178 lines
5.6 KiB
Python
178 lines
5.6 KiB
Python
RGB_SCALE = 255
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CMYK_SCALE = 100
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def rgb_to_cmyk(r, g, b):
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if (r, g, b) == (0, 0, 0):
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# black
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return 0, 0, 0, CMYK_SCALE
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# rgb [0,255] -> cmy [0,1]
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c = 1 - r / RGB_SCALE
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m = 1 - g / RGB_SCALE
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y = 1 - b / RGB_SCALE
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# extract out k [0, 1]
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min_cmy = min(c, m, y)
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c = (c - min_cmy) / (1 - min_cmy)
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m = (m - min_cmy) / (1 - min_cmy)
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y = (y - min_cmy) / (1 - min_cmy)
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k = min_cmy
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# rescale to the range [0,CMYK_SCALE]
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return c * CMYK_SCALE, m * CMYK_SCALE, y * CMYK_SCALE, k * CMYK_SCALE
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def filter_image(image, classifyer):
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width, height = image.size
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for x in range(width):
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for y in range(height):
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r,g,b = image.getpixel((x,y))
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if classifyer(r,g,b):
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image.putpixel((x,y), (255,0,0))
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image.save('test.png')
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DELTA = 1
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def find_left(image,x,y,classifyer):
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left = (x,y)
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loop = True
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while loop:
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nxt = left[0], left[1] - 1
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rr,gg,bb = image.getpixel(nxt)
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if not classifyer(rr,gg,bb):
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nxt = left[0] - DELTA, left[1] -1
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rr,gg,bb = image.getpixel(nxt)
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if not classifyer(rr,gg,bb):
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nxt = left[0] + DELTA, left[1] -1
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rr,gg,bb = image.getpixel(nxt)
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if not classifyer(rr,gg,bb):
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break
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left = nxt
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return left[1]
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def find_right(image,x,y,classifyer):
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right = (x,y)
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loop = True
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while loop:
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nxt = right[0], right[1] + 1
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rr,gg,bb = image.getpixel(nxt)
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if not classifyer(rr,gg,bb):
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nxt = right[0] - DELTA, right[1] + 1
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rr,gg,bb = image.getpixel(nxt)
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if not classifyer(rr,gg,bb):
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nxt = right[0] + DELTA, right[1] + 1
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rr,gg,bb = image.getpixel(nxt)
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if not classifyer(rr,gg,bb):
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break
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right = nxt
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return right[1]
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def find_top(image, x, y, classifyer):
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top = (x,y)
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loop = True
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while loop:
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nxt = top[0] - 1, top[1]
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rr,gg,bb = image.getpixel(nxt)
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if not classifyer(rr,gg,bb):
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nxt = top[0] - 1, top[1] - DELTA
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rr,gg,bb = image.getpixel(nxt)
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if not classifyer(rr,gg,bb):
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nxt = top[0] - 1, top[1] + DELTA
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rr,gg,bb = image.getpixel(nxt)
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if not classifyer(rr,gg,bb):
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break
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top = nxt
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return top[0]
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def find_bottom(image, x, y, classifyer):
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bottom = (x,y)
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loop = True
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while loop:
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nxt = bottom[0] + 1, bottom[1]
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rr,gg,bb = image.getpixel(nxt)
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if not classifyer(rr,gg,bb):
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nxt = bottom[0] + 1, bottom[1] - DELTA
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rr,gg,bb = image.getpixel(nxt)
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if not classifyer(rr,gg,bb):
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nxt = bottom[0] + 1, bottom[1] + DELTA
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rr,gg,bb = image.getpixel(nxt)
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if not classifyer(rr,gg,bb):
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break
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bottom = nxt
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return bottom[0]
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def xy_to_bottom_right(x,y, objects):
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"When x y is inside an object, return new xy for bottom right when search can be continued"
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for topleft, bottomright in objects:
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top, left = topleft
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bottom, right = bottomright
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if top < x and x < bottom and left < y and y < right:
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return bottomright
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return (x,y)
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def merge_objects(objects):
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for pos1 in range(len(objects)-1, 0 , -1):
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for pos2 in range(len(objects)-1, 0 , -1):
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if pos1 == pos2:
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continue
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obj1 = objects[pos1]
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obj2 = objects[pos2]
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t1, l1, b1, r1 = obj1[0] + obj1[1]
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t2, l2, b2, r2 = obj2[0] + obj2[1]
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if ((t1 <= t2 and t1 >= b2) and (l1 >= l2 and l1 <= r2)) or ((b1 >= t2 and b1 <= b2) and (r1 >= l2 and r1 <= r2)):
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t = min(t1,t2)
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l = min(l1,l2)
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b = max(b1, b2)
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r = max(r1, r2)
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objects[pos2] = ((t,l),(b, r))
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objects.remove(obj1)
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break
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for pos1 in range(len(objects)-1, 0 , -1):
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obj1 = objects[pos1]
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t1, l1, b1, r1 = obj1[0] + obj1[1]
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if b1 - t1 + r1 - l1 < 20:
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objects.remove(obj1)
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return objects
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def recognize(image, classifyer, classifyer_loose):
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objects = []
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width, height = image.size
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print(f"Image: {width}x{height}")
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x = 0
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y = 0
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while x < width:
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x = xy_to_bottom_right(x,y, objects)[0]
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while y < height:
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y = xy_to_bottom_right(x,y, objects)[1]
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#print(f"Scanning {x}/{y}")
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r,g,b = image.getpixel((x,y))
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if classifyer(r,g,b):
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top = find_top(image, x, y, classifyer_loose)
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left = find_left(image, x,y, classifyer_loose)
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bottom = find_bottom(image, x,y, classifyer_loose)
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right = find_right(image, x,y, classifyer_loose)
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objects.append(((top, left), (bottom, right)))
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print(f"Found: {x}/{y} --> {top},{left} {bottom},{right}")
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y += 4
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y = 0
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x += 4
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draw = ImageDraw.Draw(image)
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objects = merge_objects(objects)
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for topleft, bottomright in objects:
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top, left = topleft
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bottom, right = bottomright
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draw.rectangle((top,left,bottom,right), outline="#ff0000")
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image.save('test.png')
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return objects
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im = Image.open("Scrns/image_7.png")
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cl_loose = lambda r,g,b: b > 80 and r < 130 and g < 130
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cl_strict = lambda r,g,b: b > 110 and r < 130 and g < 130
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#filter_image(im, cl_loose)
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recognize(im, cl_strict, cl_loose)
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exit(0) |