def process_image(img):
t1 = time.time()
#searched_windows = 0
# threshold for heatmap
threshold = 2
#img = mpimg.imread(img_src)
draw_img = np.copy(img)
# Search for smaller appearing vehicles in the distance
ystart = 414
ystop = 414 + 96
scale = 1
cells_per_step = 2
windows_far, heatmap_far, searched_windows_far = functions.find_cars(
img, cells_per_step, ystart, ystop, scale, svc, X_scaler, orient,
pix_per_cell, cell_per_block, spatial_size, hist_bins)
# Search for middle of the visual field with medium window size
ystart = 350
ystop = 350 + 192
scale = 1.5
cells_per_step = 2
windows_middle, heatmap_middle, searched_windows_middle = functions.find_cars(
img, cells_per_step, ystart, ystop, scale, svc, X_scaler, orient,
pix_per_cell, cell_per_block, spatial_size, hist_bins)
# Search for close to the camera for vehicles that appear large
ystart = 350
ystop = 670
scale = 3
cells_per_step = 2
windows_near, heatmap_near, searched_windows_near = functions.find_cars(
img, cells_per_step, ystart, ystop, scale, svc, X_scaler, orient,
pix_per_cell, cell_per_block, spatial_size, hist_bins)
# combine all windows
windows = windows_far + windows_middle + windows_near
# combine all heatmaps
heatmap = heatmap_far + heatmap_middle + heatmap_near
# apply threshold to the heatmap
heatmap = functions.apply_threshold(heatmap, threshold)
# apply labels to the heatmap
labels = label(heatmap)
# draw labels on a copy of the image
labeled_image = functions.draw_labeled_bboxes(draw_img, labels)
return labeled_image
def pipeline(img):
bbox=[]
for j in param_list:
ystart=j[0]
ystop=j[1]
scale=j[2]
bbox_l = find_cars(img, ystart, ystop, scale, color_space, hog_channel, svc,
X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins,spatial_feat,hist_feat,axfc=None,show_scalar=show_scalar)
bbox.extend(bbox_l)
#print(len(bbox), 'bboxes found in image')
if len(bbox) > 0:
frames_track.add_bbox(bbox)
#######################################
heat = np.zeros_like(img[:,:,0])
#heat = add_heat(heat, bbox)
#heat = apply_threshold(heat, 1)
for box in frames_track.prev_bbox:
heat = add_heat(heat, box)
#divide the heatmap image by number of frames it been tracked for
heat=heat//(frames_track.n_frames)
#heat=heat//len(frames_track.prev_bbox)
heat = apply_threshold(heat, 1)
heatmap = np.clip(heat, 0, 255)
########################################
labels = label(heatmap)
#print(labels[1], 'cars found')
draw_img = draw_labeled_bboxes(np.copy(img), labels)
#######################################
#prepare heatmap to be added to main image
#resize heatmap
hmcopy=np.copy(heatmap)
heat_to_add=cv2.resize(hmcopy,(320,192),cv2.INTER_CUBIC)
#resize image
resized_img = cv2.resize(img,(320,192),cv2.INTER_CUBIC)
resized_gray = cv2.cvtColor(resized_img , cv2.COLOR_RGB2GRAY)
res_R=np.copy(resized_gray)
res_R[(heat_to_add>0)] =255
res_G =np.copy(resized_gray)
res_G[(heat_to_add>0)] =0
res_B =np.copy(resized_gray)
res_B[(heat_to_add>0)] =0
draw_img[0:192,0:320,0]=res_R #np.clip(res_R,0,255)
draw_img[0:192,0:320,1]=resized_gray
draw_img[0:192,0:320,2]=resized_gray
return draw_img
def pipeline_nosmoothing(img):
bbox=[]
for j in param_list:
ystart=j[0]
ystop=j[1]
scale=j[2]
bbox_l = find_cars(img, ystart, ystop, scale, color_space, hog_channel, svc,
X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins,spatial_feat,hist_feat,axfc=None,show_scalar=show_scalar)
bbox.extend(bbox_l)
print(len(bbox), 'bboxes found in image')
#######################################
heat = np.zeros_like(img[:,:,0])
heat = add_heat(heat, bbox)
heat = apply_threshold(heat, 1)
heatmap = np.clip(heat, 0, 255)
########################################
labels = label(heatmap)
print(labels[1], 'cars found')
draw_img = draw_labeled_bboxes(np.copy(img), labels)
return draw_img
cell_per_block=for_pickle['cell_per_block']
hog_channel=for_pickle['hog_channel']
spatial_feat=for_pickle['spatial_feat']
hist_feat=for_pickle['hist_feat']
hog_feat=for_pickle['hog_feat']
show_scalar=False
#ffc,axfc=plt.subplots(1,2)
test_img = mpimg.imread('../test_images/test1.jpg')
print("img shape",test_img.shape)
ystart = 400
ystop = 656
scale = 1.5
