All encoding of 2 palletes possible. Current best-fit code is favoring b&w constantly.
This commit is contained in:
parent
7a366525af
commit
60881319ab
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@ -0,0 +1,168 @@
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import cv2
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import numpy as np
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from math import sqrt, pow
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def convert_color (color, color_space_a, color_space_b) :
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pixel = np.zeros([1, 1, 3], dtype=np.uint8)
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if color_space_a == 'RGB' :
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pixel = cv2.cvtColor(pixel, cv2.COLOR_BGR2RGB)
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elif color_space_a == 'LAB' :
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pixel = cv2.cvtColor(pixel, cv2.COLOR_BGR2LAB)
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elif color_space_a == 'HSV' :
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pixel = cv2.cvtColor(pixel, cv2.COLOR_BGR2HSV)
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#default is BGR
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pixel[:] = color
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if color_space_a == 'RGB' and color_space_b == 'BGR' :
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b = cv2.COLOR_RGB2BGR
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elif color_space_a == 'BGR' and color_space_b == 'RGB' :
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b = cv2.COLOR_BGR2RGB
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elif color_space_a == 'RGB' and color_space_b == 'LAB' :
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b = cv2.COLOR_RGB2LAB
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elif color_space_a == 'LAB' and color_space_b == 'RGB' :
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b = cv2.COLOR_LAB2RGB
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elif color_space_a == 'BGR' and color_space_b == 'LAB' :
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b = cv2.COLOR_BGR2LAB
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elif color_space_a == 'LAB' and color_space_b == 'BGR' :
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b = cv2.COLOR_LAB2BGR
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elif color_space_a == 'HSV' and color_space_b == 'LAB' :
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b = cv2.COLOR_HSV2LAB
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elif color_space_a == 'LAB' and color_space_b == 'HSV' :
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b = cv2.COLOR_LAB2HSV
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elif color_space_a == 'RGB' and color_space_b == 'HSV' :
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b = cv2.COLOR_RGB2HSV
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elif color_space_a == 'HSV' and color_space_b == 'RGB' :
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b = cv2.COLOR_HSV2RGB
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elif color_space_a == 'BGR' and color_space_b == 'HSV' :
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b = cv2.COLOR_BGR2HSV
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elif color_space_a == 'HSV' and color_space_b == 'BGR' :
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b = cv2.COLOR_HSV2BGR
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elif color_space_a == 'RGB' and color_space_b == 'RGB' :
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b = None
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elif color_space_a == 'BGR' and color_space_b == 'BGR' :
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b = None
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elif color_space_a == 'LAB' and color_space_b == 'LAB' :
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b = None
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elif color_space_a == 'HSV' and color_space_b == 'HSV' :
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b = None
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if b is not None :
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cvt = cv2.cvtColor(pixel, b)
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else :
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cvt = pixel
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return cvt[0, 0]
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def closest_color (colors, color):
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colors = np.array(colors)
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color = np.array(color)
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distances = np.sqrt(np.sum((colors - color) ** 2, axis=1))
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index_of_smallest = np.where(distances == np.amin(distances))
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smallest_distance = colors[index_of_smallest]
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return smallest_distance[0]
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# Works for RGB, BGR, LAB and HSV(?)
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def closest_color_euclidean (colors, color) :
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#print(len(colors))
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mDist = float('inf')
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mIdx = -1
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color = [float(i) for i in list(color)]
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for idx, comp in enumerate(colors) :
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comp = [float(i) for i in list(comp)]
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dist = euclidean_distance(comp[0], comp[1], comp[2], color[0], color[1], color[2])
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#print(f'{color} -> {comp} = {dist}')
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if dist < mDist :
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mDist = dist
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mIdx = idx
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return colors[mIdx], mDist
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def closest_color_weighted_euclidean (colors, color, space) :
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#print(len(colors))
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mDist = float('inf')
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mIdx = -1
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color = [float(i) for i in list(color)]
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for idx, comp in enumerate(colors) :
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comp = [float(i) for i in list(comp)]
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if space == 'BGR' :
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dist = weighted_euclidean_distance(comp[2], comp[1], comp[0], color[2], color[1], color[0])
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elif space == 'RGB' :
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dist = weighted_euclidean_distance(comp[0], comp[1], comp[2], color[0], color[1], color[2])
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else :
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raise Exception(f'closest_color_weighted_euclidean does not support color space {space}')
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break
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#print(f'{color} -> {comp} = {dist}')
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if dist < mDist :
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mDist = dist
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mIdx = idx
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return colors[mIdx], mDist
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def create_colored_image (width, height, bgr_color):
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image = np.zeros((height, width, 3), np.uint8)
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image[:] = bgr_color
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return image
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def remove_from_list (l, item) :
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new_array = []
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for i in l :
