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import random |
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import cv2 |
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import numpy as np |
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from .utils import convert_to_numpy |
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class RegionCanvasAnnotator: |
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def __init__(self, cfg, device=None): |
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self.scale_range = cfg.get('SCALE_RANGE', [0.75, 1.0]) |
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self.canvas_value = cfg.get('CANVAS_VALUE', 255) |
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self.use_resize = cfg.get('USE_RESIZE', True) |
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self.use_canvas = cfg.get('USE_CANVAS', True) |
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self.use_aug = cfg.get('USE_AUG', False) |
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if self.use_aug: |
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from .maskaug import MaskAugAnnotator |
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self.maskaug_anno = MaskAugAnnotator(cfg={}) |
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def forward(self, image, mask, mask_cfg=None): |
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image = convert_to_numpy(image) |
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mask = convert_to_numpy(mask) |
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image_h, image_w = image.shape[:2] |
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if self.use_aug: |
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mask = self.maskaug_anno.forward(mask, mask_cfg) |
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image[np.array(mask) == 0] = self.canvas_value |
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x, y, w, h = cv2.boundingRect(mask) |
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region_crop = image[y:y + h, x:x + w] |
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if self.use_resize: |
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scale_min, scale_max = self.scale_range |
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scale_factor = random.uniform(scale_min, scale_max) |
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new_w, new_h = int(image_w * scale_factor), int(image_h * scale_factor) |
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obj_scale_factor = min(new_w/w, new_h/h) |
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new_w = int(w * obj_scale_factor) |
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new_h = int(h * obj_scale_factor) |
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region_crop_resized = cv2.resize(region_crop, (new_w, new_h), interpolation=cv2.INTER_AREA) |
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else: |
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region_crop_resized = region_crop |
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if self.use_canvas: |
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new_canvas = np.ones_like(image) * self.canvas_value |
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max_x = max(0, image_w - new_w) |
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max_y = max(0, image_h - new_h) |
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new_x = random.randint(0, max_x) |
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new_y = random.randint(0, max_y) |
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new_canvas[new_y:new_y + new_h, new_x:new_x + new_w] = region_crop_resized |
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else: |
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new_canvas = region_crop_resized |
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return new_canvas |
