Thanks for releasing the code.The code help me a lot. I have 2 questions:
1、in image.py, to resize the density map of ground truth to the 1/64 of the original size of ground truth, you used the following code:
target = cv2.resize(target,(target.shape[1]/8,target.shape[0]/8),interpolation = cv2.INTER_CUBIC)*64
but I found that the resize operation cannot make sure the crowd count of resized density map reduce to the 1/64 of the original density map, for example, if the crowd count of original density map is 640, after resize operation, the crowd count of resized density map may be 9, so 9 * 64 != 640. Am I Wrong?
2、the second doubt is the same to #18 , I read the commentary on this question but I still don't know how to solve this problem. You said 'don't mind this slight variation' in make_dataset.ipynb, but I found the variation cannot be neglect(especially when using Geometry-adaptive kernels).
considering 1 and 2, the crowd count of ground_truth we used for training and the crowd count of ground truth given by dataset is not equal, so any idea to solve these problem? Thank you!
Thanks for releasing the code.The code help me a lot. I have 2 questions: 1、in image.py, to resize the density map of ground truth to the 1/64 of the original size of ground truth, you used the following code:
target = cv2.resize(target,(target.shape[1]/8,target.shape[0]/8),interpolation = cv2.INTER_CUBIC)*64but I found that the resize operation cannot make sure the crowd count of resized density map reduce to the 1/64 of the original density map, for example, if the crowd count of original density map is 640, after resize operation, the crowd count of resized density map may be 9, so 9 * 64 != 640. Am I Wrong? 2、the second doubt is the same to #18 , I read the commentary on this question but I still don't know how to solve this problem. You said 'don't mind this slight variation' in make_dataset.ipynb, but I found the variation cannot be neglect(especially when using Geometry-adaptive kernels). considering 1 and 2, the crowd count of ground_truth we used for training and the crowd count of ground truth given by dataset is not equal, so any idea to solve these problem? Thank you!