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import argparse

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import functools




from typing import Tuple

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from PIL import Image




import colormath.color_conversions




import colormath.color_diff




import colormath.color_objects

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import numpy as np





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# TODO:

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#  compare to bmp2dhr and a2bestpix

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#  deal with fringing

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#  look ahead N pixels and compute all 2^N bit patterns, then minimize




# average error




#  optimize Dither.apply() critical path





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X_RES = 560




Y_RES = 192





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def srgb_to_linear_array(a: np.ndarray, gamma=2.4) > np.ndarray:




return np.where(a <= 0.04045, a / 12.92, ((a + 0.055) / 1.055) ** gamma)












def linear_to_srgb_array(a: np.ndarray, gamma=2.4) > np.ndarray:




return np.where(a <= 0.0031308, a * 12.92, 1.055 * a ** (1.0 / gamma) 




0.055)








def srgb_to_linear(im: Image) > Image:




a = np.array(im, dtype=np.float32) / 255.0




rgb_linear = srgb_to_linear_array(a, gamma=2.4)




return Image.fromarray(




(np.clip(rgb_linear, 0.0, 1.0) * 255).astype(np.uint8))












def linear_to_srgb(im: Image) > Image:




a = np.array(im, dtype=np.float32) / 255.0




srgb = linear_to_srgb_array(a, gamma = 2.4)




return Image.fromarray((np.clip(srgb, 0.0, 1.0) * 255).astype(np.uint8))












# Default bmp2dhr palette

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RGB = {




(False, False, False, False): np.array((0, 0, 0)), # Black




(False, False, False, True): np.array((148, 12, 125)), # Magenta




(False, False, True, False): np.array((99, 77, 0)), # Brown




(False, False, True, True): np.array((249, 86, 29)), # Orange




(False, True, False, False): np.array((51, 111, 0)), # Dark green

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# XXX RGB values are used as keys in DOTS dict, need to be unique




(False, True, False, True): np.array((126, 126, 125)), # Grey1

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(False, True, True, False): np.array((67, 200, 0)), # Green




