450 lines
19 KiB
Python
450 lines
19 KiB
Python
class NumpyUtility:
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def __init__(self):
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'''Utilitys made with chatGPT for experimentation, few are working'''
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def compress_dynamic_range(image):
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# Find the 1st and 99th percentiles of the image
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p1, p99 = np.percentile(image, (1, 99))
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# Calculate the range of the image
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img_range = p99 - p1
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# Calculate the compression factor required to fit the image into 8-bit range
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c = 1 / img_range
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# Subtract the 1st percentile from the image and clip it to the [0, 1] range
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new_image = np.clip((image - p1) * c, 0, 1)
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# Convert the image to uint8 format
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new_image = new_image.astype(np.uint8)
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return new_image
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def adjust_luminance(image, mask, amount):
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# Convert the image to LAB color space
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rgb_image = image.astype(np.float32) / 255.0
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xyz_image = np.dot(rgb_image, np.array([[0.412453, 0.357580, 0.180423],
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[0.212671, 0.715160, 0.072169],
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[0.019334, 0.119193, 0.950227]]))
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xyz_image = np.clip(xyz_image, 0, 1)
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lab_image = np.zeros_like(xyz_image)
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lab_image[..., 0] = 116.0 * np.power(xyz_image[..., 1], 1 / 3.0) - 16.0
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lab_image[..., 1] = 500.0 * (np.power(xyz_image[..., 0], 1 / 3.0) - np.power(xyz_image[..., 1], 1 / 3.0))
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lab_image[..., 2] = 200.0 * (np.power(xyz_image[..., 1], 1 / 3.0) - np.power(xyz_image[..., 2], 1 / 3.0))
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# Apply the luminance adjustment to the masked area
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lab_image[..., 0][mask == 1] = np.clip(lab_image[..., 0][mask == 1] * (1 + amount), 0, 100)
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# Convert the image back to RGB color space
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xyz_image[..., 1] = np.power((lab_image[..., 0] + 16.0) / 116.0, 3)
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xyz_image[..., 0] = np.power((lab_image[..., 0] + 16.0) / 116.0 + lab_image[..., 1] / 500.0, 3)
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xyz_image[..., 2] = np.power((lab_image[..., 0] + 16.0) / 116.0 - lab_image[..., 2] / 200.0, 3)
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rgb_image = np.dot(xyz_image, np.array([[3.240479, -1.537150, -0.498535],
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[-0.969256, 1.875992, 0.041556],
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[0.055648, -0.204043, 1.057311]]))
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# Convert the image back to the range [0, 255]
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rgb_image = np.clip(rgb_image, 0, 1) * 255.0
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rgb_image = rgb_image.astype(np.uint8)
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return rgb_image
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def gaussian_filter(mask, sigma):
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"""
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Apply Gaussian filtering to a binary mask.
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Parameters:
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mask (numpy.ndarray): Binary mask to apply filtering on.
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sigma (float): Standard deviation of the Gaussian kernel.
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Returns:
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numpy.ndarray: Binary mask after Gaussian filtering.
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"""
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# Create a Gaussian kernel with the given sigma
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ksize = int(2 * np.ceil(2 * sigma) + 1)
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kernel = np.zeros((ksize, ksize))
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for i in range(ksize):
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for j in range(ksize):
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kernel[i, j] = np.exp(-((i - ksize // 2) ** 2 + (j - ksize // 2) ** 2) / (2 * sigma ** 2))
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kernel /= np.sum(kernel)
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# Apply convolution with the kernel
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filtered = np.zeros_like(mask, dtype='float32')
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for i in range(mask.shape[0]):
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for j in range(mask.shape[1]):
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roi = mask[max(i - ksize // 2, 0):min(i + ksize // 2 + 1, mask.shape[0]),
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max(j - ksize // 2, 0):min(j + ksize // 2 + 1, mask.shape[1])]
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filtered[i, j] = np.sum(roi * kernel[:roi.shape[0], :roi.shape[1]])
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return filtered
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def binary_opening(mask, kernel):
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"""
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Perform binary morphological opening on a binary mask using a structuring element.
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Parameters:
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mask (numpy.ndarray): Binary mask to perform opening on.
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kernel (numpy.ndarray): Structuring element used for opening.
