All done lets fucking go
Gasper Spagnolo
parent
6ced34f623
commit
bfa7eb87ad
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@ -4,7 +4,6 @@ from matplotlib import pyplot as plt
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import random
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import cv2
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import uz_framework.image as uz_image
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import UZ_utils as uz
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#######################################
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# EXCERCISE 1: Basic image processing #
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@ -146,7 +145,7 @@ def excercise_two() -> None:
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two_b('./images/bird.jpg', 100, 20)
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two_c('./images/bird.jpg', 20, 100)
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two_d()
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two_e(uz.imread_gray('./images/bird.jpg', uz.ImageType.uint8).astype(np.uint8))
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two_e(uz_image.imread_gray('./images/bird.jpg', uz_image.ImageType.uint8).astype(np.uint8))
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def two_a() -> tuple[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
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@ -295,9 +294,9 @@ def two_e(image: npt.NDArray[np.uint8]):
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######################################################
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def excercise_three() -> None:
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three_a()
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#three_a()
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#mask1, _ = three_b()
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#three_c(uz.imread('./images/bird.jpg', uz.ImageType.float64), mask1)
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#three_c(uz_image.imread('./images/bird.jpg', uz_image.ImageType.float64), mask1)
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#three_d()
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three_e()
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@ -312,7 +311,7 @@ def three_a() -> None:
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Answer: Opening = Erosion then dialation
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Closing = Dialation then erosion
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"""
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img_orig = uz.imread_gray('./images/mask.png', uz.ImageType.float64)
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img_orig = uz_image.imread_gray('./images/mask.png', uz_image.ImageType.float64)
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img_er_dil = img_orig.copy()
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img_dil_er = img_orig.copy()
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@ -386,19 +385,15 @@ def three_b():
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axs[0].imshow(original_mask, cmap='gray')
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axs[0].set(title="Original mask")
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axs[1].imshow(mask1, cmap='gray')
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axs[1].set(title="Using elipse")
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axs[2].imshow(mask2, cmap='gray')
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axs[2].set(title="Using cross")
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plt.show()
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return (mask1, mask2)
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# Ez lmao
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def three_c(image: npt.NDArray[np.float64], mask: npt.NDArray[np.uint8]):
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"""
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Write a function immask that accepts a three channel image and
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@ -406,11 +401,9 @@ def three_c(image: npt.NDArray[np.float64], mask: npt.NDArray[np.uint8]):
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corresponding pixel in the mask is equal to 0. Otherwise, the pixel value should be
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equal to the corresponding image pixel. Do not use for loops, as they are slow
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"""
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mask = np.expand_dims(mask, axis=2)
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img = uz_image.apply_mask_on_image(image, mask)
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image = mask * image
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plt.imshow(image)
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plt.imshow(img)
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plt.show()
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def three_d():
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@ -422,16 +415,13 @@ def three_d():
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would you fix that in general? (just inverting the mask if necessary doesn’t count)
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Answer:
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"""
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eagle_img_gray = uz.imread_gray('./images/eagle.jpg', uz.ImageType.uint8).astype(np.uint8)
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eagle_img_color = uz.imread('./images/eagle.jpg', uz.ImageType.float64)
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eagle_img_gray = uz_image.imread_gray('./images/eagle.jpg', uz_image.ImageType.uint8).astype(np.uint8)
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eagle_img_color = uz_image.imread('./images/eagle.jpg', uz_image.ImageType.float64)
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TRESHOLD = two_e(eagle_img_gray)
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binary_mask = eagle_img_gray.copy()
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binary_mask = np.where(binary_mask < TRESHOLD, 0, 1)
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binary_mask = uz.convert_float64_array_to_uint8_array(binary_mask)
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plt.imshow(binary_mask, cmap='gray')
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plt.show()
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binary_mask = binary_mask.astype(np.uint8)
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# If I would invert image here, then we would get crap
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# So workaround:
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@ -448,7 +438,20 @@ def three_d():
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# Now invert binary mask
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binary_mask = np.where(binary_mask == 1, 0 , 1)
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three_c(eagle_img_color, binary_mask)
