import torch import math import cv2 as cv import torch.nn.functional as F import numpy as np '''modified from the original test implementation Replace cv.BORDER_REPLICATE with cv.BORDER_CONSTANT Add a variable called att_mask for computing attention and positional encoding later''' def sample_target(im, target_bb, search_area_factor, output_sz=None, mask=None): """ Extracts a square crop centered at target_bb box, of area search_area_factor^2 times target_bb area args: im - cv image target_bb - target box [x, y, w, h] search_area_factor - Ratio of crop size to target size output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done. returns: cv image - extracted crop float - the factor by which the crop has been resized to make the crop size equal output_size """ if not isinstance(target_bb, list): x, y, w, h = target_bb.tolist() else: x, y, w, h = target_bb # Crop image crop_sz = math.ceil(math.sqrt(w * h) * search_area_factor) if crop_sz < 1: raise Exception('Too small bounding box.') x1 = round(x + 0.5 * w - crop_sz * 0.5) x2 = x1 + crop_sz y1 = round(y + 0.5 * h - crop_sz * 0.5) y2 = y1 + crop_sz x1_pad = max(0, -x1) x2_pad = max(x2 - im.shape[1] + 1, 0) y1_pad = max(0, -y1) y2_pad = max(y2 - im.shape[0] + 1, 0) # Crop target im_crop = im[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :] if mask is not None: mask_crop = mask[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad] # Pad im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad, x2_pad, cv.BORDER_CONSTANT) # deal with attention mask H, W, _ = im_crop_padded.shape att_mask = np.ones((H,W)) end_x, end_y = -x2_pad, -y2_pad if y2_pad == 0: end_y = None if x2_pad == 0: end_x = None att_mask[y1_pad:end_y, x1_pad:end_x] = 0 if mask is not None: mask_crop_padded = F.pad(mask_crop, pad=(x1_pad, x2_pad, y1_pad, y2_pad), mode='constant', value=0) if output_sz is not None: resize_factor = output_sz / crop_sz im_crop_padded = cv.resize(im_crop_padded, (output_sz, output_sz)) att_mask = cv.resize(att_mask, (output_sz, output_sz)).astype(np.bool_) if mask is None: return im_crop_padded, resize_factor, att_mask mask_crop_padded = \ F.interpolate(mask_crop_padded[None, None], (output_sz, output_sz), mode='bilinear', align_corners=False)[0, 0] return im_crop_padded, resize_factor, att_mask, mask_crop_padded else: if mask is None: return im_crop_padded, att_mask.astype(np.bool_), 1.0 return im_crop_padded, 1.0, att_mask.astype(np.bool_), mask_crop_padded def transform_image_to_crop(box_in: torch.Tensor, box_extract: torch.Tensor, resize_factor: float, crop_sz: torch.Tensor, normalize=False) -> torch.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box_in - the box for which the co-ordinates are to be transformed box_extract - the box about which the image crop has been extracted. resize_factor - the ratio between the original image scale and the scale of the image crop crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_extract_center = box_extract[0:2] + 0.5 * box_extract[2:4] box_in_center = box_in[0:2] + 0.5 * box_in[2:4] box_out_center = (crop_sz - 1) / 2 + (box_in_center - box_extract_center) * resize_factor box_out_wh = box_in[2:4] * resize_factor box_out = torch.cat((box_out_center - 0.5 * box_out_wh, box_out_wh)) if normalize: return box_out / crop_sz[0] else: return box_out def jittered_center_crop(frames, box_extract, box_gt, search_area_factor, output_sz, masks=None): """ For each frame in frames, extracts a square crop centered at box_extract, of area search_area_factor^2 times box_extract area. The extracted crops are then resized to output_sz. Further, the co-ordinates of the box box_gt are transformed to the image crop co-ordinates args: frames - list of frames box_extract - list of boxes of same length as frames. The crops are extracted using anno_extract box_gt - list of boxes of same length as frames. The co-ordinates of these boxes are transformed from image co-ordinates to the crop co-ordinates search_area_factor - The area of the extracted crop is search_area_factor^2 times box_extract area output_sz - The size to which the extracted crops are resized returns: list - list of image crops list - box_gt location in the crop co-ordinates """ if masks is None: crops_resize_factors = [sample_target(f, a, search_area_factor, output_sz) for f, a in zip(frames, box_extract)] frames_crop, resize_factors, att_mask = zip(*crops_resize_factors) masks_crop = None else: crops_resize_factors = [sample_target(f, a, search_area_factor, output_sz, m) for f, a, m in zip(frames, box_extract, masks)] frames_crop, resize_factors, att_mask, masks_crop = zip(*crops_resize_factors) # frames_crop: tuple of ndarray (128,128,3), att_mask: tuple of ndarray (128,128) crop_sz = torch.Tensor([output_sz, output_sz]) # find the bb location in the crop '''Note that here we use normalized coord''' box_crop = [transform_image_to_crop(a_gt, a_ex, rf, crop_sz, normalize=True) for a_gt, a_ex, rf in zip(box_gt, box_extract, resize_factors)] # (x1,y1,w,h) list of tensors return frames_crop, box_crop, att_mask, masks_crop def transform_box_to_crop(box: torch.Tensor, crop_box: torch.Tensor, crop_sz: torch.Tensor, normalize=False) -> torch.Tensor: """ Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image args: box - the box for which the co-ordinates are to be transformed crop_box - bounding box defining the crop in the original image crop_sz - size of the cropped image returns: torch.Tensor - transformed co-ordinates of box_in """ box_out = box.clone() box_out[:2] -= crop_box[:2] scale_factor = crop_sz / crop_box[2:] box_out[:2] *= scale_factor box_out[2:] *= scale_factor if normalize: return box_out / crop_sz[0] else: return box_out