Addedd blur for smoother HDR effect
(Doubles processing time, can be switched off)
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Thomas
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b36944281f
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7a92698dfc
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@ -33,16 +33,17 @@ if int(select) == 3:
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stack = file.openImageList(path_list, True)
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if int(select) == 4:
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path_list = ['test_hdr0.jpg', 'test_hdr1.jpg', 'test_hdr2.jpg']
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path_list = ['webcam25_3_2023_ev0.jpg','webcam25_3_2023_ev1.jpg','webcam25_3_2023_ev2.jpg']
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stack = file.openImageList(path_list, True)
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print(path_list)
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#Process HDR with mertens fusion and post effects
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result = hdr.process(stack, 1, 1, 1, True)
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#Process HDR with mertens fusion and post effects, blur
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#Set last value to false for double the speed but lesser blancaed hdr effect.
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result = hdr.process(stack, 1, 1, 1, True, True)
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#Save Result to File
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file = file.saveResultToFile(result, 'hdr/result', 75)
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file = file.saveResultToFile(result, 'hdr/result2', 75)
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98
numpyHDR.py
98
numpyHDR.py
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@ -28,42 +28,42 @@ def simple_clip(fused,gamma):
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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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# Get the dimensions of the input image and kernel
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image_height, image_width = image.shape
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kernel_height, kernel_width = kernel.shape
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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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# Compute the padding needed to handle boundary effects
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pad_height = (kernel_height - 1) // 2
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pad_width = (kernel_width - 1) // 2
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padded_image = np.pad(image, ((pad_height, pad_height), (pad_width, pad_width)), mode='constant')
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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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# Define generators for row and column indices
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row_indices = range(image_height)
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col_indices = range(image_width)
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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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# Define a generator expression to generate patches centered at each pixel
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patches = (
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padded_image[
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row : row + kernel_height, col : col + kernel_width
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]
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for row in row_indices
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for col in col_indices
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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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# Define a generator expression to generate element-wise products of patches and flipped kernels
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products = (
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patch * np.flip(kernel, axis=(0, 1))
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for patch in patches
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)
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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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# Define a generator expression to generate convolved values
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convolved_values = (
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product.sum()
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for product in products
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)
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# Reshape the convolved values into an output image
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convolved_image = np.array(list(convolved_values)).reshape((image_height, image_width))
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return convolved_image
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@ -96,14 +96,35 @@ def shadowlift(img, center=0.2, width=0.1, threshold=0.2, amount= 0.05):
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img_adjusted = np.clip(img_adjusted, 0, 1)
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return img_adjusted
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def blur(image, amount=1):
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# Define a kernel for sharpening
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kernel = np.array([[0, -1, 0],
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[-1, 4, -1],
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[0, -1, 0]])
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def mertens_fusion(stack, gamma=1, contrast_weight=1):
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# Apply the kernel to each channel of the image using convolution
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blurred = convolve2d(image, kernel)
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# Add the original image to the sharpened image with a weight of the sharpening amount
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sharpened = image + amount * (image - blurred)
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sharpened = np.clip(sharpened, 0, 1)
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# Crop the output image to match the input size
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#sharpened = sharpened.reshape(image.shape)
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return sharpened
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def mertens_fusion(stack, gamma:float =1, contrast_weight:float =1 ,blurred: bool = False) -> bytearray:
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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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blurred: Helps making transitions for the weights smoother but increases provessing time x2
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Returns:
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The fused HDR image.
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@ -111,6 +132,7 @@ def mertens_fusion(stack, gamma=1, contrast_weight=1):
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images = []
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for array in stack:
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#Incoming arrays in 255 er range
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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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@ -119,8 +141,15 @@ def mertens_fusion(stack, gamma=1, contrast_weight=1):
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weight_maps = []
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for img in images:
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threshold_h = .99
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threshold_l = .1
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# Apply thresholding to filter out overexposed portions of the image
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img = np.where(img > threshold_h, 0.99, img)
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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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if blurred:
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gray = blur(gray, 1)
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#kernel = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
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kernel = np.array([[1, 2, 1], [2, -11, 2], [1, 2, 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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@ -133,7 +162,6 @@ def mertens_fusion(stack, gamma=1, contrast_weight=1):
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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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@ -178,7 +206,7 @@ def compress_dynamic_range_histo(image, new_min=0.01, new_max=0.99):
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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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def process(stack, gain: float = 1, weight: float = 1, gamma: float = 1, post: bool = True, blurred: 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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@ -202,7 +230,7 @@ def process(stack, gain: float = 1, weight: float = 1, gamma: float = 1, post: b
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'''
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hdr_image = mertens_fusion(stack ,gain, weight)
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hdr_image = mertens_fusion(stack ,gain, weight, blurred)
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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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