Greatly cleaned up the python script and added a dockerfile
This commit is contained in:
Gregory Campbell
+124
-96
@@ -1,114 +1,142 @@
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#!/usr/bin/env python3
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#SkinDetection.py
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"""SkinDetection.py
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# This program runs all 4 skin detection algorithms one after the other
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# Run `python3 SkinDetection.py Path/To/Image` to specify
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# inital image, removing this command arguements will result in
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# the script attempting hard coded locations so errors may arise
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# The
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Runs several skin-detection algorithms on a single image and saves results.
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"""
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from PIL import Image
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import math
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from __future__ import annotations
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import argparse
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import colorsys
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import sys
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from pathlib import Path
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from typing import Callable
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#Explicitly Defined Skin Region Model
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def EDSRModel(image_path):
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im = Image.open(image_path)
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pixels = im.load()
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rgb_im = im.convert('RGB')
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for i in range(im.size[0]):
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for j in range(im.size[1]):
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r,g,b = rgb_im.getpixel((i,j))
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if r > 95 and g > 40 and b > 20 and (max(r, g, b) - min(r, g, b)) > 15 and abs(r - g) > 15 and r > g and r > b:
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x = j
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y = i
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else:
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pixels[i,j] = (0,0,0)
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from PIL import Image
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im.save("Results/Explicitly-Defined-Skin-Region.png")
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#Colour Segmentation in Normalization rg Color Model
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def NormalizationrgModel(image_path):
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im = Image.open(image_path)
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pixels = im.load()
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rgb_im = im.convert('RGB')
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for i in range(im.size[0]):
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for j in range(im.size[1]):
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r,g,b = rgb_im.getpixel((i,j))
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R = 0.0
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G = 0.0
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B = 0.0
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if r > 0 or g > 0 or b > 0:
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R = r/float(r+g+b)
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G = g/float(r+g+b)
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B = b/float(r+g+b)
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if 0.465 >= R >= 0.36 and 0.363 >= G >= 0.28:
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x = j
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y = i
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else:
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pixels[i,j] = (0,0,0)
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def load_image(path: Path) -> Image.Image:
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image = Image.open(path)
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return image.convert("RGB")
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im.save("Results/Normalizationrg.png")
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#Color Segmentation in HSV Color Model
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def HSVModel(image_path):
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im = Image.open(image_path)
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pixels = im.load()
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rgb_im = im.convert('RGB')
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for i in range(im.size[0]):
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for j in range(im.size[1]):
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r,g,b = rgb_im.getpixel((i,j))
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r = r/255.0
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g = g/255.0
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b = b/255.0
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h,s,v = colorsys.rgb_to_hsv(r,g,b)
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h = h*255
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if 50 >= h >= 0 and 0.68 >= s >= 0.2 and 1 >= v >= 0.35:
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x = j
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y = i
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else:
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pixels[i,j] = (0,0,0)
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def save_result(image: Image.Image, path: Path) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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image.save(path)
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im.save("Results/HSV.png")
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#Color Segmentation in YCBCR Color Model
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def YCBCRModel(image_path):
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im = Image.open(image_path)
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pixels = im.load()
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ycbcr_im = im.convert('YCbCr')
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for i in range(im.size[0]):
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for j in range(im.size[1]):
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y,Cb,Cr = ycbcr_im.getpixel((i,j))
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if 142.5 >= Cb >= 97.5 and 134 >= Cr >= 17:
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pixels[i,j] = (0,0,0)
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else:
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x = i
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y = j
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def mask_image(
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image: Image.Image,
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predicate: Callable[[int, int, int], bool],
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invert: bool = False,
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) -> Image.Image:
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result = image.copy()
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pixels = result.load()
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im.save("Results/YCBCR.png")
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for x in range(result.width):
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for y in range(result.height):
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r, g, b = image.getpixel((x, y))
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if predicate(r, g, b) ^ invert:
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continue
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pixels[x, y] = (0, 0, 0)
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def main():
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args = sys.argv[1:]
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return result
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if len(sys.argv) == 1:
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image_path = "Images/skin.jpg"
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else:
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image_path = args[0]
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EDSRModel(image_path)
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NormalizationrgModel(image_path)
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HSVModel(image_path)
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YCBCRModel(image_path)
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return
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def explicitly_defined_skin_region(r: int, g: int, b: int) -> bool:
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return (
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r > 95
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and g > 40
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and b > 20
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and (max(r, g, b) - min(r, g, b)) > 15
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and abs(r - g) > 15
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and r > g
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and r > b
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)
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def normalized_rg_skin_region(r: int, g: int, b: int) -> bool:
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total = r + g + b
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if total == 0:
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return False
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R = r / float(total)
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G = g / float(total)
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return 0.36 <= R <= 0.465 and 0.28 <= G <= 0.363
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def hsv_skin_region(r: int, g: int, b: int) -> bool:
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r_norm = r / 255.0
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g_norm = g / 255.0
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b_norm = b / 255.0
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h, s, v = colorsys.rgb_to_hsv(r_norm, g_norm, b_norm)
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h *= 255
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return 0 <= h <= 50 and 0.2 <= s <= 0.68 and 0.35 <= v <= 1
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def ycbcr_skin_region(r: int, g: int, b: int) -> bool:
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y = 0.299 * r + 0.587 * g + 0.114 * b
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cb = 128 - 0.168736 * r - 0.331264 * g + 0.5 * b
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cr = 128 + 0.5 * r - 0.418688 * g - 0.081312 * b
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return 97.5 <= cb <= 142.5 and 17 <= cr <= 134
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(
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description="Run multiple skin detection algorithms on an input image."
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)
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parser.add_argument(
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"image_path",
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nargs="?",
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default="Images/skin.jpg",
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help="Path to the input image.",
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)
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parser.add_argument(
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"--output-dir",
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default="Results",
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help="Directory to write output images.",
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)
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parser.add_argument(
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"--show",
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action="store_true",
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help="Open each generated result image after processing.",
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)
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return parser.parse_args()
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def main() -> int:
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args = parse_args()
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image_path = Path(args.image_path)
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output_dir = Path(args.output_dir)
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show_results = args.show
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if not image_path.exists():
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print(f"Error: image path not found: {image_path}", file=sys.stderr)
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return 1
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try:
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image = load_image(image_path)
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except OSError as error:
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print(f"Error opening image: {error}", file=sys.stderr)
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return 1
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algorithms = [
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("Explicitly-Defined-Skin-Region.png", explicitly_defined_skin_region, False),
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("Normalizationrg.png", normalized_rg_skin_region, False),
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("HSV.png", hsv_skin_region, False),
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("YCBCR.png", ycbcr_skin_region, True),
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]
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for filename, predicate, invert in algorithms:
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output_path = output_dir / filename
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result = mask_image(image, predicate, invert=invert)
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save_result(result, output_path)
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if show_results:
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result.show(title=filename)
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print(f"Saved {len(algorithms)} results to {output_dir}")
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return 0
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if __name__ == "__main__":
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main()
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raise SystemExit(main())
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