Greatly cleaned up the python script and added a dockerfile

Dockerfile

@@ -0,0 +1,6 @@
+FROM python:3.11-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+ENTRYPOINT ["python", "SkinDetection.py"]

SkinDetection.py

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