| import os |
| import cv2 |
| import torch |
| import numpy as np |
| import torchvision |
| from PIL import Image |
|
|
|
|
| def unnormalize(tens, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]): |
| |
| return tens * torch.Tensor(std)[None, :, None, None] + torch.Tensor( |
| mean)[None, :, None, None] |
|
|
|
|
| def get_heatmap_cv(img, magn, max_flow_mag): |
| min_flow_mag = .5 |
| cv_magn = np.clip( |
| 255 * (magn - min_flow_mag) / (max_flow_mag - min_flow_mag), |
| a_min=0, |
| a_max=255).astype(np.uint8) |
| if img.dtype != np.uint8: |
| img = (255 * img).astype(np.uint8) |
|
|
| heatmap_img = cv2.applyColorMap(cv_magn, cv2.COLORMAP_JET) |
| heatmap_img = heatmap_img[..., ::-1] |
|
|
| h, w = magn.shape |
| img_alpha = np.ones((h, w), dtype=np.double)[:, :, None] |
| heatmap_alpha = np.clip( |
| magn / max_flow_mag, a_min=0, a_max=1)[:, :, None]**.7 |
| heatmap_alpha[heatmap_alpha < .2]**.5 |
| pm_hm = heatmap_img * heatmap_alpha |
| pm_img = img * img_alpha |
| cv_out = pm_hm + pm_img * (1 - heatmap_alpha) |
| cv_out = np.clip(cv_out, a_min=0, a_max=255).astype(np.uint8) |
|
|
| return cv_out |
|
|
|
|
| def get_heatmap_batch(img_batch, pred_batch): |
| imgrid = torchvision.utils.make_grid(img_batch).cpu() |
| magn_batch = torch.norm(pred_batch, p=2, dim=1, keepdim=True) |
| magngrid = torchvision.utils.make_grid(magn_batch) |
| magngrid = magngrid[0, :, :] |
| imgrid = unnormalize(imgrid).squeeze_() |
|
|
| cv_magn = magngrid.detach().cpu().numpy() |
| cv_img = imgrid.permute(1, 2, 0).detach().cpu().numpy() |
| cv_out = get_heatmap_cv(cv_img, cv_magn, max_flow_mag=9) |
| out = np.asarray(cv_out).astype(np.double) / 255.0 |
|
|
| out = torch.from_numpy(out).permute(2, 0, 1) |
| return out |
|
|
|
|
| def save_heatmap_cv(img, magn, path, max_flow_mag=7): |
| cv_out = get_heatmap_cv(img, magn, max_flow_mag) |
| out = Image.fromarray(cv_out) |
| out.save(path, quality=95) |
|
|
| |
|
|
