倾斜角度过大的斜线、长度过短 / 过长的非车位线保留水平方向的有效车位分隔线。按列聚类与区域划分直线聚类排序将筛选后的直线按 X 坐标聚类同一列的车位线 X 坐标差异小区分出停车场的不同列。矩形框绘制对每一列计算直线的最大 / 最小 Y 坐标绘制出该列的矩形范围确定每列的纵向边界。车位切分根据车位间距gap 值经验值 15.5 像素在每列矩形内横向切分得到单个车位的坐标X1,Y1,X2,Y2并为每个车位分配唯一编号。二模块 2车位占用状态识别核心目标基于划分好的车位坐标通过深度学习模型判断每个车位是 “空闲” 还是 “被占用”。实现步骤车位图像数据采集按车位坐标裁剪图像从原始图像中根据每个车位的坐标裁剪出对应的局部图像。数据分类与预处理将裁剪后的图像分为 “空闲车位” 和 “占用车位” 两类缩放为 48×48 像素适配模型输入并划分训练集和测试集。分类模型构建与训练模型选择基于 Keras 框架采用迁移学习使用预训练的 VGG16 模型减少训练数据量需求提升训练效率。模型微调冻结 VGG16 的前 10 层添加自定义全连接层输出二分类结果0 空闲1 占用。数据增强使用图像翻转、缩放、亮度调整等数据增强技术提升模型泛化能力。训练参数输入图像尺寸 48×48优化器选择 Adam损失函数为交叉熵训练轮次epoch设为 15最终验证准确率可达 90% 以上。实时预测与结果可视化图像预处理测试时对裁剪后的车位图像执行与训练时一致的预处理缩放、归一化并扩展为 4D 张量适配 TensorFlow 后端。模型预测通过训练好的模型输出预测概率选择概率较大的类别作为车位状态。结果标注在原始图像中用不同颜色框标注车位状态如绿色 空闲红色 占用并实时统计空闲 / 占用车位数量。三、代码实战原始数据阈值改变roi判定手动指定过滤并剔除后的图片指定mask结果需要把图中的直线利用起来找直线——霍夫变换直线检测image最好是经过边缘检测之后的结果。y2 - y1 表示检测直线的倾斜程度由于车的干扰我们需要进一步做的好一些后续再说接下来对每一列排序使用簇判断距离画出停车位训练过程有空和占据两种状态具体看train.py文件用模型推理预测parking.pyimport matplotlib.pyplot as plt import cv2 import os, glob import numpy as np class Parking: def show_images(self, images, cmapNone): cols 2 rows (len(images)1)//cols plt.figure(figsize(15, 12)) for i, image in enumerate(images): plt.subplot(rows, cols, i1) cmap gray if len(image.shape)2 else cmap plt.imshow(image, cmapcmap) plt.xticks([]) plt.yticks([]) plt.tight_layout(pad0, h_pad0, w_pad0) 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0lower_redupper_red之间的值变成255,相当于过滤背景 white_mask cv2.inRange(image, lower, upper) # 只保留 0 和 255 也就是全黑和全白了 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_threshold50, high_threshold200): 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]], dtypenp.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, rho0.1, thetanp.pi/10, threshold15, minLineLength9, maxLineGap4) def draw_lines(self,image, lines, color[255, 0, 0], thickness2, make_copyTrue): # 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_copyTrue): 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, keyoperator.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[i1][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, keylambda 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], thickness2, 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_splits1): 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_splits1): cur_len len(spot_dict) y int(y1 i*gap) spot_dict[(x1, y, x2, ygap)] cur_len 1 else: for i in range(0, num_splits1): cur_len len(spot_dict) y int(y1 i*gap) x int((x1 x2)/2) spot_dict[(x1, y, x, ygap)] cur_len 1 spot_dict[(x, y, x2, ygap)] 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], thickness2): 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), fx2.0, fy2.0) 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, axis0) # 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_copyTrue, color [0, 255, 0], alpha0.5): 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,retTrue): 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] alpha0.5 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()推理代码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, protocolpickle.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,retTrue) 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)训练文件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 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(weightsimagenet, include_topFalse, 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, activationsoftmax)(x) model_final Model(input model.input, output predictions) model_final.compile(loss categorical_crossentropy, optimizer optimizers.SGD(lr0.0001, momentum0.9), 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_range0.1, rotation_range5) test_datagen ImageDataGenerator( rescale 1./255, horizontal_flip True, fill_mode nearest, zoom_range 0.1, width_shift_range 0.1, height_shift_range0.1, rotation_range5) 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, monitorval_acc, verbose1, save_best_onlyTrue, save_weights_onlyFalse, modeauto, period1) early EarlyStopping(monitorval_acc, min_delta0, patience10, verbose1, modeauto) 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])