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numpy image

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Innocent Iguana answered on July 4, 2020 Popularity 9/10 Helpfulness 4/10

answer numpy image

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numpy image

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# Extracting color channels
red_array = img_array[:, :, 0]
blue_array = img_array[:, :, 1]
green_array = img_array[:, :, 2]

# Flipping image array in all dimensions
flipped_img = np.flip(img_array)

# Flip specified dimensions
# axis 0 is row, axis 1 is column, axis 2 is channel or color
flipped_img2 = np.flip(img_array, axis=(0, 1)) # Flip only column and row

# Transpose image dimensions
# Transposing will swap dimensions
transposed_img = np.transpose(img_array, axes=(1, 0, 2)) # Swapping first and second dimension

# Split image channels
splitting_parts = 3
dimension_index_to_split = 2
red_array, green_array, blue_array = np.split(img_array, splitting_parts, axis=dimension_index_to_split)
red_array = red_array.reshape((3, 3))

# Stacking color channels to create rgb image
new_dimension_index = 2
stacked_rgb = np.stack([red_array, green_array, blue_array], axis=2)

# Show image
plt.imshow(img_array)
plt.show()

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Source: Grepper

Tags: image numpy python

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Contributed on Nov 18 2023

Innocent Iguana

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open image in numpy

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image = PIL.Image.open(pathToImage)
frame = numpy.asarray(image)

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Source: Grepper

Tags: image imag

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Contributed on Jul 04 2020

Agreeable Angelfish

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numpy normalize image

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def normalize_images(images):
    # initial zero ndarray
    normalized_images = np.zeros_like(images.astype(float))

    # The first images index is number of images where the other indices indicates
    # hieight, width and depth of the image
    num_images = images.shape[0]

    # Computing the minimum and maximum value of the input image to do the normalization based on them
    maximum_value, minimum_value = images.max(), images.min()

    # Normalize all the pixel values of the images to be from 0 to 1
    for img in range(num_images):
        normalized_images[img, ...] = (images[img, ...] - float(minimum_value)) / float(maximum_value - minimum_value)

    return normalized_images


# encoding the input images. Each image will be represented by a vector of zeros except for the class index of the image
# that this vector represents. The length of this vector depends on number of classes that we have
# the dataset which is 10 in CIFAR-10

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Source: www.programcreek.com

Tags: image imag

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Contributed on Aug 01 2022

Phuong Anh Dang

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numpy image

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numpy image

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numpy normalize image