I shared the output videos and test videos to you via private message.
More, in TAO deploy, there is center_crop of preprocessing, see tao_deploy/nvidia_tao_deploy/cv/classification_tf1/dataloader.py at 31c7e0ed3fe48942c254b3b85517e7418eea17b3 · NVIDIA/tao_deploy · GitHub.
but this “center_crop” is not supported in deepstream(similar topic: How to set true center crop for classification model in deepstream pipeline?). I already sync with deepstream team about this feature in deepstream.
So, for your staff video, it is needed to do “center_crop” for test images and then generate test video file.
You can add below to save “center_crop” images.
def _load_gt_image(self, image_path):
"""Load GT image from file."""
self.image_path = image_path #morganh
img = Image.open(image_path)
image = image.crop(
(left_corner,
top_corner,
left_corner + self.width,
top_corner + self.height))
tmp_name = "your_staff_crop_folder/" + str(self.image_path).split("/")[-1] #morganh
image.save(tmp_name) #morganh