def sliding_window(cls, pview):
#Perspective View coordinates
x1, y1, x2, y2 = pview
#Initial height,width & aspect ratio of perspective view
iheight = y2 - y1
iwidth = x2 - x1
aspect_ratio = iwidth / iheight
#Resized height,width
nheight = 64
nwidth = int(nheight * aspect_ratio)
#Resized Perspective view
pmg = cv2.resize(cls.image[y1:y2, x1:x2, :], (nwidth, nheight))
#Hog features of the entire perspective view
hog_features = FeatureExtractor.extract_hog_features(pmg)
#Sliding window on perspective view with a sliding of 8 pixel increments
#Slide size is 8 pixels is to be able to subsample the hog features as it has 8 pixels per block
img_block_size = 64
slide_size = 8
hog_block_size = 8
xend = nwidth - img_block_size + 1
for idx, vx in enumerate(range(0, xend, slide_size)):
#Select the sub-image specified by the sliding window
smg = pmg[:, vx:vx + img_block_size]
#Get the features for the current sliding window
sfeatures = FeatureExtractor.extract_spatial_features(smg)
hfeatures = FeatureExtractor.extract_histogram_features(smg)
gfeatures0 = hog_features[0][:, idx:idx + hog_block_size].ravel()
gfeatures1 = hog_features[1][:, idx:idx + hog_block_size].ravel()
gfeatures2 = hog_features[2][:, idx:idx + hog_block_size].ravel()
features = np.concatenate(
(sfeatures, hfeatures, gfeatures0, gfeatures1, gfeatures2))
pred = cls.model.predict(features)
#bmg=cv2.cvtColor(smg,cv2.COLOR_HSV2BGR)
#cv2.imwrite("genimgs/img" + str(random.randint(0,1000)) + ".png" ,bmg)
if (pred == 1):
#Calculating the window coordinates in main image basing on the resized image coordinates
rx1 = int(vx * (iwidth / nwidth)) + x1
ry1 = y1
rx2 = rx1 + iheight
ry2 = ry1 + iheight
cls.hot_windows.append(((rx1, ry1), (rx2, ry2)))
#cv2.rectangle(cls.frame_image,(rx1,ry1),(rx2,ry2), (0,255,0), 1)
if (vx > xend - 32):
cls.hot_windows.append(((rx1, ry1), (rx2, ry2)))
def get_features_labels(cls):
v_features = []
n_features = []
t1=time.time()
for img in cls.v_images:
sfeatures = FE.extract_spatial_features(img)
hfeatures = FE.extract_histogram_features(img)
gfeatures = FE.extract_hog_features(img)
features=np.concatenate( (sfeatures,hfeatures,gfeatures[0].ravel(),gfeatures[1].ravel(),gfeatures[2].ravel()) )
v_features.append(features)
for img in cls.n_images:
sfeatures = FE.extract_spatial_features(img)
hfeatures = FE.extract_histogram_features(img)
gfeatures = FE.extract_hog_features(img)
features=np.concatenate( (sfeatures,hfeatures,gfeatures[0].ravel(),gfeatures[1].ravel(),gfeatures[2].ravel()) )
n_features.append(features)
cls.features = np.vstack((v_features, n_features)).astype(np.float64)
cls.labels = np.hstack((np.ones(len(v_features)), np.zeros(len(n_features))))
t2=time.time()
print("Feature & Label time - " ,round(t2-t1, 2) )
def find_cars(image, classifier, ystart, ystop, scale=1.0):
"""
scan the image within given ystart and ystop for car
"""
img_tosearch = image[ystart:ystop, :, :]
ctrans_tosearch = Helpers.convert_color(img_tosearch, color_space=classifier.color_space)
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] // classifier.pix_per_cell) - classifier.cell_per_block + 1
nyblocks = (ch1.shape[0] // classifier.pix_per_cell) - classifier.cell_per_block + 1
# 64 was the original sampling rate, with 8 cells and 8 pix per cell
window = 64
nblocks_per_window = (window // classifier.pix_per_cell) - classifier.cell_per_block + 1
# compute individual channel HOG features for the entire image
hog1 = FeatureExtractor.get_hog_features(ch1, classifier.orient, classifier.pix_per_cell, classifier.cell_per_block, vis=False, feature_vec=False)
hog2 = FeatureExtractor.get_hog_features(ch2, classifier.orient, classifier.pix_per_cell, classifier.cell_per_block, vis=False, feature_vec=False)
hog3 = FeatureExtractor.get_hog_features(ch3, classifier.orient, classifier.pix_per_cell, classifier.cell_per_block, vis=False, feature_vec=False)
bboxes = []
max_x_step = nxblocks - nblocks_per_window
max_y_step = nyblocks - nblocks_per_window
for y_step in range(max_y_step):
y_pos_cells = y_step
x_step = 0
while x_step < max_x_step:
x_pos_cells = x_step
# extract hog for this patch
hog_feat1 = hog1[y_pos_cells:y_pos_cells+nblocks_per_window, x_pos_cells:x_pos_cells+nblocks_per_window].ravel()
hog_feat2 = hog2[y_pos_cells:y_pos_cells+nblocks_per_window, x_pos_cells:x_pos_cells+nblocks_per_window].ravel()
hog_feat3 = hog3[y_pos_cells:y_pos_cells+nblocks_per_window, x_pos_cells:x_pos_cells+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
x_left_pixels = x_pos_cells * classifier.pix_per_cell
y_top_pixels = y_pos_cells * classifier.pix_per_cell
# extract the image patch
subimg = cv2.resize(ctrans_tosearch[y_top_pixels:y_top_pixels+window, x_left_pixels:x_left_pixels+window], (64, 64))
# get color features
spatial_features = FeatureExtractor.extract_spatial_features(subimg, size=classifier.spatial_size)
hist_features = FeatureExtractor.extract_hist_features(subimg, nbins=classifier.hist_bins)
# scale features and make a prediction
test_features = classifier.x_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
test_prediction = classifier.svc.predict(test_features)
if test_prediction == 1:
xbox_left = np.int(x_left_pixels*scale)
ytop_draw = np.int(y_top_pixels*scale)
win_draw = np.int(window*scale)
bbox = [(xbox_left, ytop_draw + ystart), (xbox_left + win_draw, ytop_draw + ystart + win_draw)]
bboxes.append(bbox)
x_step += 1
else:
x_step += 2
return bboxes
