import numpy as np

class MaskedImage:
    DSCALE = 10_000 # valeur arbitraire qui est le nombre max de la function de distance
    base = [1.0, 0.99, 0.96, 0.83, 0.38, 0.11, 0.02, 0.005, 0.0006, 0.0001, 0]
    similarity = np.interp(np.linspace(0, 1, DSCALE), np.linspace(0, 1, len(base)), base)

    def __init__(self,image=None,mask=None,width=None,height=None):
        if image is not None:
            self.image = image
            self.height, self.width = image.shape[:2]
            self.mask = mask
            return
        self.width = width
        self.height = height
        self.image = np.zeros((self.height, self.width, 3), dtype=np.uint8)
        self.mask = np.zeros((self.height, self.width), dtype=bool)
    
    def containsMask(self,x,y,patchSize):
        for dy in range(-patchSize,patchSize):
            if (y+dy < 0 or self.height <= y+dy):
                continue
            for dx in range(-patchSize,patchSize):
                if (x+dx < 0 or self.width <= x+dx):
                    continue
                if self.mask[y+dy,x+dx]:
                    return True
        return False

    def copy(self):
        return MaskedImage(image=self.image.copy(), mask=self.mask.copy())
    
    def downsample(self):
        newW, newH = self.width // 2, self.height // 2
        kernel = np.array([1, 5, 10, 10, 5, 1])
        newimage = np.zeros((newH, newW, 3), dtype=np.uint8)
        newmask = np.zeros((newH, newW), dtype=bool)
        
        for y in range(0, self.height - 1, 2):
            for x in range(0, self.width - 1, 2):
                r, g, b, ksum, m = 0, 0, 0, 0, 0
                
                for dy in range(-2, 4):
                    yk = y + dy
                    if (yk<0 or yk >= self.height):
                        continue
                    ky = kernel[2 + dy]
                    
                    for dx in range(-2, 4):
                        xk = x + dx
                        if (xk<0 or xk >= self.width):
                            continue
                        if self.mask[yk, xk]:
                            continue
                        k = kernel[2 + dx] * ky
                        r += k * self.image[yk, xk, 0]
                        g += k * self.image[yk, xk, 1]
                        b += k * self.image[yk, xk, 2]
                        ksum += k
                        m += 1
                
                if ksum > 0:
                    newimage[y // 2, x // 2] = (r // ksum, g // ksum, b // ksum)
                    newmask[y // 2, x // 2] = False
                else:
                    newmask[y // 2, x // 2] = True
        
        return MaskedImage(image=newimage, mask=newmask)

    def upscale(self, newH, newW):
        y_indices = np.floor(np.linspace(0, self.height-1, newH)).astype(int)
        x_indices = np.floor(np.linspace(0, self.width-1, newW)).astype(int)
        y_coords, x_coords = np.meshgrid(y_indices, x_indices, indexing='ij')
        newImage = MaskedImage(width=newW, height=newH)
        newImage.image = self.image[y_coords, x_coords]
        newImage.mask = self.mask[y_coords, x_coords]
        return newImage