def main():
filter = Filter(model_file="model.p", scaler_file="scaler.p")
#filter.predict_batch(image_path=glob("./labeled_data_smallset/vehicles_smallset/**/*.*"))
#filter.predict_batch(image_path=filter.test_clf_image_paths)
frame = None
cnt = 0
for path in filter.test_video_images_path:
cnt += 1
if frame != None and cnt == frame:
image = cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)
final_image = filter.pipepine(image)
plt.imshow(final_image)
plt.show()
break
elif frame == None:
image = cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB)
final_image = filter.pipepine(image)
plt.imshow(final_image)
plt.show()
# image_res, centroids_and_sizes = filter.sliding_box_multi_level(image, level=2)
"""
def main():
filter = Filter(model_file="model.p", scaler_file="scaler.p")
clip = VideoFileClip("project_video_short3.mp4")
cnt = 0
stop_frame_num = 113
for img in clip.iter_frames():
cnt += 1
if (cnt == stop_frame_num):
if img.shape[2] == 4:
img = img[:, :, :3]
ret = filter.pipepine(img)
plt.figure(figsize=(16, 10))
plt.imshow(filter.diagScreen)
plt.subplots_adjust(left=0.03, bottom=0.03, right=1, top=1)
plt.show()