将会进行如下操作：
-
定义图像特征提取方
-
读入车辆和非车辆图像数据
-
数据预处理，shuffle，标准化
-
训练模型，使用的是svm
-
测试模型
-
使用滑动窗口和多尺度的图像大小在图像上检测车辆
- 滑动窗口
- 多尺度图像
- heatmap
- label
- box center
-
检测视频中行驶的车辆
总结：车辆检测主要是两方面的内容，1:物体识别 2:滑动窗口
-
汽车和非汽车图片展示
import glob import matplotlib.pyplot as plt vehicle_lable = 1 non_vehicle_label = 0 # 读入数据 vehicles_file_paths = glob.glob("./vehicles/*/*.png") non_vehicles_file_paths = glob.glob("./non-vehicles/*/*.png") print("汽车样本数量：" , len(vehicles_file_paths)) print("非汽车样本数量：" , len(non_vehicles_file_paths)) # 显示汽车和非汽车图片 vehicle_img = mpimg.imread(vehicles_file_paths[0]) non_vehicle_img = mpimg.imread(non_vehicles_file_paths[0]) fig, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(8, 20)) ax1.imshow(vehicle_img) ax1.set_title("vehicle") ax2.imshow(non_vehicle_img) ax2.set_title("non_vehicle") plt.show()
-
提取HOG特征
hog参数说明：
orientations：每个cell直方图的方向的个数
pix_per_cell：每个cell像素个数
cells_per_block：每个block的cell个数
import numpy as np import cv2 import matplotlib.image as mpimg def convert_color(img, conv): """ 颜色空间转换 """ return cv2.cvtColor(img, conv) def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True): """ 提取图像的hog特征 """ if vis == True: features, hog_image = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell), cells_per_block=(cell_per_block, cell_per_block), block_norm= 'L2-Hys', transform_sqrt=False, visualise=vis, feature_vector=feature_vec) return features, hog_image else: features = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell), cells_per_block=(cell_per_block, cell_per_block), block_norm= 'L2-Hys', transform_sqrt=False, visualise=vis, feature_vector=feature_vec) return features # 测试提取HOG特征 vehicle_img = mpimg.imread(vehicles_file_paths[0]) non_vehicle_img = mpimg.imread(non_vehicles_file_paths[0]) v_hog_feats,v_hog_imgs = get_hog_features(vehicle_img[:, :, 0], 9, 8, 2, vis=True, feature_vec=False) n_hog_feats,n_hog_imgs = get_hog_features(non_vehicle_img[:, :, 0], 9, 8, 2, vis=True, feature_vec=False) # 显示汽车和非汽车图片 vehicle_img = mpimg.imread(vehicles_file_paths[0]) non_vehicle_img = mpimg.imread(non_vehicles_file_paths[0]) fig, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(8, 20)) ax1.imshow(v_hog_imgs, cmap="gray") ax1.set_title("vehicle") ax2.imshow(n_hog_imgs, cmap="gray") ax2.set_title("non_vehicle") plt.show()
-
提取空间特征和颜色直方图特征，且把之上的特征进行合并。
def bin_spatial(img, size=(32, 32)): """ 提取图像的空间特征 """ color1 = cv2.resize(img[:,:,0], size).ravel() color2 = cv2.resize(img[:,:,1], size).ravel() color3 = cv2.resize(img[:,:,2], size).ravel() return np.hstack((color1, color2, color3)) def color_hist(img, nbins=32): """ 颜色直方图 """ channel1_hist = np.histogram(img[:,:,0], bins=nbins) channel2_hist = np.histogram(img[:,:,1], bins=nbins) channel3_hist = np.histogram(img[:,:,2], bins=nbins) hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0])) return hist_features def extract_img_features(img): """ 把之上的图像处理方法进行综合，且对结果进行归一化处理 """ img = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb) hist_feature = color_hist(img) spatial_feature = bin_spatial(img) ch1 = img[:, :, 0] ch2 = img[:, :, 1] ch3 = img[:, :, 2] ch1_hog_feature = get_hog_features(ch1, 9, 8, 2) ch2_hog_feature = get_hog_features(ch2, 9, 8, 2) ch3_hog_feature = get_hog_features(ch3, 9, 8, 2) hog_features = np.hstack((ch1_hog_feature, ch2_hog_feature, ch3_hog_feature)) features = np.concatenate((hist_feature, spatial_feature, hog_features)) return features
-
根据上边的图像特征提取方法，对所有的数据进行特征提取
X = [] y = [] for path in vehicles_file_paths: img = mpimg.imread(path) features = extract_img_features(img) X.append(features) y.append(vehicle_lable) for path in non_vehicles_file_paths: img = mpimg.imread(path) features = extract_img_features(img) X.append(features) y.append(non_vehicle_label)
-
数据预处理
shuffle —> train_test_split—>StandardScaler
注意：数据标准化的时候，使用训练数据作为fit的对象。
from skimage.feature import hog from sklearn.model_selection import train_test_split from sklearn.utils import shuffle from sklearn.preprocessing import StandardScaler # shuffle X, y = shuffle(X, y) # split rand_state = np.random.randint(0, 100) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=rand_state) # scaller X_scaler = StandardScaler().fit(X_train) X_train = X_scaler.transform(X_train) X_test = X_scaler.transform(X_test)
-
训练模型
from sklearn.svm import LinearSVC svm = LinearSVC() svm.fit(X_train, y_train)
-
测试模型
最后模型的准确率为：0.986204954955
from sklearn.metrics import accuracy_score predicts = svm.predict(X_test) score = accuracy_score(predicts, y_test) print("在测试数据上的准确率为：" + score)
-
从图片中检测汽车
滑动窗口技术是关键
因为不知道检测物体的大小，远的地方车辆看起来小，近的地方看起来大。为了得到合适的检测数据，需要图片进行适当的缩放。但是这样带来的后果是，计算量会急剧增加。可以采查找车辆只在图像的下半部分，因为上半部分是天空。为了提高效率，先对整幅图像进行HOG特征提取，然后再根据需要进行特征分割。
