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83 lines
3.1 KiB
83 lines
3.1 KiB
import numpy as np
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class MaskedImage:
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DSCALE = 65535
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base = [1.0, 0.99, 0.96, 0.83, 0.38, 0.11, 0.02, 0.005, 0.0006, 0.0001, 0]
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similarity = np.interp(np.linspace(0, 1, DSCALE + 1), np.linspace(0, 1, len(base)), base)
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def __init__(self,image=None,mask=None,width=None,height=None):
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if image is not None:
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self.image = image
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self.height, self.width = image.shape[:2]
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self.mask = mask
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return
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self.width = width
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self.height = height
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self.image = np.zeros((self.height, self.width, 3), dtype=np.uint8)
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self.mask = np.zeros((self.height, self.width), dtype=bool)
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def containsMask(self,x,y,patchSize):
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for dy in range(-patchSize,patchSize):
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for dx in range(-patchSize,patchSize):
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if (x+dx < 0 or self.width <= x+dx):
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continue
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if (y+dy < 0 or self.height <= y+dy):
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continue
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if self.mask[y+dy,x+dx]:
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return True
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return False
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def copy(self):
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return MaskedImage(image=self.image.copy(), mask=self.mask.copy())
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def downsample(self):
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newW, newH = self.width // 2, self.height // 2
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kernel = np.array([1, 5, 10, 10, 5, 1])
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newimage = np.zeros((newH, newW, 3), dtype=np.uint8)
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newmask = np.zeros((newH, newW), dtype=bool)
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for y in range(0, self.height - 1, 2):
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for x in range(0, self.width - 1, 2):
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r, g, b, ksum, m = 0, 0, 0, 0, 0
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for dy in range(-2, 4):
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yk = y + dy
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if (yk<0 or yk >= self.height):
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continue
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ky = kernel[2 + dy]
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for dx in range(-2, 4):
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xk = x + dx
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if (xk<0 or xk >= self.width):
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continue
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if self.mask[yk, xk]:
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continue
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k = kernel[2 + dx] * ky
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r += k * self.image[yk, xk, 0]
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g += k * self.image[yk, xk, 1]
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b += k * self.image[yk, xk, 2]
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ksum += k
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m += 1
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if ksum > 0:
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newimage[y // 2, x // 2] = [r // ksum, g // ksum, b // ksum]
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newmask[y // 2, x // 2] = False
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else:
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newmask[y // 2, x // 2] = True
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return MaskedImage(image=newimage, mask=newmask)
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def upscale(self, newH, newW):
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newImage = MaskedImage(width=newW,height=newH)
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for y in range(newH):
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for x in range(newW):
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xs = int(x*self.width/newW)
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ys = int(y*self.height/newH)
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if not self.mask[ys,xs]:
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newImage.image[y,x] = self.image[ys,xs]
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newImage.mask[y,x] = False
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else:
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newImage.mask[y,x] = True
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return newImage
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