def get_frame_hog(ctrans_tosearch):
ch1 = ctrans_tosearch[:, :, 0]
ch2 = ctrans_tosearch[:, :, 1]
ch3 = ctrans_tosearch[:, :, 2]
# Compute individual channel HOG features for the entire image
# Y channel
hog1 = FeatureExtraction.get_hog_features(ch1, folder="../buffer/hog-features/")
# Cr channel
hog2 = FeatureExtraction.get_hog_features(ch2)
# Cb channel
hog3 = FeatureExtraction.get_hog_features(ch3)
return hog1, hog2, hog3
def extract_single_img_features(img):
"""
combine spatial bin, color histogram and gradient histogram features for a single image
"""
# Create a list to append feature vectors to
features = []
# apply color conversion if other than 'RGB'
feature_image = Helper.change_cspace(img)
# get hog features for either specific channel or for all channels
if config["hog_channel"] == 'ALL':
hog_features = []
channels = feature_image.shape[2]
# get features for all 3 channels
for channel in range(channels):
hog_features.append(
FeatureExtraction.get_hog_features(feature_image[:, :,
channel],
feature_vec=True))
hog_features = np.ravel(hog_features)
else:
# get features for specific channel
hog_features = FeatureExtraction.get_hog_features(
feature_image[:, :, config["hog_channel"]], feature_vec=True)
# Apply bin_spatial() to get spatial color features
bin_features = FeatureExtraction.bin_spatial(feature_image,
config["spatial_size"])
# Apply color_hist() to get color histogram features
color_hist_features = FeatureExtraction.color_hist(
feature_image, config["hist_bins"])
# concatenate all 3 types of features
feature = np.concatenate(
(bin_features, color_hist_features, hog_features), axis=0)
# Append the new feature vector to the features list
features.append(feature)
# Return list of feature vectors
return features