def test_hotspot(self):
# Read in a pickle file with bboxes saved
# Each item in the "all_bboxes" list will contain a
# list of boxes for one of the images shown above
box_list = pickle.load(open("bbox_pickle.p", "rb"))
# Read in image similar to one shown above
image = mpimg.imread(IMG_FILE)
image_with_boxes = draw_boxes(image, box_list)
heat = np.zeros_like(image[:, :, 0]).astype(np.float)
# Add heat to each box in box list
heat = uut.add_heat(heat, box_list)
# Apply threshold to help remove false positives
heat = uut.apply_threshold(heat, 1)
# Visualize the heatmap when displaying
heatmap = np.clip(heat, 0, 255)
# Find final boxes from heatmap using label function
labels = label(heatmap)
draw_img = uut.draw_labeled_bboxes(np.copy(image), labels)
final_image = image_util.arrangeImages(
[image, image_with_boxes, heatmap, draw_img],
["Original", "Boxes that detected car", "Heatmap", "labeld boxes"],
2)
image_util.saveImage(final_image, TEST_OUT_DIR + "/hotspot.jpg")
def test_readme_video_images(self):
# images from sequence in video
p = pipeline.Pipeline(1)
imgs = image_util.loadImagesRGB(IMG_DIR2)
for i, img in enumerate(imgs):
draw_img, labels, heatmap, boxed_image = p.process_verbose(
np.copy(img))
out = image_util.arrangeImages(
[img, boxed_image, heatmap, labels[0], draw_img],
["original", "car detections", "heatmap", "labels", "result"],
figsize=(5, 1))
image_util.saveImage(
out, TEST_OUT_DIR + "/readme_videoprocess" + str(i) + ".png")
p = pipeline.Pipeline(9)
imgs = image_util.loadImagesRGB(IMG_DIR2)
for i, img in enumerate(imgs):
draw_img, labels, heatmap, boxed_image = p.process_verbose(
np.copy(img))
out = image_util.arrangeImages(
[img, boxed_image, heatmap, labels[0], draw_img],
["original", "car detections", "heatmap", "labels", "result"],
figsize=(5, 1))
image_util.saveImage(
out, TEST_OUT_DIR + "/readme_videoprocess_with_history" +
str(i) + ".png")
def draw_sliding_windows(self, filename, img, y_start, y_stop, scale):
xy_window = (int(64*scale),int(64*scale))
boxes = slide_window(img, y_start_stop=[y_start, y_stop], xy_window=xy_window, xy_overlap=(0.75, 0.75))
img = draw_boxes(img,boxes)
img = cv2.rectangle(img,(0,y_start),(xy_window[0],y_start+xy_window[1]), (255,0,0), 6)
print (len(boxes))
print (img.shape)
image_util.saveImage(img, filename)
def _test_process(self):
p = pipeline.Pipeline(1)
imgs = image_util.loadImagesRGB(IMG_DIR)
for i, img in enumerate(imgs):
processed_img = p.process(np.copy(img))
out = image_util.arrangeImages([img, processed_img],
["original", "car detection"],
figsize=(4, 2))
image_util.saveImage(
out, TEST_OUT_DIR + "/identified_boxes" + str(i) + ".png")
def test_02_example_labeled_data(self):
vehicle_img = image_util.loadImageRGB(self.vehicles[0])
non_vehicle_img = image_util.loadImageRGB(self.non_vehicles[0])
img = image_util.arrangeImages([vehicle_img, non_vehicle_img], ["vehicle","non vehicle"])
image_util.saveImage(img, TEST_OUT_DIR + "/vehicle_non_vehicle.png")
def visualize_hog(self, img, prefix, orientations, pix_per_cell, cell_per_block, color ):
ch1, ch2, ch3, hog_image_c1, hog_image_c2, hog_image_c3 = self.hogChannels(img, orientations=orientations, pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, color=color )
img = image_util.arrangeImages([img, ch1, ch2, ch3, hog_image_c1, hog_image_c2, hog_image_c3], ["original", "c1", "c2", "c3", "hog c1","hog c2","hog c3"], figsize=(7,1))
image_util.saveImage(img, TEST_OUT_DIR + "/"+ prefix +"_orient"+str(orientations)+"_pix_per_cell"+str(pix_per_cell)+"_cell_per_block"+str(cell_per_block)+"_"+str(color)+".png")
def test_02_loadAndSave(self):
imgs = uut.loadImagesRGB(CAMERA_CAL)
for i,img in enumerate(imgs):
print("2" + str(img.shape))
uut.saveImage(img, TEST_OUT_DIR+"/out"+str(i)+".png")