def find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins): """ 检测当前图像中的所有可能的汽车位置 """ boxs = [] draw_img = np.copy(img) img = img.astype(np.float32)/255 img_tosearch = img[ystart:ystop,:,:] ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YCrCb) if scale != 1: imshape = ctrans_tosearch.shape ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale))) ch1 = ctrans_tosearch[:,:,0] ch2 = ctrans_tosearch[:,:,1] ch3 = ctrans_tosearch[:,:,2] # Define blocks and steps as above nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1 nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1 nfeat_per_block = orient*cell_per_block**2 # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell window = 64 nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1 cells_per_step = 2 # Instead of overlap, define how many cells to step nxsteps = (nxblocks - nblocks_per_window) // cells_per_step + 1 nysteps = (nyblocks - nblocks_per_window) // cells_per_step + 1 # Compute individual channel HOG features for the entire image hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False) hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False) hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False) for xb in range(nxsteps): for yb in range(nysteps): ypos = yb*cells_per_step xpos = xb*cells_per_step # Extract HOG for this patch hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3)) xleft = xpos*pix_per_cell ytop = ypos*pix_per_cell # Extract the image patch subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64)) # Get color features spatial_features = bin_spatial(subimg, size=spatial_size) hist_features = color_hist(subimg, nbins=hist_bins) # Scale features and make a prediction test_features = X_scaler.transform(np.hstack((hist_features, spatial_features, hog_features)).reshape(1, -1)) #test_features = X_scaler.transform(np.hstack((shape_feat, hist_feat)).reshape(1, -1)) test_prediction = svc.predict(test_features) if test_prediction == 1: xbox_left = np.int(xleft*scale) ytop_draw = np.int(ytop*scale) win_draw = np.int(window*scale) boxs.append([(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart)]) return boxs img = mpimg.imread("test_images/test1.jpg") boxs = find_cars(img, 400, 600, 1, svm, X_scaler, 9, 8, 2, (32, 32), 32) for box in boxs: cv2.rectangle(img, box[0], box[1], (0, 0, 255), 6) plt.imshow(img) plt.show()
-
使用heatmap去掉错误检测的数据
错误检测的数据出现的频率不高，根据频率去掉低频率的数据点，保留高频率的数据点。
# 创建热度图 heatmap = np.zeros_like(img[:, :, :]).astype(np.float) for box in boxs: heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0], 0] += 1 fig,(ax1, ax2) = plt.subplots(1, 2, figsize=(15, 20)) ax1.imshow(heatmap) ax1.set_title("heatmap") heatmap[heatmap < 3] = 0 ax2.imshow(heatmap) ax2.set_title("heatmap_filted") plt.show()
-
使用label把检测到的区域外接矩形框。
from scipy.ndimage.measurements import label labels = label(heatmap) for car_number in range(1, labels[1] + 1): nonzero = (labels[0] == car_number).nonzero() nonzeroy = np.array(nonzero[0]) nonzerox = np.array(nonzero[1]) bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy))) cv2.rectangle(img, bbox[0], bbox[1], (0, 0, 255), 6) fig,ax1 = plt.subplots(1, 1, figsize=(7, 12)) ax1.imshow(img) plt.show()
-
设置中心点检测判断
每检测完一幅图像，记录一下在该图像中检测到的矩形框的中心点。下次检测到的矩形框判断是否有中心点在其中。这样可以减少错误的判断。
box_center = [] def detect_object(img): bbox_list = [] # 分别对各个尺度的窗口查找汽车的位置 boxs = find_cars(img, 400, 656, 1.2, svm, X_scaler, 9, 8, 2, (32, 32), 32) bbox_list = bbox_list + boxs boxs = find_cars(img, 400, 600, 1, svm, X_scaler, 9, 8, 2, (32, 32), 32) bbox_list = bbox_list + boxs boxs = find_cars(img, 400, 500, 0.8, svm, X_scaler, 9, 8, 2, (32, 32), 32) bbox_list = bbox_list + boxs # 创建热度图 heatmap = np.zeros_like(img[:, :, 0]).astype(np.float) for box in bbox_list: heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1 # 排除掉出现频率小的汽车检测位置 heatmap[heatmap <=8] = 0 # 对汽车的位置进行标记，画矩形 labels = label(heatmap) for car_number in range(1, labels[1] + 1): nonzero = (labels[0] == car_number).nonzero() nonzeroy = np.array(nonzero[0]) nonzerox = np.array(nonzero[1]) bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy))) if len(box_center) > 0: for center in box_center: if bbox[0][0] < center[0] < bbox[1][0] and bbox[0][1] < center[1] < bbox[1][1]: cv2.rectangle(img, bbox[0], bbox[1], (0, 0, 255), 6) break box_center.append(((bbox[0][0]+bbox[1][0] )/2, (bbox[0][1] + bbox[1][1])/2)) return img
-
处理视频中的车辆检测
from moviepy.editor import VideoFileClip clip1 = VideoFileClip("project_video.mp4") white_clip = clip1.fl_image(detect_object) #white_clip.write_images_sequence("test_challenge/frame%2d.jpg") white_clip.write_videofile("project_video_output.mp4", audio=False)
-
展示，视频地址 点这里
from IPython.display import HTML HTML(""" <video width="960" height="540" controls> <source src="{0}"> </video> """.format("project_video_output.mp4"))
