Python mmcv.impad() Examples

The following are 30 code examples of mmcv.impad(). 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 mmcv , or try the search function .
Example #1
Source File: transforms.py    From ttfnet with Apache License 2.0 6 votes vote down vote up 
def __call__(self, results):
        if results['keep_ratio']:
            gt_seg = mmcv.imrescale(
                results['gt_semantic_seg'],
                results['scale'],
                interpolation='nearest')
        else:
            gt_seg = mmcv.imresize(
                results['gt_semantic_seg'],
                results['scale'],
                interpolation='nearest')
        if results['flip']:
            gt_seg = mmcv.imflip(gt_seg)
        if gt_seg.shape != results['pad_shape']:
            gt_seg = mmcv.impad(gt_seg, results['pad_shape'][:2])
        if self.scale_factor != 1:
            gt_seg = mmcv.imrescale(
                gt_seg, self.scale_factor, interpolation='nearest')
        results['gt_semantic_seg'] = gt_seg
        return results
Example #2
Source File: transforms.py    From IoU-Uniform-R-CNN with Apache License 2.0 6 votes vote down vote up 
def __call__(self, results):
        if results['keep_ratio']:
            gt_seg = mmcv.imrescale(
                results['gt_semantic_seg'],
                results['scale'],
                interpolation='nearest')
        else:
            gt_seg = mmcv.imresize(
                results['gt_semantic_seg'],
                results['scale'],
                interpolation='nearest')
        if results['flip']:
            gt_seg = mmcv.imflip(gt_seg)
        if gt_seg.shape != results['pad_shape']:
            gt_seg = mmcv.impad(gt_seg, results['pad_shape'][:2])
        if self.scale_factor != 1:
            gt_seg = mmcv.imrescale(
                gt_seg, self.scale_factor, interpolation='nearest')
        results['gt_semantic_seg'] = gt_seg
        return results
Example #3
Source File: transforms.py    From RDSNet with Apache License 2.0 6 votes vote down vote up 
def __call__(self, results):
        if results['keep_ratio']:
            gt_seg = mmcv.imrescale(
                results['gt_semantic_seg'],
                results['scale'],
                interpolation='nearest')
        else:
            gt_seg = mmcv.imresize(
                results['gt_semantic_seg'],
                results['scale'],
                interpolation='nearest')
        if results['flip']:
            gt_seg = mmcv.imflip(gt_seg)
        if gt_seg.shape != results['pad_shape']:
            gt_seg = mmcv.impad(gt_seg, results['pad_shape'][:2])
        if self.scale_factor != 1:
            gt_seg = mmcv.imrescale(
                gt_seg, self.scale_factor, interpolation='nearest')
        results['gt_semantic_seg'] = gt_seg
        return results
Example #4
Source File: transforms.py    From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 6 votes vote down vote up 
def __call__(self, results):
        if results['keep_ratio']:
            gt_seg = mmcv.imrescale(
                results['gt_semantic_seg'],
                results['scale'],
                interpolation='nearest')
        else:
            gt_seg = mmcv.imresize(
                results['gt_semantic_seg'],
                results['scale'],
                interpolation='nearest')
        if results['flip']:
            gt_seg = mmcv.imflip(gt_seg)
        if gt_seg.shape != results['pad_shape']:
            gt_seg = mmcv.impad(gt_seg, results['pad_shape'][:2])
        if self.scale_factor != 1:
            gt_seg = mmcv.imrescale(
                gt_seg, self.scale_factor, interpolation='nearest')
        results['gt_semantic_seg'] = gt_seg
        return results
Example #5
Source File: transforms.py    From Cascade-RPN with Apache License 2.0 6 votes vote down vote up 
def __call__(self, results):
        if results['keep_ratio']:
            gt_seg = mmcv.imrescale(
                results['gt_semantic_seg'],
                results['scale'],
                interpolation='nearest')
        else:
            gt_seg = mmcv.imresize(
                results['gt_semantic_seg'],
                results['scale'],
                interpolation='nearest')
        if results['flip']:
            gt_seg = mmcv.imflip(gt_seg)