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if not list_match(i, item) :
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new_array.append(i)
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return new_array
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def list_match (a, b) :
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for i in range(len(a)) :
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if a[i] != b[i] :
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return False
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return True
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def rgb_euclidean_distance(rgba, rgbb) :
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return euclidean_distance(rgba[0], rgba[1], rgba[2], rgbb[0], rgbb[1], rgbb[2])
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def bgr_euclidean_distance(bgra, bgrb) :
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return euclidean_distance(bgra[2], bgra[1], bgra[0], bgrb[2], bgrb[1], bgrb[0])
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def numpy_distance (r1, g1, b1, r2, g2, b2) :
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p0 = np.array([r1, g1, b1])
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p1 = np.array([r2, g2, b2])
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d = np.linalg.norm(p0 - p1)
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return d
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#return sqrt(pow(abs(r1-r2), 2) + pow(abs(g1-g2), 2) + pow(abs(b1-b2), 2))
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def euclidean_distance (r1, g1, b1, r2, g2, b2):
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d = 0.0
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d = sqrt((r2 - r1)**2 + (g2 - g1)**2 + (b2 - b1)**2)
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return d
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def weighted_euclidean_distance (r1, g1, b1, r2, g2, b2) :
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R = 0.30
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G = 0.59
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B = 0.11
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#print(type(r1))
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return sqrt( ((r2-r1) * R)**2 + ((g2-g1) * G)**2 + ((b2-b1) * B)**2 )
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def to_luma (color, space) :
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color = [float(i) for i in list(color)]
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if space == 'RGB' :
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return rgb_to_luma(color[0], color[1], color[2])
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elif space == 'BGR' :
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return rgb_to_luma(color[2], color[1], color[0])
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def rgb_to_luma (r, g, b) :
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R = 0.2126
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G = 0.7152
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B = 0.0722
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return (r * R) + (g * G) + (b * B)
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def convertScale(img, alpha, beta):
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"""Add bias and gain to an image with saturation arithmetics. Unlike
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cv2.convertScaleAbs, it does not take an absolute value, which would lead to
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nonsensical results (e.g., a pixel at 44 with alpha = 3 and beta = -210
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becomes 78 with OpenCV, when in fact it should become 0).
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"""
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new_img = img * alpha + beta
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new_img[new_img < 0] = 0
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new_img[new_img > 255] = 255
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return new_img.astype(np.uint8)
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67
encode.py
67
encode.py
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@ -1,5 +1,7 @@
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import cv2
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from argparse import ArgumentParser
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import json
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from common import convert_color, closest_color, create_colored_image, remove_from_list, closest_color_euclidean, closest_color_weighted_euclidean, euclidean_distance, weighted_euclidean_distance
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parser = ArgumentParser()
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@ -8,18 +10,69 @@ parser.add_argument('output', help='Output bin')
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args = parser.parse_args()
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with open('palette16.json', 'r') as file:
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palette16 = json.load(file)
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with open('palettegray.json', 'r') as file:
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palettegray = json.load(file)
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def find_array_index(arrays, target):
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return next((i for i, arr in enumerate(arrays) if arr == target), -1)
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palette = []
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for a in range(16) :
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for b in range(16) :
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palette.append([ a, b ])
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#print(palette)
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#exit()
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file_path = args.output
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img = cv2.imread(args.input, cv2.IMREAD_GRAYSCALE)
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img = cv2.imread(args.input, cv2.COLOR_BGR2RGB) #, cv2.IMREAD_GRAYSCALE)
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height, width, depth = img.shape
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height, width = img.shape
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vals = []
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def compare (p) :
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score = 0
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pair = [0, 0]
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vals = []
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for i in range(height):
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for j in range(width):
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k = img[i, j]
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closest, dist = closest_color_weighted_euclidean(p, k, 'RGB')
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score += dist
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#print(closest)
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index = find_array_index(p, closest)
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if len(pair) == 2 :
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b = find_array_index(palette, pair)
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if b == -1 :
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print(f'{pair}')
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vals.append(b)
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pair = []
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for i in range(height):
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for j in range(width):
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k = img[i, j]
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vals.append(k)
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if len(pair) < 2 :
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pair.append(index)
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if len(pair) == 1 :
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pair.append(0)
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if len(pair) == 2 :
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vals.append(find_array_index(palette, pair))
