实现步骤：
- 分析训练数据，提取图片HOG特征。
- 训练分类器
- 应用滑动窗口实现车辆检测
- 应用热力图(heatMap)过滤错误检测(false positive)
训练数据为64x64x3的RBG图片，包含车辆与非车辆图片两类，车辆图片8792张，非车辆图片8968张。
以下为车辆，非车辆图片样例：
提取HOG特征，以下为实现方法：
# Define a function to return HOG features and visualization
def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):
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), transform_sqrt=False,
visualise=True, feature_vector=False)
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), transform_sqrt=False,
visualise=False, feature_vector=feature_vec)
return features
以下为原图与提取的HOG特征图对比：
这里使用SVM分类器，以下为代码：
t = time.time()
car_features = utils.extract_features(cars, cspace=colorspace, orient=orient,
pix_per_cell=pix_per_cell, cell_per_block=cell_per_block,
hog_channel=hog_channel)
notcar_features = utils.extract_features(notcars, cspace=colorspace, orient=orient,
pix_per_cell=pix_per_cell, cell_per_block=cell_per_block,
hog_channel=hog_channel)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to extract features...')
# Create an array stack of feature vectors
X = np.vstack((car_features, notcar_features))
X = X.astype(np.float64)
# Fit a per-column scaler
# X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
# scaled_X = X_scaler.transform(X)
# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))
# Split up data into randomized training and test sets
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)
print('Feature vector length:', len(X_train[0]))
# Use a linear SVC
svc = LinearSVC()
# Check the training time for the SVC
t = time.time()
svc.fit(X_train, y_train)
t2 = time.time()
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train classfier...')
# Check the score of the SVC
print('Test Accuracy of classfier = ', round(svc.score(X_test, y_test), 4))
# Check the prediction time for a single sample
t=time.time()
n_predict = 10
print('My classfier predicts: ', svc.predict(X_test[0:n_predict]))
print('For these',n_predict, 'labels: ', y_test[0:n_predict])
t2 = time.time()
print(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with classfier')
最终训练的分类器在测试数据集得到98.0%准确率
由于提取HOG特征比较耗时，先直接提取整张图片的HOG特征，然后获取每个窗口所属的那部分HOG特征，这样效率会更高，以下为滑动窗口搜索的代码实现：
# Define a single function that can extract features using hog sub-sampling and make predictions
def find_cars(img, ystart, ystop, scale, cspace, hog_channel, svc, X_scaler, orient,
pix_per_cell, cell_per_block, spatial_size, hist_bins, show_all_rectangles=False):
# array of rectangles where cars were detected
windows = []
img = img.astype(np.float32) / 255
img_tosearch = img[ystart:ystop, :, :]
# apply color conversion if other than 'RGB'
if cspace != 'RGB':
if cspace == 'HSV':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HSV)
elif cspace == 'LUV':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2LUV)
elif cspace == 'HLS':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2HLS)
elif cspace == 'YUV':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YUV)
elif cspace == 'YCrCb':
ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YCrCb)
else:
ctrans_tosearch = np.copy(img)
# rescale image if other than 1.0 scale
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1] / scale), np.int(imshape[0] / scale)))
# select colorspace channel for HOG
if hog_channel == 'ALL':
ch1 = ctrans_tosearch[:, :, 0]
ch2 = ctrans_tosearch[:, :, 1]
ch3 = ctrans_tosearch[:, :, 2]
else:
ch1 = ctrans_tosearch[:, :, hog_channel]
# Define blocks and steps as above
nxblocks = (ch1.shape[1] // pix_per_cell) + 1 # -1
nyblocks = (ch1.shape[0] // pix_per_cell) + 1 # -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) - 1
cells_per_step = 2 # Instead of overlap, define how many cells to step
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
nysteps = (nyblocks - nblocks_per_window) // cells_per_step
# Compute individual channel HOG features for the entire image
hog1 = utils.get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
if hog_channel == 'ALL':
hog2 = utils.get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
hog3 = utils.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()
if hog_channel == 'ALL':
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))
else:
hog_features = hog_feat1
xleft = xpos * pix_per_cell
ytop = ypos * pix_per_cell
test_prediction = svc.predict(hog_features)
if test_prediction == 1 or show_all_rectangles:
xbox_left = np.int(xleft * scale)
ytop_draw = np.int(ytop * scale)
win_draw = np.int(window * scale)
windows.append(
((xbox_left, ytop_draw + ystart), (xbox_left + win_draw, ytop_draw + win_draw + ystart)))
return windows
这里使用4类不同大小的滑动窗口对图片中的车辆进行搜索：
第一类大小为64x64,重叠率(overlap)为0.75：
第二类大小为96x96，重叠率(overlap)为0.75：
第三类大小为128x128,重叠率(overlap)为0.75:
第四类大小为224x224,重叠率(overlap)为0.75:
应用在测试图片得到的下列结果：
可以看到存在一些多窗口重合及错误检测现象
由于使用多个大小不一滑动窗口，且窗口存在重叠，单个车辆图像会被多个窗口捕捉检测。使用这个现象可以过滤错误检测。
记录一张图片上所有positive detections，使用记录的positive detections形成一个检测热力图：
def add_heat(heatmap, bbox_list):
# Iterate through list of bboxes
for box in bbox_list:
# Add += 1 for all pixels inside each bbox
# Assuming each "box" takes the form ((x1, y1), (x2, y2))
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1
以下应用在测试图片得到的检测热力图：
然后对热力图进行阈值过滤,过滤错误检测,以下为阈值过滤实现代码:
def apply_threshold(heatmap, threshold):
# Zero out pixels below the threshold
heatmap[heatmap <= threshold] = 0
# Return thresholded map
return heatmap
最后使用scipy.ndimage.measurements.label()方法传入过滤后的热力图可获取整合的检测窗口。
以下为pipeline应用在测试图片的效果：
以下为应用在测试视频的最终结果：