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Returns:
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numpy.ndarray: Binary mask after morphological opening.
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"""
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# Create padding on all sides of the mask
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pad_width = [(kernel.shape[i] // 2, kernel.shape[i] // 2) for i in range(kernel.ndim)]
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mask_padded = np.pad(mask, pad_width, mode='constant', constant_values=0)
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# Apply morphological erosion using the kernel
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eroded = np.zeros_like(mask_padded)
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for i in range(mask_padded.ndim):
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eroded = np.maximum(eroded, np.apply_along_axis(np.roll, i, mask_padded, shift=-kernel.shape[i] // 2))
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eroded = eroded[pad_width[0][0]:-pad_width[0][1], pad_width[1][0]:-pad_width[1][1]]
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# Create padding on all sides of the eroded mask
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pad_width = [(kernel.shape[i] // 2, kernel.shape[i] // 2) for i in range(kernel.ndim)]
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eroded_padded = np.pad(eroded, pad_width, mode='constant', constant_values=0)
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# Apply morphological dilation using the kernel
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dilated = np.zeros_like(eroded_padded)
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for i in range(eroded_padded.ndim):
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dilated = np.minimum(dilated, np.apply_along_axis(np.roll, i, eroded_padded, shift=kernel.shape[i] // 2))
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dilated = dilated[pad_width[0][0]:-pad_width[0][1], pad_width[1][0]:-pad_width[1][1]]
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return dilated
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def binary_closing(mask, kernel):
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"""
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Perform binary morphological closing on a binary mask using a structuring element.
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Parameters:
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mask (numpy.ndarray): Binary mask to perform closing on.
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kernel (numpy.ndarray): Structuring element used for closing.
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Returns:
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numpy.ndarray: Binary mask after morphological closing.
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"""
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# Create padding on all sides of the mask
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pad_width = [(kernel.shape[i] // 2, kernel.shape[i] // 2) for i in range(kernel.ndim)]
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mask_padded = np.pad(mask, pad_width, mode='constant', constant_values=0)
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# Apply morphological dilation using the kernel
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dilated = np.zeros_like(mask_padded)
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for i in range(mask_padded.ndim):
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dilated = np.minimum(dilated, np.apply_along_axis(np.roll, i, mask_padded, shift=kernel.shape[i] // 2))
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dilated = dilated[pad_width[0][0]:-pad_width[0][1], pad_width[1][0]:-pad_width[1][1]]
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# Create padding on all sides of the dilated mask
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pad_width = [(kernel.shape[i] // 2, kernel.shape[i] // 2) for i in range(kernel.ndim)]
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dilated_padded = np.pad(dilated, pad_width, mode='constant', constant_values=0)
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# Apply morphological erosion using the kernel
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eroded = np.zeros_like(dilated_padded)
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for i in range(dilated_padded.ndim):
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eroded = np.maximum(eroded, np.apply_along_axis(np.roll, i, dilated_padded, shift=-kernel.shape[i] // 2))
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eroded = eroded[pad_width[0][0]:-pad_width[0][1], pad_width[1][0]:-pad_width[1][1]]
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return eroded
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def create_shadow_mask(image, threshold=80, range_width=50):
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lab_image = np.apply_along_axis(lambda x: np.dot([0.2126, 0.7152, 0.0722], x), 2, image).astype('float64')
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luminance_range = np.max(lab_image) - np.min(lab_image)
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if luminance_range < range_width:
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range_width = luminance_range
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threshold_min = np.min(lab_image) + range_width / 2
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threshold_max = np.max(lab_image) - range_width / 2
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if threshold < threshold_min:
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threshold = threshold_min
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elif threshold > threshold_max:
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threshold = threshold_max
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mask = np.logical_and(lab_image >= threshold - range_width / 2, lab_image <= threshold + range_width / 2)
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if np.sum(mask) == 0:
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return np.zeros_like(mask).astype(float)
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else:
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center_line = np.nanmean(np.where(mask, lab_image, np.nan), axis=0)
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x = np.arange(center_line.shape[0])
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slope = np.zeros(center_line.shape)
