def save_rgb_results(gt_rgb: torch.Tensor, pred_rgb: torch.Tensor,
path: str) -> None:
r"""For saving RGB reconstruction results during EQA-CNN-Pretrain eval.
Args:
gt_rgb: RGB ground truth tensor
pred_rgb: RGB reconstruction tensor
path: to save images
"""
path = path.format(split="val", type="rgb")
gt_bgr_o, pred_bgr = tensor_to_bgr_images([gt_rgb, pred_rgb])
cv2.imwrite(path + "_gt.jpg", gt_bgr_o)
cv2.imwrite(path + "_pred.jpg", pred_bgr)
def save_vqa_image_results(
images_tensor: torch.Tensor,
question: str,
prediction: str,
ground_truth: str,
path: str,
) -> None:
r"""For saving VQA input images with input question and predicted answer.
Being used to save model predictions during eval.
Args:
images_tensor: images' tensor containing input frames
question: input question to model
prediction: model's answer prediction
ground_truth: ground truth answer
path: to save images
Returns:
None
"""
images = tensor_to_bgr_images(images_tensor)
collage_image = cv2.hconcat(images)
collage_image = cv2.copyMakeBorder(
collage_image,
55,
0,
0,
0,
cv2.BORDER_CONSTANT,
value=(255, 255, 255),
)
image = put_vqa_text_on_image(collage_image, question, prediction,
ground_truth)
cv2.imwrite(path, image)