        if gt_seg.shape != results['pad_shape']:
            gt_seg = mmcv.impad(gt_seg, results['pad_shape'][:2])
        if self.scale_factor != 1:
            gt_seg = mmcv.imrescale(
                gt_seg, self.scale_factor, interpolation='nearest')
        results['gt_semantic_seg'] = gt_seg
        return results
Example #6
Source File: transforms.py    From kaggle-kuzushiji-recognition with MIT License 6 votes vote down vote up 
def __call__(self, results):
        if results['keep_ratio']:
            gt_seg = mmcv.imrescale(
                results['gt_semantic_seg'],
                results['scale'],
                interpolation='nearest')
        else:
            gt_seg = mmcv.imresize(
                results['gt_semantic_seg'],
                results['scale'],
                interpolation='nearest')
        if results['flip']:
            gt_seg = mmcv.imflip(gt_seg)
        if gt_seg.shape != results['pad_shape']:
            gt_seg = mmcv.impad(gt_seg, results['pad_shape'][:2])
        if self.scale_factor != 1:
            gt_seg = mmcv.imrescale(
                gt_seg, self.scale_factor, interpolation='nearest')
        results['gt_semantic_seg'] = gt_seg
        return results
Example #7
Source File: transforms.py    From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 5 votes vote down vote up 
def _pad_img(self, results):
        if self.size is not None:
            padded_img = mmcv.impad(results['img'], self.size)
        elif self.size_divisor is not None:
            padded_img = mmcv.impad_to_multiple(
                results['img'], self.size_divisor, pad_val=self.pad_val)
        results['img'] = padded_img
        results['pad_shape'] = padded_img.shape
        results['pad_fixed_size'] = self.size
        results['pad_size_divisor'] = self.size_divisor
Example #8
Source File: transforms.py    From Libra_R-CNN with Apache License 2.0 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        masks = [
            mmcv.imrescale(mask, scale_factor, interpolation='nearest')
            for mask in masks
        ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #9
Source File: transforms.py    From FoveaBox with Apache License 2.0 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        # aspect ratio unchanged
        if isinstance(scale_factor, float):
            masks = [
                mmcv.imrescale(mask, scale_factor, interpolation='nearest')
                for mask in masks
            ]
        # aspect ratio changed
        else:
            w_ratio, h_ratio = scale_factor[:2]
            if masks:
                h, w = masks[0].shape[:2]
                new_h = int(np.round(h * h_ratio))
                new_w = int(np.round(w * w_ratio))
                new_size = (new_w, new_h)
                masks = [
                    mmcv.imresize(mask, new_size, interpolation='nearest')
                    for mask in masks
                ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #10
Source File: transforms.py    From Cascade-RPN with Apache License 2.0 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        # aspect ratio unchanged
        if isinstance(scale_factor, float):
            masks = [
                mmcv.imrescale(mask, scale_factor, interpolation='nearest')
                for mask in masks
            ]
        # aspect ratio changed
        else:
            w_ratio, h_ratio = scale_factor[:2]
            if masks:
                h, w = masks[0].shape[:2]
                new_h = int(np.round(h * h_ratio))
                new_w = int(np.round(w * w_ratio))
                new_size = (new_w, new_h)
                masks = [
                    mmcv.imresize(mask, new_size, interpolation='nearest')
                    for mask in masks
                ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #11
Source File: transforms.py    From Cascade-RPN with Apache License 2.0 5 votes vote down vote up 
def _pad_img(self, results):
        if self.size is not None:
            padded_img = mmcv.impad(results['img'], self.size)
        elif self.size_divisor is not None:
            padded_img = mmcv.impad_to_multiple(
                results['img'], self.size_divisor, pad_val=self.pad_val)
        results['img'] = padded_img
