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 _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_pipe_test_video(self): p = car_finder_pipeline.Pipeline(20) uut.process("../input_videos/test_video.mp4",TEST_OUT_DIR+"/L_test_video.mp4",p.process)#,subC=(0,15))
def _test_pipe_project(self): p = car_finder_pipeline.Pipeline(20) uut.process("../input_videos/project_video.mp4",TEST_OUT_DIR+"/L_project_video.mp4",p.process)#,subC=(15,20))