SpikeCV/src/ImgUtils.py at main · ForthePareto/SpikeCV · GitHub

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import numpy as np
from PIL import Image

np.seterr(over='ignore')


class ImgUtils:
    @staticmethod
    def histogramEqualization(img: np.ndarray):

        shape = img.shape
        pixel_depth = 2**(img.dtype.itemsize * 8)
        # no_channel = shape[-1]
        # print(f"no_channels : {no_channel}")
        img_copy = np.copy(img)
        #  separete each color channel
        b, g, r = img_copy[:, :, 0], img_copy[:, :, 1], img_copy[:, :,
                                                                 2]
        # count each pixel value in each color channel
        r_hist = np.bincount(r.flatten())
        # print(F"r_hist.shape {r_hist.shape}")
        # print(F"r_hist.shape {r_hist[255]}")
        # print(F"r_hist.shape {(r == 255).sum()}")
        g_hist = np.bincount(g.flatten())
        b_hist = np.bincount(b.flatten())
        r_sum = 0
        g_sum = 0
        b_sum = 0
        r_cdf = [0] * pixel_depth
        g_cdf = [0] * pixel_depth
        b_cdf = [0] * pixel_depth

        # compute CDF over each channel
        for i in range(pixel_depth):
            r_sum = r_sum + r_hist[i]
            r_cdf[i] = r_sum

            g_sum = g_sum + g_hist[i]
            g_cdf[i] = g_sum

            b_sum = b_sum + b_hist[i]
            b_cdf[i] = b_sum

        # print(f"r_hist {(r_hist[-1])}")
        # print(f"r_cdf {(r_cdf[-1])}")
        # print(f"g_cdf {(g_cdf[-1])}")
        r_cdf_min = r_cdf[0]
        g_cdf_min = g_cdf[0]
        b_cdf_min = b_cdf[0]
        # print(f"b_cdf_min {(b_cdf[0])}")
        # print(f"b_cdf_min {(b_cdf_min)}")

        r_fin = [0] * pixel_depth
        g_fin = [0] * pixel_depth
        b_fin = [0] * pixel_depth
        tot_pixels = (shape[0] * shape[1])
        # compute  histogram equalization
        for i in range(pixel_depth):
            r_fin[i] = round(
                ((r_cdf[i] - r_cdf_min) /
                 (tot_pixels-r_cdf_min)) * (pixel_depth-1))
            g_fin[i] = round(
                ((g_cdf[i] - g_cdf_min) /
                 (tot_pixels-g_cdf_min)) * (pixel_depth-1))
            b_fin[i] = round(
                ((b_cdf[i] - b_cdf_min) /
                 (tot_pixels-b_cdf_min)) * (pixel_depth-1))

        # map old pixel vals
        # print(f"old r: {r}")
        # print(f"old r: {r.shape}")
        for i, pixel in np.ndenumerate(r):
            r[i] = r_fin[r[i]]
            g[i] = g_fin[g[i]]
            b[i] = b_fin[b[i]]

        # print(r_hist[0:10])
        # print(r_cdf[0:10])
        # print(r_fin[0:10])
        # print(f"new r {r}")
        # print(f"new r {r.shape}")

        img_copy[:, :, 0] = b
        img_copy[:, :, 1] = g
        img_copy[:, :, 2] = r

        # print(f"b_hist : {b_hist}")
        # print(f"g_hist : {g_hist}")
        # print(f"r_hist : {r_hist}")
        print(img_copy.shape)
        return img_copy

    @staticmethod
    def hybrid(img: np.ndarray, img2: np.ndarray) -> np.ndarray:
        # check if dimensions matche
        shape = img.shape

        if (shape == img2.shape):

            hybrid = np.zeros(shape, dtype=np.uint8)

            for i, pixel in np.ndenumerate(img):
                hybrid[i] = pixel + img2[i]
        else:
            raise Exception(f"input image must match size: {shape} ")

        return hybrid

    @staticmethod
    def globalThresholding(img: np.ndarray, threshold: int) -> np.ndarray:

        img_copy = np.copy(img)
        pixel_depth = 2**(img.dtype.itemsize * 8)

        print(f'pixel_depth: {pixel_depth}')
        print(f'shape: {img.shape}')

        if (0 <= threshold <= pixel_depth - 1):
            for i, pixel in np.ndenumerate(img_copy):
                img_copy[i] = (pixel_depth -
                               1) if pixel >= threshold else 0
        else:
            raise Exception(
                f"Threshold valuse must be in range: [0,{pixel_depth - 1}]"
            )
        return img_copy


if __name__ == '__main__':

    def imgReader(imgPath: str):
        im = Image.open(imgPath)
        im.show()
        return np.asarray(im, dtype=np.uint8)

    im1 = imgReader("./img1.jpg")
    print(f"im1 shape: {im1.shape}")
    # print(f"im1 type: {type(im1)}")

    # im2 = imgReader("./img2.jpg")
    # print(f"im2 shape: {im2.shape}")
    # print(f"im2 type: {type(im2)}")

    # im0 = np.zeros(im1.shape)
    imUtil = ImgUtils(im1)
    equalized = imUtil.histogramEqualization()
    # thresholded = imUtil.globalThresholding(250)
    # hybrid = imUtil.hybrid(im2)

    # print(f"hybrid shape: {hybrid.shape}")
    # print(f"hybrid type: {type(hybrid)}")

    im3 = Image.fromarray(equalized.astype(np.uint8))
    im3.show()