def _resize_img(self, results):
if self.keep_ratio:
img1, scale_factor1 = commons.imrescale(results['img1'],
results['scale'],
return_scale=True)
img2, scale_factor2 = commons.imrescale(results['img2'],
results['scale'],
return_scale=True)
else:
img1, w_scale, h_scale = commons.imresize(results['img1'],
results['scale'],
return_scale=True)
scale_factor1 = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
img2, w_scale, h_scale = commons.imresize(results['img2'],
results['scale'],
return_scale=True)
scale_factor2 = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
results['img1'] = img1
results['img2'] = img2
results['img1_shape'] = img1.shape
results['img2_shape'] = img2.shape
results['pad1_shape'] = img1.shape # in case that there is no padding
results['pad2_shape'] = img2.shape # in case that there is no padding
results['scale_factor1'] = scale_factor1
results['scale_factor2'] = scale_factor2
results['keep_ratio'] = self.keep_ratio
def _resize_img(self, results):
results["img2"] = results['img'].copy()
flag = random.random()
donw_up_stage = random.randint(0, self.max_stage)
if flag < 0.5:
for i in range(donw_up_stage):
results["img2"] = cv2.pyrDown(results["img2"])
for i in range(donw_up_stage):
results["img2"] = cv2.pyrUp(results["img2"])
results['img2'] = cv2.resize(
results['img2'],
(results['img'].shape[1], results['img'].shape[0]))
else:
for i in range(donw_up_stage):
results["img"] = cv2.pyrDown(results["img"])
for i in range(donw_up_stage):
results["img"] = cv2.pyrUp(results["img"])
results['img'] = cv2.resize(
results['img'],
(results['img2'].shape[1], results['img2'].shape[0]))
assert results['img2'].shape[0] == results['img'].shape[0] and \
results['img2'].shape[1] == results['img'].shape[1]
if self.keep_ratio:
img1, scale_factor1 = commons.imrescale(results['img'],
results['scale'],
return_scale=True)
img2, scale_factor2 = commons.imrescale(results['img2'],
results['scale'],
return_scale=True)
else:
img1, w_scale, h_scale = commons.imresize(results['img'],
results['scale'],
return_scale=True)
scale_factor1 = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
img2, w_scale, h_scale = commons.imresize(results['img2'],
results['scale'],
return_scale=True)
scale_factor2 = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
results['img1'] = img1
results['img2'] = img2
results['img1_shape'] = img1.shape
results['img2_shape'] = img2.shape
results['pad1_shape'] = img1.shape # in case that there is no padding
results['pad2_shape'] = img2.shape # in case that there is no padding
results['scale_factor1'] = scale_factor1
results['scale_factor2'] = scale_factor2
results['keep_ratio'] = self.keep_ratio
def __call__(self, results):
if results['keep_ratio']:
gt_seg = commons.imrescale(results['gt_semantic_seg'],
results['scale'],
interpolation='nearest')
else:
gt_seg = commons.imresize(results['gt_semantic_seg'],
results['scale'],
interpolation='nearest')
if results['flip']:
gt_seg = commons.imflip(gt_seg)
if gt_seg.shape != results['pad_shape']:
gt_seg = commons.impad(gt_seg, results['pad_shape'][:2])
if self.scale_factor != 1:
gt_seg = commons.imrescale(gt_seg,
self.scale_factor,
interpolation='nearest')
results['gt_semantic_seg'] = gt_seg
return results
def _resize_img(self, results):
if self.keep_ratio:
img, scale_factor = commons.imrescale(results['img'],
results['scale'],
return_scale=True)
else:
img, w_scale, h_scale = commons.imresize(results['img'],
results['scale'],
return_scale=True)
scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
dtype=np.float32)
results['img'] = img
results['img_shape'] = img.shape
results['pad_shape'] = img.shape # in case that there is no padding
results['scale_factor'] = scale_factor
results['keep_ratio'] = self.keep_ratio