print("spatial enabled:",spatial_feat,"Hist enabled:",hist_feat)
param_list=((400,464,1.0),(416,480,1.0),(400,496,1.5),(432,528,1.5),(400,528,2.0),(432,560,2.0),(400,596,3.5),(464,660,3.5))
bbox=[]
for j in param_list:
ystart=j[0]
ystop=j[1]
scale=j[2]
bbox_l = find_cars(test_img, ystart, ystop, scale, color_space, hog_channel, svc,
X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins,spatial_feat,hist_feat,axfc=None,show_scalar=show_scalar)
bbox.extend(bbox_l)
print(len(bbox), 'bboxes found in image')
im_bbox=draw_boxes(test_img, bbox, color=(0, 0, 255), thick=6)
f,p=plt.subplots()
p.imshow(im_bbox)
def process_frame(self, image):
"""
Process a single image frame and do hog subsampling to find cars and improve detection by using heatmap
:param image:
:return:
"""
# undistort image
image = cv2.undistort(image, self.calibration['mtx'],
self.calibration['dist'], None,
self.calibration['mtx'])
# normalize 0-1
image = image.astype(np.float32) / 255
draw = False
window_img, hot_windows = functions.find_cars(
image, self.ystart, self.ystop, 1.5, self.svc, self.X_scaler,
self.orient, self.pix_per_cell, self.cell_per_block,
self.spatial_size, self.hist_bins, self.hist_range)
# if windows found which possibly can be cars
if len(hot_windows) > 0:
print('Found {} windows'.format(len(hot_windows)))
heat = np.zeros_like(window_img[:, :, 0]).astype(np.float)
# Add heat to each box in box list
heat = functions.add_heat(heat, hot_windows)
# Apply threshold to help remove false positives
heat = functions.apply_threshold(heat, 2)
# add to list of previous heatmaps
self.heatmaps.append(heat)
# queue only latest 10 heatmaps
if len(self.heatmaps) > 10:
self.heatmaps.pop(0)
self.calc_avg_heat(10)
avg_heat = functions.apply_threshold(self.avg_heatmap, 10)
# Find final boxes from heatmap using label function
labels = label(avg_heat)
if labels[1] > 0:
print(labels[1], 'cars found')
if draw:
plt.imshow(labels[0], cmap='gray')
plt.show()
draw_img, car_centers = functions.draw_labeled_bboxes(
np.copy(window_img), labels=labels, color=(1.0, 0, 1.0))
if draw:
fig = plt.figure()
plt.subplot(121)
plt.imshow(draw_img)
plt.title('Car Positions')
plt.subplot(122)
# Visualize the heatmap when displaying
heatmap = np.clip(avg_heat, 0, 255)
plt.imshow(heatmap, cmap='hot')
plt.title('Heat Map')
fig.tight_layout()
plt.show()
window_img = draw_img
# Back to image space
return window_img * 255
test_images = glob.glob('./test_images/test*.jpg')
fig1 = plt.figure(figsize=(8, 12))
fig2 = plt.figure(figsize=(8, 18))
fig3 = plt.figure(figsize=(8, 12))
for idx, file in enumerate(test_images):
img = mpimg.imread(file)
total_heat = np.zeros_like(img[:, :, 0]).astype(np.float)
for scale, ystart, ystop in search_boxes:
bbox_list = find_cars(img,
colorspace,
ystart,
ystop,
scale,
svc,
X_scaler,
orient,
pix_per_cell,
cell_per_block, (spatial, spatial),
histbin,
hog_channel,
spatial_feat=True,
hist_feat=True)
heat = np.zeros_like(img[:, :, 0]).astype(np.float)
heat = add_heat(heat, bbox_list)
heat = apply_threshold(heat, 2)
total_heat = total_heat + heat
heatmap = np.clip(total_heat, 0, 255)
a = fig1.add_subplot(6, 2, (idx + 1) * 2 - 1)
plt.imshow(img)
b = fig1.add_subplot(6, 2, (idx + 1) * 2)
# threshold for heatmap
threshold = 2
for img_src in example_images:
t1 = time.time()
searched_windows = 0
img = mpimg.imread(img_src)
draw_img = np.copy(img)
# Search for smaller appearing vehicles in the distance
ystart = 414
ystop = 414 + 96
scale = 1
cells_per_step = 2
windows_far, heatmap_far, searched_windows_far = functions.find_cars(img, cells_per_step, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
# Search for middle of the visual field with medium window size
ystart = 350
ystop = 350 + 192
scale = 1.5
cells_per_step = 2
windows_middle, heatmap_middle, searched_windows_middle = functions.find_cars(img, cells_per_step, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
# Search for close to the camera for vehicles that appear large
ystart = 350
ystop = 670
scale = 3
cells_per_step = 2
windows_near, heatmap_near, searched_windows_near = functions.find_cars(img, cells_per_step, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
