started text filter

sobel.py

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+import scipy.ndimage as nd
+import imageio.v3 as iio
+import numpy as np
+import math
+from PIL import Image, ImageFilter
+
+IMAGE_PATH = 'engine.png'
+
+BLUR_STRENGTH_1 = 1
+BLUR_STRENGTH_2 = 3
+
+DOG_THRESHOLD = 8
+
+sobel_x_kernel = ImageFilter.Kernel((3, 3),
+                                    (
+                                        1, 0, -1,
+                                        2, 0, -2,
+                                        1, 0, -1
+                                    ))
+
+sobel_y_kernel = ImageFilter.Kernel((3, 3),
+                                    (
+                                        1, 2, 1,
+                                        0, 0, 0,
+                                        -1, -2, -1
+                                    ))
+
+
+def main():
+
+    image = Image.open(IMAGE_PATH)
+
+    # image = image.convert('L')
+
+    blur_1 = image.filter(ImageFilter.GaussianBlur(BLUR_STRENGTH_1))
+    blur_2 = image.filter(ImageFilter.GaussianBlur(BLUR_STRENGTH_2))
+
+    dog: Image = difference_of_gaussians(blur_1, blur_2)
+
+    sb = sobel(image)
+
+    sb.save('sobel-pil.png')
+
+    sb = sobel(dog)
+
+    sb.save('sobel-dog.png')
+
+    # dog.save('dog.png')
+
+    # sb_x = dog.filter(sobel_x_kernel)
+
+    # sb_x.save('pil-sobel_x.png')
+
+    # image_np = np.array(dog)
+
+    # sb_x = nd.sobel(image_np, 1)
+    # sb_y = nd.sobel(image_np, 0)
+
+    # combined_sobel = np.sqrt(np.square(sb_x) + np.square(sb_y))
+
+    # i = Image.fromarray(combined_sobel, 'L')
+
+    # i.save('sobel.png')
+
+    # iio.imwrite('sobel_x.png', sb_x)
+    # iio.imwrite('sobel_y.png', sb_y)
+
+
+def subtract_colors(t1: tuple, t2: tuple) -> tuple:
+
+    if type(t1) != tuple:
+        if (t2 - t1) >= DOG_THRESHOLD:
+            return t2 - t1
+        else:
+            return 0
+
+    if len(t1) != len(t2):
+        print('Len of first tuple should equal second tuple probably')
+        exit(1)
+
+    ans = []
+    for i, n in enumerate(t1):
+        ans.append(t2[i] - n)
+
+    return tuple(ans)
+
+def difference_of_gaussians(blur_1: Image, blur_2: Image) -> Image:
+
+    w, h = blur_1.size
+
+    dog = Image.new(blur_1.mode, blur_1.size)
+
+    for pixel_y in range(0, h):
+        for pixel_x in range(0, w):
+
+            coords = (pixel_x, pixel_y)
+
+            dog.putpixel(coords, subtract_colors(blur_2.getpixel(coords), blur_1.getpixel(coords)))
+
+    return dog
+
+# Taken from
+# https://enzoftware.github.io/posts/image-filter-python
+def sobel(img: Image) -> Image:
+    width, height = img.size
+
+    newimg = Image.new("RGB", (width, height), "white")
+    for x in range(1, width-1):  # ignore the edge pixels for simplicity (1 to width-1)
+        for y in range(1, height-1): # ignore edge pixels for simplicity (1 to height-1)
+
+            # initialise Gx to 0 and Gy to 0 for every pixel
+            Gx = 0
+            Gy = 0
+
+            # top left pixel
+            p = img.getpixel((x-1, y-1))
+            r = p[0]
+            g = p[1]
+            b = p[2]
+
+            # intensity ranges from 0 to 765 (255 * 3)
+            intensity = r + g + b
+
+            # accumulate the value into Gx, and Gy
+            Gx += -intensity
+            Gy += -intensity
+
+            # remaining left column
+            p = img.getpixel((x-1, y))
+            r = p[0]
+            g = p[1]
+            b = p[2]
+
+            Gx += -2 * (r + g + b)
+
+            p = img.getpixel((x-1, y+1))
+            r = p[0]
+            g = p[1]
+            b = p[2]
+
+            Gx += -(r + g + b)
+            Gy += (r + g + b)
+
+            # middle pixels
+            p = img.getpixel((x, y-1))
+            r = p[0]
+            g = p[1]
+            b = p[2]
+
+            Gy += -2 * (r + g + b)
+
+            p = img.getpixel((x, y+1))
+            r = p[0]
+            g = p[1]
+            b = p[2]
+
+            Gy += 2 * (r + g + b)
+
+            # right column
+            p = img.getpixel((x+1, y-1))
+            r = p[0]
+            g = p[1]
+            b = p[2]
+
+            Gx += (r + g + b)
+            Gy += -(r + g + b)
+
+            p = img.getpixel((x+1, y))
+            r = p[0]
+            g = p[1]
+            b = p[2]
+
+            Gx += 2 * (r + g + b)
+
+            p = img.getpixel((x+1, y+1))
+            r = p[0]
+            g = p[1]
+            b = p[2]
+
+            Gx += (r + g + b)
+            Gy += (r + g + b)
+
+            # calculate the length of the gradient (Pythagorean theorem)
+            length = math.sqrt((Gx * Gx) + (Gy * Gy))
+
+            # normalise the length of gradient to the range 0 to 255
+            length = length / 4328 * 255
+
+            length = int(length)
+
+            # draw the length in the edge image
+            #newpixel = img.putpixel((length,length,length))
+            newimg.putpixel((x,y),(length,length,length))
+
+    return newimg
+
+if __name__ == '__main__':
+    main()
+
+
+# image = iio.imread(IMAGE_PATH)
+
+# sb_x = nd.sobel(image, 1)
+# sb_y = nd.sobel(image, 0)
+
+
+# iio.imwrite('sobel_x.png', sb_x)
+# iio.imwrite('sobel_y.png', sb_y)
+
+
+
+
+