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return score, vals
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score16, vals16 = compare(palette16['palette'])
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scoregray, valsgray = compare(palettegray['palette'])
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print(f'{scoregray} vs. {score16 / 3}')
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if scoregray < (score16 / 3) :
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vals = valsgray
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vals[0] = 0
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print('B&W')
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else :
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vals = vals16
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vals[0] = 1
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print('Color')
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print(f'{len(vals)} bytes')
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with open(file_path, "wb") as file:
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file.write(bytes(vals))
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{
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"palette" : [
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[255, 0, 0],
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[0, 255, 0],
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[0, 0, 255],
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[255, 255, 0],
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[255, 0, 255],
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[0, 255, 255],
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[255, 128, 0],
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[128, 0, 255],
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[0, 255, 128],
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[255, 255, 255],
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[0, 0, 0],
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[128, 128, 128],
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[128, 64, 0],
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[0, 128, 128],
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[255, 192, 203],
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[64, 128, 0]
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]
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}
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{
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"palette": [
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[0, 0, 0],
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[17, 17, 17],
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[34, 34, 34],
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[51, 51, 51],
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[68, 68, 68],
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[85, 85, 85],
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[102, 102, 102],
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[119, 119, 119],
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[136, 136, 136],
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[153, 153, 153],
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[170, 170, 170],
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[187, 187, 187],
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[204, 204, 204],
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[221, 221, 221],
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[238, 238, 238],
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[255, 255, 255]
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]
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}
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@ -37,7 +37,46 @@ var __generator = (this && this.__generator) || function (thisArg, body) {
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var Content = /** @class */ (function () {
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function Content() {
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this.width = 19;
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this.height = 14;
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this.height = 34;
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this.size = 324;
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this.palette16 = [
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[255, 0, 0],
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[0, 255, 0],
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[0, 0, 255],
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[255, 255, 0],
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[255, 0, 255],
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[0, 255, 255],
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[255, 128, 0],
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[128, 0, 255],
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[0, 255, 128],
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[255, 255, 255],
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[0, 0, 0],
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[128, 128, 128],
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[128, 64, 0],
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[0, 128, 128],
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[255, 192, 203],
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[64, 128, 0]
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];
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this.palettegray = [
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[0, 0, 0],
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[17, 17, 17],
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[34, 34, 34],
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[51, 51, 51],
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[68, 68, 68],
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[85, 85, 85],
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[102, 102, 102],
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[119, 119, 119],
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[136, 136, 136],
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[153, 153, 153],
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[170, 170, 170],
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[187, 187, 187],
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[204, 204, 204],
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[221, 221, 221],
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[238, 238, 238],
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[255, 255, 255]
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];
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this.palette = [];
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this.gray = [];
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var dpr = window.devicePixelRatio;
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this.displayCanvas = document.getElementById('display');
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this.displayCtx = this.displayCanvas.getContext('2d');
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@ -46,6 +85,16 @@ var Content = /** @class */ (function () {
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//document.getElementById('video') as HTMLCanvasElement;
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this.videoCanvas = document.createElement('canvas');
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this.videoCtx = this.videoCanvas.getContext('2d');
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for (var a = 0; a < this.palette16.length; a++) {
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for (var b = 0; b < this.palette16.length; b++) {
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this.palette.push([this.palette16[a], this.palette16[b]]);
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}
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}
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for (var a = 0; a < this.palettegray.length; a++) {
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for (var b = 0; b < this.palettegray.length; b++) {
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this.gray.push([this.palettegray[a], this.palettegray[b]]);
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}
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}
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this.startCamera();
