Python utils.image.affine_transform() Examples

The following are 2 code examples of utils.image.affine_transform(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module utils.image , or try the search function .
Example #1
Source File: h36m_iccv.py    From pytorch-pose-hg-3d with GNU General Public License v3.0 4 votes vote down vote up 
def __getitem__(self, index):
    if index < 10 and self.split == 'train':
      self.idxs = np.random.choice(
        self.num_samples, self.num_samples, replace=False)
    img = self._load_image(index)
    gt_3d, pts, c, s = self._get_part_info(index)
    
    r = 0
    s = np.array([s, s])
    s = adjust_aspect_ratio(s, self.aspect_ratio, self.opt.fit_short_side)
    
    trans_input = get_affine_transform(
      c, s, r, [self.opt.input_h, self.opt.input_w])
    inp = cv2.warpAffine(img, trans_input, (self.opt.input_h, self.opt.input_w),
                         flags=cv2.INTER_LINEAR)
    inp = (inp.astype(np.float32) / 256. - self.mean) / self.std
    inp = inp.transpose(2, 0, 1)

    trans_output = get_affine_transform(
      c, s, r, [self.opt.output_h, self.opt.output_w])
    out = np.zeros((self.num_joints, self.opt.output_h, self.opt.output_w), 
                    dtype=np.float32)
    reg_target = np.zeros((self.num_joints, 1), dtype=np.float32)
    reg_ind = np.zeros((self.num_joints), dtype=np.int64)
    reg_mask = np.zeros((self.num_joints), dtype=np.uint8)
    pts_crop = np.zeros((self.num_joints, 2), dtype=np.int32)
    for i in range(self.num_joints):
      pt = affine_transform(pts[i, :2], trans_output).astype(np.int32)
      if pt[0] >= 0 and pt[1] >=0 and pt[0] < self.opt.output_w \
        and pt[1] < self.opt.output_h:
        pts_crop[i] = pt
        out[i] = draw_gaussian(out[i], pt, self.opt.hm_gauss)
        reg_target[i] = pts[i, 2] / s[0] # assert not fit_short
        reg_ind[i] = pt[1] * self.opt.output_w * self.num_joints + \
                     pt[0] * self.num_joints + i # note transposed
        reg_mask[i] = 1
    
    meta = {'index' : self.idxs[index], 'center' : c, 'scale' : s,
            'gt_3d': gt_3d, 'pts_crop': pts_crop}

    ret = {'input': inp, 'target': out, 'meta': meta, 
           'reg_target': reg_target, 'reg_ind': reg_ind, 'reg_mask': reg_mask}
    
    return ret
Example #2
Source File: mpii.py    From pytorch-pose-hg-3d with GNU General Public License v3.0 4 votes vote down vote up 
def __getitem__(self, index):
    img = self._load_image(index)
    _, pts, c, s = self._get_part_info(index)
    r = 0
    
    if self.split == 'train':
      sf = self.opt.scale
      rf = self.opt.rotate
      s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf)
      r = np.clip(np.random.randn()*rf, -rf*2, rf*2) \
          if np.random.random() <= 0.6 else 0
    s = min(s, max(img.shape[0], img.shape[1])) * 1.0
    s = np.array([s, s])
    s = adjust_aspect_ratio(s, self.aspect_ratio, self.opt.fit_short_side)

    flipped = (self.split == 'train' and np.random.random() < self.opt.flip)
    if flipped:
      img = img[:, ::-1, :]
      c[0] = img.shape[1] - 1 - c[0]
      pts[:, 0] = img.shape[1] - 1 - pts[:, 0]
      for e in self.shuffle_ref:
        pts[e[0]], pts[e[1]] = pts[e[1]].copy(), pts[e[0]].copy()

    trans_input = get_affine_transform(
      c, s, r, [self.opt.input_h, self.opt.input_w])
    inp = cv2.warpAffine(img, trans_input, (self.opt.input_h, self.opt.input_w),
                         flags=cv2.INTER_LINEAR)
    inp = (inp.astype(np.float32) / 256. - self.mean) / self.std
    inp = inp.transpose(2, 0, 1)

    trans_output = get_affine_transform(
      c, s, r, [self.opt.output_h, self.opt.output_w])
    out = np.zeros((self.num_joints, self.opt.output_h, self.opt.output_w), 
                    dtype=np.float32)
    pts_crop = np.zeros((self.num_joints, 2), dtype=np.int32)
    for i in range(self.num_joints):
      if pts[i, 0] > 0 or pts[i, 1] > 0:
        pts_crop[i] = affine_transform(pts[i], trans_output)
        out[i] = draw_gaussian(out[i], pts_crop[i], self.opt.hm_gauss) 
    
    meta = {'index' : index, 'center' : c, 'scale' : s, \
            'pts_crop': pts_crop}
    return {'input': inp, 'target': out, 'meta': meta}