        results['pad_shape'] = padded_img.shape
        results['pad_fixed_size'] = self.size
        results['pad_size_divisor'] = self.size_divisor
Example #12
Source File: transforms.py    From Cascade-RPN with Apache License 2.0 5 votes vote down vote up 
def _pad_masks(self, results):
        pad_shape = results['pad_shape'][:2]
        for key in results.get('mask_fields', []):
            padded_masks = [
                mmcv.impad(mask, pad_shape, pad_val=self.pad_val)
                for mask in results[key]
            ]
            results[key] = np.stack(padded_masks, axis=0)
Example #13
Source File: transforms.py    From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        # aspect ratio unchanged
        if isinstance(scale_factor, float):
            masks = [
                mmcv.imrescale(mask, scale_factor, interpolation='nearest')
                for mask in masks
            ]
        # aspect ratio changed
        else:
            w_ratio, h_ratio = scale_factor[:2]
            if masks:
                h, w = masks[0].shape[:2]
                new_h = int(np.round(h * h_ratio))
                new_w = int(np.round(w * w_ratio))
                new_size = (new_w, new_h)
                masks = [
                    mmcv.imresize(mask, new_size, interpolation='nearest')
                    for mask in masks
                ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #14
Source File: transforms.py    From AugFPN with Apache License 2.0 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        masks = [
            mmcv.imrescale(mask, scale_factor, interpolation='nearest')
            for mask in masks
        ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #15
Source File: transforms.py    From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 5 votes vote down vote up 
def _pad_masks(self, results):
        pad_shape = results['pad_shape'][:2]
        for key in results.get('mask_fields', []):
            padded_masks = [
                mmcv.impad(mask, pad_shape, pad_val=self.pad_val)
                for mask in results[key]
            ]
            results[key] = np.stack(padded_masks, axis=0)
Example #16
Source File: transforms.py    From kaggle-imaterialist with MIT License 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        masks = [
            mmcv.imrescale(mask, scale_factor, interpolation='nearest')
            for mask in masks
        ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #17
Source File: transforms.py    From hrnet with MIT License 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        masks = [
            mmcv.imrescale(mask, scale_factor, interpolation='nearest')
            for mask in masks
        ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #18
Source File: transforms.py    From CenterNet with Apache License 2.0 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        masks = [
            mmcv.imrescale(mask, scale_factor, interpolation='nearest')
            for mask in masks
        ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #19
Source File: transforms.py    From ttfnet with Apache License 2.0 5 votes vote down vote up 
def _pad_img(self, results):
        if self.size is not None:
            padded_img = mmcv.impad(results['img'], self.size)
        elif self.size_divisor is not None:
            padded_img = mmcv.impad_to_multiple(
                results['img'], self.size_divisor, pad_val=self.pad_val)
        results['img'] = padded_img
        results['pad_shape'] = padded_img.shape
        results['pad_fixed_size'] = self.size
        results['pad_size_divisor'] = self.size_divisor
Example #20
Source File: transforms.py    From ttfnet with Apache License 2.0 5 votes vote down vote up 
def _pad_masks(self, results):
        pad_shape = results['pad_shape'][:2]
        for key in results.get('mask_fields', []):
            padded_masks = [
                mmcv.impad(mask, pad_shape, pad_val=self.pad_val)
                for mask in results[key]
            ]
            results[key] = np.stack(padded_masks, axis=0)
Example #21
Source File: transforms.py    From mmdetection with Apache License 2.0 5 votes vote down vote up 
def _pad_img(self, results):
        """Pad images according to ``self.size``."""
