Comments (2)
@breezelj Hi, thanks for pointing out the issue. I also noticed that there is minor difference between bounding boxes produced by evaluate.py
and demo.py
.
# OTB Man sequence 0002 result
69.457,46.669,26.011,39.444 # demo.py
69.459,46.294,26.026,39.786 # evaluate.py
I recently digged into this issue and found out that got10k toolkit (evaluate.py
) uses PIL to read images. On the other hand, my demo.py
script reads images using opencv. There is a slight difference between pixel values of opencv and PIL images, which leads to different results. The following script reproduces this fact:
import numpy as np
import cv2
from PIL import Image
path = 'data/OTB/Man/img/0001.jpg'
cv2_img = cv2.imread(path)
cv2_img = cv2.cvtColor(cv2_img, cv2.COLOR_BGR2RGB)
pil_img = Image.open(path)
pil_img = np.array(pil_img)
print((cv2_img == pil_img).all()) # False
demo.py
yields exactly same results as evaluate.py
if you replace these lines with:
img_prev = np.array(Image.open(frames[i]))
img_curr = np.array(Image.open(frames[i+1]))
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very nice, thank you very much for detailed explanation
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Related Issues (20)
- get unstable results when evaluate the model HOT 3
- the choice of loss function and learning rate HOT 6
- an error about the use of exp_lr_scheduler HOT 6
- Error when executing test.py HOT 1
- Error when executing test.py which is similar to the error #17 HOT 9
- Model Load Error HOT 1
- About helper.py HOT 7
- pretrained model HOT 8
- What is the red bounding box and white bounding box in the images? HOT 1
- Training content HOT 3
- The loss is not drop any more on got1k dataset? HOT 4
- Inference on KITTI HOT 1
- bounding box recenter and uncenter? HOT 1
- Tracking of multiple Objects?
- Code cleanup
- help on annotation of Alov300++
- The loss explodes
- Performance is not ideal HOT 1
- [Bug] Generate batch sample function HOT 1
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