117 lines
3.6 KiB
Python
117 lines
3.6 KiB
Python
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import logging
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import numpy as np
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import cv2
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from matplotlib import pyplot as plt
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from PIL import Image
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def show(img, win_name="temp"):
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cv2.imshow(win_name, img)
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cv2.waitKey(0)
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def crop(img):
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mid_point_h, mid_point_w = int(img.shape[0] / 2 + 30), int(img.shape[1] / 2)
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img_roi = img[mid_point_h - 520: mid_point_h + 520, mid_point_w - 340: mid_point_w + 340]
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return img_roi
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class Layer(object):
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def __init__(self):
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self._layer = []
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@property
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def layer(self):
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return self._layer
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def insert(self, layer_instance):
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if layer_instance['name'] == 'body':
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self._body = layer_instance
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self._layer.append(layer_instance)
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def sort(self, priority):
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self._layer.sort(key=lambda x: priority[x['name']])
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# def merge(self, cfg):
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# """
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# opencv shape order (height, width, channel)
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# image coordinate system:
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# |------------->x (width)
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# |
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# |
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# |
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# y (height)
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# Returns:
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#
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#
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# """
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# base_image = Image.new('RGBA', self._layer[1]['image'].size, (0, 0, 0, 0))
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# for layer in self._layer:
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# y, x = layer['position']
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# base_image.paste(layer['image'], (x, y), layer['image'])
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# # base_image.show()
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#
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# for x in self._layer:
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# if np.all(x['mask'] == 0):
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# continue
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# # obtain region of interest about roi(roi) and item-image(roi_image, roi_mask)
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# roi, roi_mask, roi_image, signal = self.get_roi(dst=dst, image=x)
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# temp_bg = np.expand_dims(cv2.bitwise_not(roi_mask), axis=2).repeat(3, axis=2)
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# tmp1 = (roi * (temp_bg / 255)).astype(np.uint8)
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# temp_fg = np.expand_dims(roi_mask, axis=2).repeat(3, axis=2)
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# tmp2 = (roi_image * (temp_fg / 255)).astype(np.uint8)
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#
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# roi[:] = cv2.add(tmp1, tmp2)
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# # show(cv2.resize(dst, (int(dst.shape[1] * 0.5), int(dst.shape[0] * 0.5)), interpolation=cv2.INTER_AREA),
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# # win_name=x.get('name'))
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# # crop image and get the central part
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# if cfg.get('basic')['self_template'] == False:
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# dst_roi = crop(dst)
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# else:
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# dst_roi = dst
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# return dst_roi, signal
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#
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# @staticmethod
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# def get_roi(dst, image):
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# signal = False
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# dst_y, dst_x = dst.shape[:2]
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# roi_height, roi_width = image['mask'].shape
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# roi_y0, roi_x0 = image['position']
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#
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# if roi_y0 < 0:
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# roi_yin = 0
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# mask_yin = -roi_y0
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# signal = True
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# else:
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# roi_yin = roi_y0
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# mask_yin = 0
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# if roi_y0 + roi_height > dst_y:
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# roi_yout = dst_y
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# mask_yout = dst_y - roi_y0
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# signal = True
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# else:
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# roi_yout = roi_height + roi_y0
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# mask_yout = roi_height
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# # x part
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# if roi_x0 < 0:
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# roi_xin = 0
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# mask_xin = -roi_x0
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# signal = True
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# else:
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# roi_xin = roi_x0
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# mask_xin = 0
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# if roi_x0 + roi_width > dst_x:
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# roi_xout = dst_x
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# mask_xout = dst_x - roi_x0
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# signal = True
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# else:
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# roi_xout = roi_width + roi_x0
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# mask_xout = roi_width
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#
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# roi = dst[roi_yin: roi_yout, roi_xin: roi_xout]
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# roi_mask = image['mask'][mask_yin: mask_yout, mask_xin: mask_xout]
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# roi_image = image['image'][mask_yin: mask_yout, mask_xin: mask_xout]
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# return roi, roi_mask, roi_image, signal
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