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slope[1:-1] = (center_line[2:] - center_line[:-2]) / 2
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slope[0] = slope[1]
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slope[-1] = slope[-2]
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intercept = center_line - slope * x
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x = np.arange(image.shape[1]) # x-coordinates of pixels
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y = np.arange(image.shape[0]) # y-coordinates of pixels
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print(y)
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x, y = np.meshgrid(x, y) # create 2D arrays of x- and y-coordinates
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# compute distances from each pixel to the shadow line
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dist = np.abs((y[:, :, np.newaxis] - slope[np.newaxis, np.newaxis, :] * x[:, :, np.newaxis]
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- intercept[np.newaxis, np.newaxis, :]) / np.sqrt(1 + slope[np.newaxis, np.newaxis, :] ** 2))
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print(dist)
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# sigma = np.nanmedian(dist)/0.6745
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# mask = np.exp(-0.5*(dist/sigma)**2)
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return dist
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def highpass_mask(img, cutoff, order=1):
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# Calculate the Fourier transform of the image
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fft_img = np.fft.fft2(img)
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# Shift the zero-frequency component to the center of the spectrum
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fft_img = np.fft.fftshift(fft_img)
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# Construct a highpass filter in the Fourier domain
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x, y = np.meshgrid(np.linspace(-1, 1, img.shape[1]), np.linspace(-1, 1, img.shape[0]))
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r = np.sqrt(x ** 2 + y ** 2)
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hp_filter = 1 - 1 / (1 + (cutoff / r) ** (2 * order))
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# Apply the highpass filter to the Fourier transform of the image
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fft_img_hp = fft_img * hp_filter
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# Shift the zero-frequency component back to the corners of the spectrum
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fft_img_hp = np.fft.ifftshift(fft_img_hp)
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# Calculate the inverse Fourier transform of the highpass-filtered image
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img_hp = np.fft.ifft2(fft_img_hp)
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# Take the absolute value of the real part of the inverse Fourier transform
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img_hp = np.abs(np.real(img_hp))
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# Normalize the highpass-filtered image to the range [0, 1]
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img_hp_norm = img_hp / np.max(img_hp)
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# Invert the highpass-filtered image to create a highpass mask
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mask = 1 - img_hp_norm
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return mask
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def cvtColor(hsv_img):
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# Assuming you have an HSV image loaded in the variable 'hsv_img'
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# Let's convert the image back to RGB color space
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h, s, v = np.split(hsv_img, 3, axis=-1)
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h = h.reshape(h.shape[:2])
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s = s.reshape(s.shape[:2])
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v = v.reshape(v.shape[:2])
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c = v * s
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x = c * (1 - np.abs((h / 60) % 2 - 1))
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m = v - c
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z = np.zeros_like(h)
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# Set up the RGB channels according to the hue value
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rgb_img = np.dstack((
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np.where((0 <= h) & (h < 60), c,
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np.where((120 <= h) & (h < 180), z, np.where((240 <= h) & (h < 300), x, m))),
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np.where((300 <= h), c, np.where((60 <= h) & (h < 120), x, np.where((180 <= h) & (h < 240), c, m))),
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np.where((0 <= h) & (h < 360), v, m)
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))
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return rgb_img
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def saturate(img, amount):
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hsv_img = np.copy(img)
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hsv_img = np.asarray(hsv_img, dtype=np.float32) / 255.0 # Normalize pixel values
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hsv_img = np.clip(hsv_img, 0, 1) # Clip values to the range [0, 1]
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hsv_img = np.squeeze(cv2.cvtColor(hsv_img, cv2.COLOR_RGB2HSV_FULL)) # Convert to HSV
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# Let's adjust the saturation of the image
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saturation_factor = 1.5 # Adjust this value to your preference
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hsv_img[..., 1] *= saturation_factor
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# Now let's convert the image back to RGB color space
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hsv_img = np.clip(hsv_img, 0, 255) # Clip values to the range [0, 255]
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hsv_img = np.asarray(hsv_img, dtype=np.uint8) # Convert back to uint8
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rgb_img = cv2.cvtColor(hsv_img, cv2.COLOR_HSV2RGB_FULL) # Convert back to RGB