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removed_background_img = uz_image.apply_mask_on_image(eagle_img_color, binary_mask)
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fig, axs = plt.subplots(1, 3)
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fig.suptitle('Removing background of Eagle')
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axs[0].imshow(eagle_img_color)
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axs[0].set(title='Original image')
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axs[1].imshow(binary_mask, cmap='gray')
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axs[1].set(title='Computed Mask')
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axs[2].imshow(removed_background_img, cmap='gray')
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axs[2].set(title='Image with removed background')
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plt.show()
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def three_e():
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"""
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@ -461,26 +464,24 @@ def three_e():
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from the original image (replace them with white background). Display the results
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"""
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# https://stackoverflow.com/a/42812226
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coin_img_gray = uz.imread_gray('./images/coins.jpg', uz.ImageType.uint8).astype(np.uint8)
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coin_img_color = uz.imread('./images/coins.jpg', uz.ImageType.float64)
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coin_img_gray = uz_image.imread_gray('./images/coins.jpg', uz_image.ImageType.uint8).astype(np.uint8)
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coin_img_color = uz_image.imread('./images/coins.jpg', uz_image.ImageType.float64)
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TRESHOLD = two_e(coin_img_gray)
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COIN_SIZE = 700
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binary_mask = coin_img_gray.copy()
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binary_mask = np.where(binary_mask < TRESHOLD, 0, 1)
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binary_mask = uz.convert_float64_array_to_uint8_array(binary_mask)
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binary_mask = binary_mask.astype(np.uint8)
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SE_CROSS = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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binary_mask = cv2.erode(binary_mask, SE_CROSS, iterations=3)
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binary_mask = cv2.dilate(binary_mask, SE_CROSS, iterations=2)
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SE_CROSS = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2, 3))
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binary_mask = cv2.dilate(binary_mask, SE_CROSS, iterations=2)
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SE_CROSS = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 2))
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binary_mask = cv2.dilate(binary_mask, SE_CROSS, iterations=2)
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# Now invert binary mask
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binary_mask = np.where(binary_mask == 1, 0 , 1)
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binary_mask = uz.convert_float64_array_to_uint8_array(binary_mask)
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binary_mask = binary_mask.astype(np.uint8)
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output = cv2.connectedComponentsWithStats(binary_mask, 4, cv2.CV_32S)
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n_blobs, im_with_separated_blobs, stats, _ = output
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@ -491,13 +492,26 @@ def three_e():
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if sizes[blob] > COIN_SIZE:
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binary_mask[im_with_separated_blobs == blob] = 0
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three_c(coin_img_color, binary_mask)
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removed_coins = uz_image.apply_mask_on_image(coin_img_color, binary_mask)
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removed_coins[removed_coins == 0] = 1
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fig, axs = plt.subplots(1, 3)
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fig.suptitle('Removal of >700px coins')
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axs[0].imshow(coin_img_color)
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axs[0].set(title='Original image')
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axs[1].imshow(binary_mask, cmap='gray')
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axs[1].set(title='Computed Mask')
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axs[2].imshow(removed_coins)
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axs[2].set(title='Image with coins removed')
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plt.show()
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def main() -> None:
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#excercise_one()
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excercise_one()
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excercise_two()
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#excercise_three()
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excercise_three()
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if __name__ == "__main__":
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main()
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@ -80,8 +80,6 @@ def transform_coloured_image_to_grayscale(image: npt.NDArray[np.float64]) -> npt
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for j in range(image.shape[1]):
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grayscale_image[i, j] = (image[i, j, 0] + image[i,j, 1] + image[i, j, 2]) / 3
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print(grayscale_image)
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print(grayscale_image.shape)
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return grayscale_image
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@ -213,3 +211,12 @@ def get_image_bins(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]
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counts = np.insert(counts, i - 1, 0)
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return counts / np.sum(counts)
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def apply_mask_on_image(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], mask: npt.NDArray[np.uint8]):
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image = image.copy()
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mask = np.expand_dims(mask, axis=2)
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image = mask * image
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return image
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