(False, True, True, True): np.array((221, 206, 23)), # Yellow




(True, False, False, False): np.array((32, 54, 212)), # Dark blue




(True, False, False, True): np.array((188, 55, 255)), # Violet




(True, False, True, False): np.array((126, 126, 126)), # Grey2




(True, False, True, True): np.array((255, 129, 236)), # Pink




(True, True, False, False): np.array((7, 168, 225)), # Med blue




(True, True, False, True): np.array((158, 172, 255)), # Light blue




(True, True, True, False): np.array((93, 248, 133)), # Aqua




(True, True, True, True): np.array((255, 255, 255)), # White




}





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# OpenEmulator








# RGB = {




# (False, False, False, False): np.array((0, 0, 0)), # Black




# (False, False, False, True): np.array((189, 0, 102)), # Magenta




# (False, False, True, False): np.array((81, 86, 0)), # Brown




# (False, False, True, True): np.array((238, 55, 0)), # Orange




# (False, True, False, False): np.array((3, 135, 0)), # Dark green




# # XXX RGB values are used as keys in DOTS dict, need to be unique




# (False, True, False, True): np.array((111, 111, 111)), # Grey1




# (False, True, True, False): np.array((14, 237, 0)), # Green




# (False, True, True, True): np.array((204, 213, 0)), # Yellow




# (True, False, False, False): np.array((13, 0, 242)), # Dark blue




# (True, False, False, True): np.array((221, 0, 241)), # Violet




# (True, False, True, False): np.array((112, 112, 112)), # Grey2




# (True, False, True, True): np.array((236, 72, 229)), # Pink




# (True, True, False, False): np.array((0, 157, 241)), # Med blue




# (True, True, False, True): np.array((142, 133, 240)), # Light blue




# (True, True, True, False): np.array((39, 247, 117)), # Aqua




# (True, True, True, True): np.array((236, 236, 236)), # White




# }




sRGB = {




(False, False, False, False): np.array((0, 0, 0)), # Black




(False, False, False, True): np.array((206, 0, 123)), # Magenta




(False, False, True, False): np.array((100, 105, 0)), # Brown




(False, False, True, True): np.array((247, 79, 0)), # Orange




(False, True, False, False): np.array((0, 153, 0)), # Dark green




# XXX RGB values are used as keys in DOTS dict, need to be unique




(False, True, False, True): np.array((131, 132, 132)), # Grey1




(False, True, True, False): np.array((0, 242, 0)), # Green




(False, True, True, True): np.array((216, 220, 0)), # Yellow




(True, False, False, False): np.array((21, 0, 248)), # Dark blue




(True, False, False, True): np.array((235, 0, 242)), # Violet




(True, False, True, False): np.array((140, 140, 140)), # Grey2 # XXX




(True, False, True, True): np.array((244, 104, 240)), # Pink




(True, True, False, False): np.array((0, 181, 248)), # Med blue




(True, True, False, True): np.array((160, 156, 249)), # Light blue




(True, True, True, False): np.array((21, 241, 132)), # Aqua




(True, True, True, True): np.array((244, 247, 244)), # White

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}

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#




# # Virtual II (sRGB)




# sRGB = {




# (False, False, False, False): np.array((0, 0, 0)), # Black




# (False, False, False, True): np.array((231,36,66)), # Magenta




# (False, False, True, False): np.array((154,104,0)), # Brown




# (False, False, True, True): np.array((255,124,0)), # Orange




# (False, True, False, False): np.array((0,135,45)), # Dark green




# (False, True, False, True): np.array((104,104,104)), # Grey2 XXX




# (False, True, True, False): np.array((0,222,0)), # Green




# (False, True, True, True): np.array((255,252,0)), # Yellow




# (True, False, False, False): np.array((1,30,169)), # Dark blue




# (True, False, False, True): np.array((230,73,228)), # Violet




# (True, False, True, False): np.array((185,185,185)), # Grey1 XXX




# (True, False, True, True): np.array((255,171,153)), # Pink




# (True, True, False, False): np.array((47,69,255)), # Med blue




# (True, True, False, True): np.array((120,187,255)), # Light blue




# (True, True, True, False): np.array((83,250,208)), # Aqua




# (True, True, True, True): np.array((255, 255, 255)), # White




# }




RGB = {}




for k, v in sRGB.items():




RGB[k] = (np.clip(srgb_to_linear_array(v / 255), 0.0, 1.0) * 255).astype(




np.uint8)

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DOTS = {}




for k, v in RGB.items():




DOTS[tuple(v)] = k





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class ColourDistance:




@staticmethod




def distance(rgb1: Tuple[int], rgb2: Tuple[int]) > float:




raise NotImplementedError












class RGBDistance(ColourDistance):




"""Euclidean squared distance in RGB colour space."""








@staticmethod




def distance(rgb1: Tuple[int], rgb2: Tuple[int]) > float:




return float(np.asscalar(np.sum(np.power(np.array(rgb1)  np.array(




rgb2), 2))))












class CIE2000Distance(ColourDistance):




"""CIE2000 deltaE distance."""








@staticmethod




@functools.lru_cache(None)




def _to_lab(rgb):




srgb = np.clip(linear_to_srgb_array(np.array(rgb) / 255), 0.0,




1.0) * 255




srgb = colormath.color_objects.sRGBColor(*tuple(srgb), is_upscaled=True)




lab = colormath.color_conversions.convert_color(




srgb, colormath.color_objects.LabColor)




return lab








def distance(self, rgb1: Tuple[int], rgb2: Tuple[int]) > float:




lab1 = self._to_lab(rgb1)




lab2 = self._to_lab(rgb2)




return colormath.color_diff.delta_e_cie2000(lab1, lab2)












class CCIR601Distance(ColourDistance):




@staticmethod




def _to_luma(rgb):




return rgb[0] * 0.299 + rgb[1] * 0.587 + rgb[2] * 0.114








def distance(self, rgb1: Tuple[int], rgb2: Tuple[int]) > float:




delta_rgb = ((rgb1[0]  rgb2[0])/255, (rgb1[1]  rgb2[1])/255,




(rgb1[2]  rgb2[2])/255)




luma_diff = (self._to_luma(rgb1)  self._to_luma(rgb2)) / 255








return (




delta_rgb[0] * delta_rgb[0] * 0.299 +




delta_rgb[1] * delta_rgb[1] * 0.587 +




delta_rgb[2] * delta_rgb[2] * 0.114) * 0.75 + (




luma_diff * luma_diff)









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def find_closest_color(pixel, last_pixel, x: int):

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least_diff = 1e9




best_colour = None

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last_dots = DOTS[tuple(last_pixel)]




other_dots = list(last_dots)




other_dots[x % 4] = not other_dots[x % 4]




other_dots = tuple(other_dots)




for v in (RGB[last_dots], RGB[other_dots]):

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diff = np.sum(np.power(v  np.array(pixel), 2))




if diff < least_diff:




least_diff = diff




best_colour = v




return best_colour









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def find_closest_color(pixel, last_pixel, x: int, differ: ColourDistance):




least_diff = 1e9




best_colour = None








for v in RGB.values():




diff = differ.distance(tuple(v), pixel)




if diff < least_diff:




least_diff = diff




best_colour = v




return best_colour









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class Dither:




PATTERN = None




ORIGIN = None








def apply(self, image, x, y, quant_error):

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for offset, error_fraction in np.ndenumerate(self.PATTERN / np.sum(




self.PATTERN)):




xx = x + offset[1]  self.ORIGIN[1]




yy = y + offset[0]  self.ORIGIN[0]

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if xx < 0 or yy < 0 or xx > (X_RES // 4  1) or yy > (Y_RES  1):

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continue




new_pixel = image.getpixel((xx, yy)) + error_fraction * quant_error




image.putpixel((xx, yy), tuple(new_pixel.astype(int)))

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class FloydSteinbergDither(Dither):




# 0 * 7




# 3 5 1




PATTERN = np.array(((0, 0, 7), (3, 5, 1)))




ORIGIN = (0, 1)









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class BuckelsDither(Dither):