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# The resulting image with increased saturation is stored in the 'rgb_img' variable
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def rgb2ycbcr(rgb_img):
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# Create the conversion matrix for RGB to YCbCr
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conv_mat = np.array([[0.299, 0.587, 0.114],
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[-0.168736, -0.331264, 0.5],
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[0.5, -0.418688, -0.081312]])
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# Reshape the input image to a 2D array of pixels
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pixels = rgb_img.reshape(-1, 3)
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# Apply the conversion matrix to the pixels
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ycbcr_pixels = np.dot(pixels, conv_mat.T)
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# Reshape the converted pixels back into the original image shape
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ycbcr_img = ycbcr_pixels.reshape(rgb_img.shape)
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# Convert the image data type to uint8 and return it
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return ycbcr_img.astype(np.uint8)
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def ycbcr2rgb(ycbcr_img):
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# Create the conversion matrix for YCbCr to RGB
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conv_mat = np.array([[1.0, 0.0, 1.402],
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[1.0, -0.344136, -0.714136],
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[1.0, 1.772, 0.0]])
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# Reshape the input image to a 2D array of pixels
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pixels = ycbcr_img.reshape(-1, 3)
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# Apply the conversion matrix to the pixels
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rgb_pixels = np.dot(pixels, conv_mat.T)
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# Reshape the converted pixels back into the original image shape
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rgb_img = rgb_pixels.reshape(ycbcr_img.shape)
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# Convert the image data type to uint8 and return it
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return rgb_img.astype(np.uint8)
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def adjust_luminance(img, mask, adjustment):
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# Convert the input image to the YCbCr color space
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img = (255.0 * img).astype(np.uint8)
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mask = (255.0 * mask).astype(np.uint8)
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ycbcr_img = rgb2ycbcr(img)
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ycbcr_mask = rgb2ycbcr(mask)
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print(ycbcr_mask)
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print(ycbcr_img)
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plot_histogram(ycbcr_img, title="ycbcr")
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plot_histogram(ycbcr_mask, title="ycmask")
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# Separate the luminance channel (Y)
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y_channel = ycbcr_img[..., 0]
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y_channel_mask = ycbcr_mask[..., 0]
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# Apply the adjustment to the luminance channel using the mask
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y_adjusted = np.clip(y_channel + (adjustment * y_channel_mask), 0, 255).astype(np.uint8)
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# Replace the original luminance channel with the adjusted one
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ycbcr_img[..., 0] = y_adjusted
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# Convert the image back to the RGB color space and return it
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return ycbcr2rgb(ycbcr_img)
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def tonemap_reinhard(image, gamma=2.2, intensity=0.18, light_adapt=0.8):
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"""
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Tonemaps the input HDR image using the Reinhard algorithm.
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Args:
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image: The input HDR image as a NumPy array.
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gamma: The gamma correction value to apply to the output image.
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intensity: The target scene brightness.
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light_adapt: The adaptation rate for the brightness.
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Returns:
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The tonemapped output image as a NumPy array.
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"""
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# Convert the image to floating point RGB.
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#image = image.astype(np.float32)
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# Compute the log average luminance.
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lum = np.exp(np.mean(np.log(0.0001 + image)))
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# Normalize the image by the log average luminance.
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image /= lum
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# Apply the Reinhard tonemapping algorithm.
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mapped = np.zeros_like(image)
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mapped = intensity * (mapped / np.max(mapped))
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mapped = light_adapt * (mapped * (1 + mapped / np.max(mapped) ** 2)) / (1 + mapped)
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mapped *= lum
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# Apply gamma correction to the tonemapped image.
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mapped = np.power(np.clip(mapped, 0, 1), 1 / gamma)
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# Convert the tonemapped image to 8-bit RGB.
