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))