        for key in results.get('img_fields', ['img']):
            if self.size is not None:
                padded_img = mmcv.impad(
                    results[key], shape=self.size, pad_val=self.pad_val)
            elif self.size_divisor is not None:
                padded_img = mmcv.impad_to_multiple(
                    results[key], self.size_divisor, pad_val=self.pad_val)
            results[key] = padded_img
        results['pad_shape'] = padded_img.shape
        results['pad_fixed_size'] = self.size
        results['pad_size_divisor'] = self.size_divisor
Example #22
Source File: transforms.py    From kaggle-kuzushiji-recognition with MIT License 5 votes vote down vote up 
def _pad_img(self, results):
        if self.size is not None:
            padded_img = mmcv.impad(results['img'], self.size)
        elif self.size_divisor is not None:
            padded_img = mmcv.impad_to_multiple(
                results['img'], self.size_divisor, pad_val=self.pad_val)
        results['img'] = padded_img
        results['pad_shape'] = padded_img.shape
        results['pad_fixed_size'] = self.size
        results['pad_size_divisor'] = self.size_divisor
Example #23
Source File: transforms.py    From mmdetection with Apache License 2.0 5 votes vote down vote up 
def _pad_seg(self, results):
        """Pad semantic segmentation map according to
        ``results['pad_shape']``."""
        for key in results.get('seg_fields', []):
            results[key] = mmcv.impad(
                results[key], shape=results['pad_shape'][:2])
Example #24
Source File: structures.py    From mmdetection with Apache License 2.0 5 votes vote down vote up 
def pad(self, out_shape, pad_val=0):
        """See :func:`BaseInstanceMasks.pad`."""
        if len(self.masks) == 0:
            padded_masks = np.empty((0, *out_shape), dtype=np.uint8)
        else:
            padded_masks = np.stack([
                mmcv.impad(mask, shape=out_shape, pad_val=pad_val)
                for mask in self.masks
            ])
        return BitmapMasks(padded_masks, *out_shape)
Example #25
Source File: transforms.py    From AerialDetection with Apache License 2.0 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        masks = [
            mmcv.imrescale(mask, scale_factor, interpolation='nearest')
            for mask in masks
        ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #26
Source File: transforms.py    From GCNet with Apache License 2.0 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        masks = [
            mmcv.imrescale(mask, scale_factor, interpolation='nearest')
            for mask in masks
        ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #27
Source File: transforms.py    From mmdetection-annotated with Apache License 2.0 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        masks = [
            mmcv.imrescale(mask, scale_factor, interpolation='nearest')
            for mask in masks
        ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #28
Source File: transforms.py    From mmdetection_with_SENet154 with Apache License 2.0 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        masks = [
            mmcv.imrescale(mask, scale_factor, interpolation='nearest')
            for mask in masks
        ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #29
Source File: transforms.py    From PolarMask with Apache License 2.0 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        # aspect ratio unchanged
        if isinstance(scale_factor, float):
            masks = [
                mmcv.imrescale(mask, scale_factor, interpolation='nearest')
                for mask in masks
            ]
        # aspect ratio changed
        else:
            w_ratio, h_ratio = scale_factor[:2]
            if masks:
                h, w = masks[0].shape[:2]
                new_h = int(np.round(h * h_ratio))
                new_w = int(np.round(w * w_ratio))
                new_size = (new_w, new_h)
                masks = [
                    mmcv.imresize(mask, new_size, interpolation='nearest')
                    for mask in masks
                ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks
Example #30
Source File: transforms.py    From kaggle-kuzushiji-recognition with MIT License 5 votes vote down vote up 
def __call__(self, masks, pad_shape, scale_factor, flip=False):
        # aspect ratio unchanged
        if isinstance(scale_factor, float):
            masks = [
                mmcv.imrescale(mask, scale_factor, interpolation='nearest')
                for mask in masks
            ]
        # aspect ratio changed
        else:
            w_ratio, h_ratio = scale_factor[:2]
            if masks:
                h, w = masks[0].shape[:2]
                new_h = int(np.round(h * h_ratio))
                new_w = int(np.round(w * w_ratio))
                new_size = (new_w, new_h)
                masks = [
                    mmcv.imresize(mask, new_size, interpolation='nearest')
                    for mask in masks
                ]
        if flip:
            masks = [mask[:, ::-1] for mask in masks]
        padded_masks = [
            mmcv.impad(mask, pad_shape[:2], pad_val=0) for mask in masks
        ]
        padded_masks = np.stack(padded_masks, axis=0)
        return padded_masks