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}
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Content.prototype.startCamera = function () {
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@ -102,8 +151,8 @@ var Content = /** @class */ (function () {
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catch (err) {
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//
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}
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if (code !== null && code.binaryData.length === 266) {
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document.querySelector('#data').innerText = code.binaryData.toString();
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if (code !== null && code.binaryData.length === this.size) {
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//(document.querySelector('#data') as HTMLDivElement).innerText = code.binaryData.toString();
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//console.dir(code);
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this.display(code.binaryData);
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}
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@ -112,12 +161,18 @@ var Content = /** @class */ (function () {
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}
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};
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Content.prototype.display = function (data) {
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for (this.i = 0; this.i < data.length; this.i++) {
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var pos = 0;
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var palette = data[0] === 0 ? this.gray : this.palette;
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for (this.i = 1; this.i < data.length; this.i++) {
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this.val = data[this.i];
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this.x = this.i % this.width;
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this.y = Math.floor(this.i / this.width);
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this.displayCtx.fillStyle = "rgba(".concat(this.val, ",").concat(this.val, ",").concat(this.val, ",1.0)");
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this.displayCtx.fillRect(this.x, this.y, 1, 1);
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for (var _i = 0, _a = palette[this.val]; _i < _a.length; _i++) {
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var color = _a[_i];
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this.x = pos % this.width;
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this.y = Math.floor(pos / this.width);
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this.displayCtx.fillStyle = "rgba(".concat(color[0], ",").concat(color[1], ",").concat(color[2], ",1.0)");
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this.displayCtx.fillRect(this.x, this.y, 1, 1);
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pos += 1;
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}
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}
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};
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return Content;
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File diff suppressed because one or more lines are too long
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@ -1,6 +1,7 @@
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class Content {
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private width : number = 19;
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private height : number = 14;
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private height : number = 34;
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private size : number = 324;
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private x : number;
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private y : number;
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@ -15,6 +16,47 @@ class Content {
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private video : HTMLVideoElement;
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private palette16 : number[][] = [
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[255, 0, 0],
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[0, 255, 0],
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[0, 0, 255],
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[255, 255, 0],
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[255, 0, 255],
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[0, 255, 255],
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[255, 128, 0],
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[128, 0, 255],
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[0, 255, 128],
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[255, 255, 255],
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[0, 0, 0],
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[128, 128, 128],
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[128, 64, 0],
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[0, 128, 128],
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[255, 192, 203],
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[64, 128, 0]
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];
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private palettegray : number[][] = [
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[0, 0, 0],
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[17, 17, 17],
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[34, 34, 34],
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[51, 51, 51],
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[68, 68, 68],
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[85, 85, 85],
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[102, 102, 102],
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[119, 119, 119],
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[136, 136, 136],
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[153, 153, 153],
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[170, 170, 170],
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[187, 187, 187],
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[204, 204, 204],
|
||||
[221, 221, 221],
|
||||
[238, 238, 238],
|
||||
[255, 255, 255]
|
||||
];
|
||||
|
||||
private palette : number[][][] = [];
|
||||
private gray : number[][][] = [];
|
||||
|
||||
constructor () {
|
||||
const dpr : number = window.devicePixelRatio;
|
||||
|
||||
|
|
@ -26,6 +68,18 @@ class Content {
|
|||
//document.getElementById('video') as HTMLCanvasElement;
|
||||
this.videoCanvas = document.createElement('canvas') as HTMLCanvasElement;
|
||||
this.videoCtx = this.videoCanvas.getContext('2d');
|
||||
|
||||
for (let a = 0; a < this.palette16.length; a++) {
|
||||
for (let b = 0; b < this.palette16.length; b++) {
|
||||
this.palette.push([this.palette16[a], this.palette16[b]]);
|
||||
}
|
||||
}
|
||||
|
||||
for (let a = 0; a < this.palettegray.length; a++) {
|
||||
for (let b = 0; b < this.palettegray.length; b++) {
|
||||
this.gray.push([this.palettegray[a], this.palettegray[b]]);
|
||||
}
|
||||
}
|
||||
|
||||
this.startCamera();
|
||||
}
|
||||
|
|
@ -80,7 +134,7 @@ class Content {
|
|||
//
|
||||
}
|
||||
|
||||
if (code !== null && code.binaryData.length === 266) {
|
||||
if (code !== null && code.binaryData.length === this.size) {
|
||||
//(document.querySelector('#data') as HTMLDivElement).innerText = code.binaryData.toString();
|
||||
//console.dir(code);
|
||||
this.display(code.binaryData);
|
||||
|
|
@ -90,12 +144,17 @@ class Content {
|
|||
}
|
||||
|
||||
display (data : any[]) {
|
||||
for (this.i = 0; this.i < data.length; this.i++) {
|
||||
let pos : number = 0;
|
||||
const palette : number[][][] = data[0] === 0 ? this.gray : this.palette;
|
||||
for (this.i = 1; this.i < data.length; this.i++) {
|
||||
this.val = data[this.i];
|
||||
this.x = this.i % this.width;
|
||||
this.y = Math.floor(this.i / this.width);
|
||||
this.displayCtx.fillStyle = `rgba(${this.val},${this.val},${this.val},1.0)`;
|
||||
this.displayCtx.fillRect(this.x, this.y, 1, 1);
|
||||
for (let color of palette[this.val]) {
|
||||
this.x = pos % this.width;
|
||||
this.y = Math.floor(pos / this.width);
|
||||
this.displayCtx.fillStyle = `rgba(${color[0]},${color[1]},${color[2]},1.0)`;
|
||||
this.displayCtx.fillRect(this.x, this.y, 1, 1);
|
||||
pos += 1
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue