219 lines
7.8 KiB
Python
219 lines
7.8 KiB
Python
import numpy as np
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'''Numpy and PIL implementation of a Mertens Fusion alghoritm
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Usage: Instantiate then set attributes:
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input_image = List containing path strings including .jpg Extension
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output_path = String ot Output without jpg ending
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compress_quality = 0-100 Jpeg compression level defaults to 75
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Run function sequence() to start processing.
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Example:
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hdr = numpyHDR.NumpyHDR()
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hdr.input_image = photos/EV- stages/
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hdr.compress_quality = 50
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hdr.output_path = photos/result/
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hdr.sequence()
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returns: Nothing
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'''
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def simple_clip(fused,gamma):
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# Apply gamma correction
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#fused = np.clip(fused, 0, 1)
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fused = np.power(fused, 1.0 / gamma)
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#hdr_8bit = np.clip(res_mertens * 255, 0, 255).astype('uint8')
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fused = (255.0 * fused).astype(np.uint8)
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#fused = Image.fromarray(fused)
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return fused
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def convolve2d(image, kernel):
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"""Perform a 2D convolution on the given image with the given kernel.
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Args:
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image: The input image to convolve.
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kernel: The kernel to convolve the image with.
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Returns:
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The convolved image.
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"""
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# Get the dimensions of the image and kernel.
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image_height, image_width = image.shape[:2]
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kernel_height, kernel_width = kernel.shape[:2]
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# Compute the amount of padding to add to the image.
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pad_height = kernel_height // 2
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pad_width = kernel_width // 2
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# Pad the image with zeros.
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padded_image = np.zeros(
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(image_height + 2 * pad_height, image_width + 2 * pad_width),
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dtype=np.float32,
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)
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padded_image[pad_height:-pad_height, pad_width:-pad_width] = image
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# Flip the kernel horizontally and vertically.
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flipped_kernel = np.flipud(np.fliplr(kernel))
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# Convolve the padded image with the flipped kernel.
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convolved_image = np.zeros_like(image, dtype=np.float32)
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for row in range(image_height):
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for col in range(image_width):
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patch = padded_image[
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row : row + kernel_height, col : col + kernel_width
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]
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product = patch * flipped_kernel
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convolved_image[row, col] = product.sum()
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return convolved_image
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def mask(img, center=50, width=20, threshold=0.2):
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'''Mask with sigmoid smooth'''
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mask = 1 / (1 + np.exp((center - img) / width)) # Smooth gradient mask
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mask = np.where(img > threshold, mask, 1) # Apply threshold to the mask
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mask = img * mask
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#plot_histogram(mask, title="mask")
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return mask
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def highlightsdrop(img, center=0.7, width=0.2, threshold=0.6, amount=0.08):
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'''Mask with sigmoid smooth targets bright sections'''
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mask = 1 / (1 + np.exp((center - img) / width)) # Smooth gradient mask
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mask = np.where(img > threshold, mask, 0) # Apply threshold to the mask
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mask = mask.reshape((img.shape))
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print(np.max(mask))
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img_adjusted = img - (mask * amount) # Adjust the image with a user-specified amount
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img_adjusted = np.clip(img_adjusted, 0, 1)
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return img_adjusted
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def shadowlift(img, center=0.2, width=0.1, threshold=0.2, amount= 0.05):
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'''Mask with sigmoid smooth targets bright sections'''
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mask = 1 / (1 + np.exp((center - img) / width)) # Smooth gradient mask
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mask = np.where(img < threshold, mask, 0) # Apply threshold to the mask
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mask = mask.reshape((img.shape))
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print(np.max(mask))
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img_adjusted = (mask * amount) + img # Adjust the image with a user-specified amount
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img_adjusted = np.clip(img_adjusted, 0, 1)
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return img_adjusted
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def mertens_fusion(stack, gamma=1, contrast_weight=1):
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"""Fuse multiple exposures into a single HDR image using the Mertens algorithm.
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Args:
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image_paths: A list of paths to input images.
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gamma: The gamma correction value to apply to the input images.
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contrast_weight: The weight of the local contrast term in the weight map computation.
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Returns:
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The fused HDR image.
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"""
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images = []
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for array in stack:
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img = np.array(array).astype(np.float32) / 255.0
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img = np.power(img, gamma)
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images.append(img)
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# Compute the weight maps for each input image based on the local contrast.
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weight_maps = []
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for img in images:
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gray = np.dot(img, [0.2989, 0.5870, 0.1140])
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kernel = np.array([[-1, -1, -1], [-1, 7, -1], [-1, -1, -1]])
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laplacian = np.abs(convolve2d(gray, kernel))
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weight = np.power(laplacian, contrast_weight)
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weight_maps.append(weight)
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# Normalize the weight maps.
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total_weight = sum(weight_maps)
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weight_maps = [w / total_weight for w in weight_maps]
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# Compute the fused HDR image by computing a weighted sum of the input images.
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fused = np.zeros(images[0].shape, dtype=np.float32)
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for i, img in enumerate(images):
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fused += weight_maps[i][:, :, np.newaxis] * img
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#print(fused)
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return fused
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def compress_dynamic_range(image):
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'''Compress dynamic range based on percentile'''
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# Find the 1st and 99th percentiles of the image
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p1, p99 = np.percentile(image, (0, 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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return new_image
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def compress_dynamic_range_histo(image, new_min=0.01, new_max=0.99):
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"""Compress the dynamic range of an image using histogram stretching.
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Args:
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image: A numpy array representing an image.
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new_min: The minimum value of the new range.
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new_max: The maximum value of the new range.
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Returns:
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The compressed image.
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"""
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# Calculate the histogram of the image.
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hist, bins = np.histogram(image.ravel(), bins=256, range=(0, 1))
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# Calculate the cumulative distribution function (CDF) of the histogram.
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cdf = hist.cumsum()
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cdf = (cdf - cdf.min()) / (cdf.max() - cdf.min()) # normalize to [0, 1]
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# Interpolate the CDF to get the new pixel values.
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new_pixels = np.interp(image.ravel(), bins[:-1], cdf * (new_max - new_min) + new_min)
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# Reshape the new pixel values to the shape of the original image.
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new_image = new_pixels.reshape((image.shape[0], image.shape[1], image.shape[2]))
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return new_image
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def process(stack, gain: float = 1, weight: float = 1, gamma: float = 1, post: bool = True):
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'''Processes the stack that contains a list of arrays form the camera into a PIL compatible clipped output array
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Args:
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stack : input list with arrays
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gain : low value low contrast, high value high contrast and brightness
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weight: How much the extracted portions of each image gets allpied to to the result "HDR effect intensity"
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gamma: Post fusion adjustment of the gamma.
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post: Enable or disable effects applied after the fusion True or False, default True
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shadowlift = slightly lifts the shadows
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Args:
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center: position of the filter dropoff
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width: range of the gradient, softness
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threshold: sets the threshhold form 0 to 1 0.1= lowest blacks....
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amount: How much the shadows should be lifted. Values under 0.1 seem to be good.
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returns:
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Hdr image with lifted blacks clipped to 0,1 range
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compress dynamic range:
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Tries to fit the image better into the available range. Less loggy image.
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Returns:
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HDR Image as PIL compatible array.
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'''
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hdr_image = mertens_fusion(stack ,gain, weight)
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if post == True:
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#hdr_image = self.highlightsdrop(hdr_image)
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hdr_image = shadowlift(hdr_image)
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hdr_image = compress_dynamic_range(hdr_image)
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#hdr_image = self.compress_dynamic_range_histo(hdr_image)
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hdr_image = simple_clip(hdr_image,gamma)
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return hdr_image
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