# 0 * 2 1




# 1 2 1 0




# 0 1 0 0




PATTERN = np.array(((0, 0, 2, 1), (1, 2, 1, 0), (0, 1, 0, 0)))

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ORIGIN = (0, 1)









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class JarvisDither(Dither):




# 0 0 X 7 5




# 3 5 7 5 3




# 1 3 5 3 1




PATTERN = np.array(((0, 0, 0, 7, 5), (3, 5, 7, 5, 3), (1, 3, 5, 3, 1)))




ORIGIN = (0, 2)












# XXX needed?




def SRGBResize(im, size, filter):




# Convert to numpy array of float




arr = np.array(im, dtype=np.float32) / 255.0




# Convert sRGB > linear




arr = np.where(arr <= 0.04045, arr / 12.92, ((arr + 0.055) / 1.055) ** 2.4)




# Resize using PIL




arrOut = np.zeros((size[1], size[0], arr.shape[2]))




for i in range(arr.shape[2]):




chan = Image.fromarray(arr[:, :, i])




chan = chan.resize(size, filter)




arrOut[:, :, i] = np.array(chan).clip(0.0, 1.0)




# Convert linear > sRGB




arrOut = np.where(arrOut <= 0.0031308, 12.92 * arrOut,




1.055 * arrOut ** (1.0 / 2.4)  0.055)




# Convert to 8bit




arrOut = np.uint8(np.rint(arrOut * 255.0))




# Convert back to PIL




return Image.fromarray(arrOut)









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def open_image(filename: str) > Image:

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im = Image.open(filename)




if im.mode != "RGB":




im = im.convert("RGB")

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# rgb_linear = srgb_to_linear(np.array(im, dtype=np.float32) / 255.0)




# im = Image.fromarray(rgb_linear * 255)




return srgb_to_linear(SRGBResize(im, (X_RES // 4, Y_RES), Image.LANCZOS))




# return SRGBResize(im, (X_RES // 4, Y_RES), Image.LANCZOS)

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def dither_image(image: Image, dither: Dither, differ: ColourDistance) > Image:

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for y in range(Y_RES):




print(y)

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new_pixel = (0, 0, 0)

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for x in range(X_RES // 4):

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old_pixel = image.getpixel((x, y))

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new_pixel = find_closest_color(old_pixel, new_pixel, x, differ)

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image.putpixel((x, y), tuple(new_pixel))




quant_error = old_pixel  new_pixel




dither.apply(image, x, y, quant_error)




return image












class Screen:




def __init__(self, image: Image):




self.bitmap = np.zeros((Y_RES, X_RES), dtype=np.bool)








self.main = np.zeros(8192, dtype=np.uint8)




self.aux = np.zeros(8192, dtype=np.uint8)








for y in range(Y_RES):

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for x in range(X_RES // 4):

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pixel = image.getpixel((x, y))




dots = DOTS[pixel]

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# phase = x % 4




# self.bitmap[y, x] = dots[phase]




self.bitmap[y, x * 4:(x + 1) * 4] = dots

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@staticmethod




def y_to_base_addr(y: int) > int:




"""Maps y coordinate to screen memory base address."""




a = y // 64




d = y  64 * a




b = d // 8




c = d  8 * b








return 1024 * c + 128 * b + 40 * a








def pack(self):




# The DHGR display encodes 7 pixels across interleaved 4byte sequences




# of AUX and MAIN memory, as follows:




# PBBBAAAA PDDCCCCB PFEEEEDD PGGGGFFF




# Aux N Main N Aux N+1 Main N+1 (N even)




main_col = np.zeros((Y_RES, X_RES // 14), dtype=np.uint8)




aux_col = np.zeros((Y_RES, X_RES // 14), dtype=np.uint8)




for byte_offset in range(80):




column = np.zeros(Y_RES, dtype=np.uint8)




for bit in range(7):




column = (self.bitmap[:, 7 * byte_offset + bit].astype(




np.uint8) << bit)




if byte_offset % 2 == 0:




aux_col[:, byte_offset // 2] = column




else:




main_col[:, (byte_offset  1) // 2] = column








for y in range(Y_RES):




addr = self.y_to_base_addr(y)




self.aux[addr:addr + 40] = aux_col[y, :]




self.main[addr:addr + 40] = main_col[y, :]

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def main():




parser = argparse.ArgumentParser()




parser.add_argument("input", type=str, help="Input file to process")

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parser.add_argument("output", type=str, help="Output file for ")

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args = parser.parse_args()

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image = open_image(args.input)





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image.show()








# dither = FloydSteinbergDither()

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# dither = BuckelsDither()

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dither = JarvisDither()








# differ = CIE2000Distance()




differ = CCIR601Distance()

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output = dither_image(image, dither, differ)




# output.show()

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screen = Screen(output)

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linear_to_srgb(output).show()




# bitmap = Image.fromarray(screen.bitmap.astype('uint8') * 255)

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screen.pack()





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with open(args.output, "wb") as f:

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f.write(screen.main)




f.write(screen.aux)

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if __name__ == "__main__":




main()