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mapped = (255 * mapped).astype(np.uint8)
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return mapped
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def f(t):
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# Helper function to compute the nonlinear transformation function for the LAB color space
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delta = 6.0 / 29.0
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t_thresh = delta ** 3
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return np.where(t > t_thresh, t ** (1 / 3), (1 / 3) * (29 / 6) ** 2 * t + 4 / 29)
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def numpy_lab2rgb(lab_img):
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XYZ = np.zeros_like(lab_img, dtype=np.float32)
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XYZ[..., 0] = (lab_img[..., 0] + 16.0) / 116.0
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XYZ[..., 1] = (lab_img[..., 1] / 500.0) + XYZ[..., 0]
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XYZ[..., 2] = XYZ[..., 0] - (lab_img[..., 2] / 200.0)
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mask = XYZ > 0.2068966
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XYZ[mask] = XYZ[mask] ** 3
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XYZ[~mask] = (XYZ[~mask] - 16.0 / 116.0) / 7.787
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D50 = np.array([0.9642, 1.0, 0.8249], dtype=np.float32)
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RGB_linear = np.dot(XYZ,
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np.array([[3.2406, -1.5372, -0.4986], [-0.9689, 1.8758, 0.0415], [0.0557, -0.2040, 1.0570]],
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dtype=np.float32).T)
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RGB_linear = np.clip(RGB_linear, 0.0, 1.0)
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RGB_linear_D50 = RGB_linear / D50
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sRGB = np.where(RGB_linear_D50 <= 0.0031308, 12.92 * RGB_linear_D50,
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1.055 * (RGB_linear_D50 ** (1.0 / 2.4)) - 0.055)
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sRGB = np.clip(sRGB, 0.0, 1.0)
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return (RGB_linear_D50 * 255).astype(np.uint8)
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def numpy_split_lab(lab_img):
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L, a, b = np.rollaxis(lab_img, axis=-1)
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return L, a, b
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def numpy_merge_lab(l_channel_adjusted, a_channel, b_channel):
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merged_lab = np.dstack((l_channel_adjusted, a_channel, b_channel))
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return merged_lab
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def rgb2lab(img):
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# Convert the RGB image to a float array with values between 0 and 1
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img = img.astype(np.float32) / 255.0
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# Convert the RGB image to the XYZ color space
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# using the transformation matrix specified by the CIE
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# (https://en.wikipedia.org/wiki/CIE_1931_color_space)
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r, g, b = np.split(img, 3, axis=2)
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x = 0.412453 * r + 0.357580 * g + 0.180423 * b
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y = 0.212671 * r + 0.715160 * g + 0.072169 * b
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z = 0.019334 * r + 0.119193 * g + 0.950227 * b
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# Convert the XYZ image to the LAB color space using the D50 white point
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# (https://en.wikipedia.org/wiki/Lab_color_space#Conversion_from_XYZ_to_Lab)
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x /= 0.950456
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z /= 1.088754
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l = 116.0 * f(y) - 16.0
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a = 500.0 * (f(x) - f(y))
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b = 200.0 * (f(y) - f(z))
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# Stack the LAB channels back into a single image and return it
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return np.concatenate((l, a, b), axis=2)
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def apply_luminance_mask_lab(img, mask):
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# Convert the image to the LAB color space
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lab_img = rgb2lab(img)
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print(lab_img)
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lab_mask = rgb2lab(mask)
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print(lab_mask)
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# Split the LAB image into its channels
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l_channel, a_channel, b_channel = numpy_split_lab(lab_img)
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l_channel_mask, a_channel, b_channel = numpy_split_lab(lab_mask)
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# Apply the mask to the luminance channel
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l_channel_adjusted = l_channel + l_channel_mask # np.where(l_channel_mask > 0, l_channel - 50, l_channel)
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# Merge the adjusted channels back into a LAB image
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lab_img_adjusted = numpy_merge_lab(l_channel_adjusted, a_channel, b_channel)
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# Convert the adjusted image back to the RGB color space and return it
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return numpy_lab2rgb(lab_img_adjusted)
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def rgb2lab(img):
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# Convert the RGB image to a float array with values between 0 and 1
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img = img.astype(np.float32) / 255.0
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# Convert the RGB image to the XYZ color space
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# using the transformation matrix specified by the CIE
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# (https://en.wikipedia.org/wiki/CIE_1931_color_space)
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r, g, b = np.split(img, 3, axis=2)
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x = 0.412453 * r + 0.357580 * g + 0.180423 * b
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y = 0.212671 * r + 0.715160 * g + 0.072169 * b
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z = 0.019334 * r + 0.119193 * g + 0.950227 * b
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# Convert the XYZ image to the LAB color space using the D50 white point
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# (https://en.wikipedia.org/wiki/Lab_color_space#Conversion_from_XYZ_to_Lab)
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x /= 0.950456
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z /= 1.088754
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l = 116.0 * f(y) - 16.0
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a = 500.0 * (f(x) - f(y))
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b = 200.0 * (f(y) - f(z))
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# Stack the LAB channels back into a single image and return it
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return np.concatenate((l, a, b), axis=2)
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