init commit of samurai

lib/train/data/processing_utils.py

@@ -0,0 +1,168 @@
+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
+