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test_model.py: cleanup

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Nils Koch 2022-06-02 15:20:28 +02:00
parent 7e0305ed91
commit 6ba2cd9e5d
1 changed files with 13 additions and 43 deletions
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test_model.py
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@ -8,7 +8,6 @@ from nets import Model
import wandb
import random
from torch.utils.data import DataLoader
from dataset import CTDDataset
from train import normalize_and_colormap, parse_yaml, inference as ctd_inference
@ -79,45 +78,16 @@ if __name__ == '__main__':
model.to(device)
model.eval()
CTD = True
if not CTD:
left_img = cv2.imread("../test_imgs/left.png")
right_img = cv2.imread("../test_imgs/right.png")
in_h, in_w = left_img.shape[:2]
# Resize image in case the GPU memory overflows
eval_h, eval_w = (in_h, in_w)
# FIXME borked for some reason, hopefully not very important
imgL = cv2.resize(left_img, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR)
imgR = cv2.resize(right_img, (eval_w, eval_h), interpolation=cv2.INTER_LINEAR)
pred = inference(imgL, imgR, model, n_iter=20)
t = float(in_w) / float(eval_w)
disp = cv2.resize(pred, (in_w, in_h), interpolation=cv2.INTER_LINEAR) * t
disp_vis = (disp - disp.min()) / (disp.max() - disp.min()) * 255.0
disp_vis = disp_vis.astype("uint8")
disp_vis = cv2.applyColorMap(disp_vis, cv2.COLORMAP_INFERNO)
combined_img = np.hstack((left_img, disp_vis))
# cv2.namedWindow("output", cv2.WINDOW_NORMAL)
# cv2.imshow("output", combined_img)
cv2.imwrite("output.jpg", disp_vis)
# cv2.waitKey(0)
else:
dataset = CTDDataset('/media/Data1/connecting_the_dots_data/ctd_data/', data_type=data_type,
pattern_path=reference_pattern_path, augment=augment)
dataloader = DataLoader(dataset, args.batch_size, shuffle=True,
num_workers=0, drop_last=False, persistent_workers=False, pin_memory=True)
for batch in dataloader:
for left, right, disparity in zip(batch['left'], batch['right'], batch['disparity']):
right = right.transpose(0, 2).transpose(0, 1)
left_img = left
imgL = left.cpu().detach().numpy()
right_img = right
imgR = right.cpu().detach().numpy()
gt_disp = disparity
do_infer(left_img, right_img, gt_disp, model)
dataset = CTDDataset('/media/Data1/connecting_the_dots_data/ctd_data/', data_type=data_type,
pattern_path=reference_pattern_path, augment=augment)
dataloader = DataLoader(dataset, args.batch_size, shuffle=True,
num_workers=0, drop_last=False, persistent_workers=False, pin_memory=True)
for batch in dataloader:
for left, right, disparity in zip(batch['left'], batch['right'], batch['disparity']):
right = right.transpose(0, 2).transpose(0, 1)
left_img = left
imgL = left.cpu().detach().numpy()
right_img = right
imgR = right.cpu().detach().numpy()
gt_disp = disparity
do_infer(left_img, right_img, gt_disp, model)