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* Added Sharpen block * Resolved linting issues * linting issues resolved * updated diagram and README * Added print statement in sharpen to print sharpen strength value for isp logs * Added print statement in sharpen to print sharpen strength value for isp logs
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,126 @@ | ||
| """ | ||
| File: sharpen.py | ||
| Description: Simple unsharp masking with frequency and strength control. | ||
| Code / Paper Reference: | ||
| Author: xx-isp ([email protected]) | ||
| """ | ||
|
|
||
| import time | ||
| import os | ||
| import numpy as np | ||
| from scipy import ndimage | ||
|
|
||
| from util.utils import create_coeff_file | ||
|
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| class Sharpening: | ||
| """ | ||
| Sharpening | ||
| """ | ||
|
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| def __init__(self, img, platform, sensor_info, parm_sha, save_out_obj): | ||
| self.img = img.copy() | ||
| self.enable = parm_sha["is_enable"] | ||
| self.sensor_info = sensor_info | ||
| self.parm_sha = parm_sha | ||
| self.is_save = parm_sha["is_save"] | ||
| self.platform = platform | ||
| self.save_out_obj = save_out_obj | ||
|
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||
| def gaussian_kernel(self, size_x, size_y=None, sigma_x=5, sigma_y=None): | ||
| """ | ||
| Generate a Gaussian kernel for convolutions for Sharpening Algorithm | ||
| """ | ||
|
|
||
| if size_y is None: | ||
| size_y = size_x | ||
| if sigma_y is None: | ||
| sigma_y = sigma_x | ||
|
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||
| assert isinstance(size_x, int) | ||
| assert isinstance(size_y, int) | ||
|
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| x_0 = size_x // 2 | ||
| y_0 = size_y // 2 | ||
|
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| x_axis = np.arange(0, size_x, dtype=float) # x_axis range [0:size_x] | ||
| y_axis = np.arange(0, size_y, dtype=float)[:, np.newaxis] | ||
|
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| x_axis -= x_0 | ||
| y_axis -= y_0 | ||
|
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| exp_part = (x_axis**2 / (2 * sigma_x**2)) + (y_axis**2 / (2 * sigma_y**2)) | ||
| return 1 / ((2 * np.pi * sigma_x * sigma_y) * np.exp(-exp_part)) | ||
|
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||
| def apply_sharpen(self): | ||
| """Sharpens an image using the unsharp mask algorithm. | ||
| Args: | ||
| image: A numpy array of shape (height, width) representing the image to be sharpened. | ||
| kernel_size: The size of the Gaussian kernel to use for blurring the image. | ||
| sigma: The standard deviation of the Gaussian kernel. | ||
| Returns: | ||
| A numpy array of shape (height, width) representing the sharpened image. | ||
| """ | ||
|
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| sigma = self.parm_sha["sharpen_sigma"] | ||
| kernel_size = 9 | ||
|
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| kernel = self.gaussian_kernel(kernel_size, kernel_size, sigma, sigma) | ||
|
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| kernel = (kernel * (2**20)).astype(np.int32) | ||
|
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| folder_name = "coefficients/SHARP" | ||
| if not os.path.exists(folder_name): | ||
| os.makedirs(folder_name) | ||
| create_coeff_file(kernel, "coefficients/SHARP/luma_kernel", 20) | ||
|
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| luma = (self.img[:, :, 0]).astype(np.int32) | ||
|
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| # Filter the luma component of the image with a Gaussian LPF | ||
| # Smoothing magnitude can be controlled with the sharpen_sigma parameter | ||
| correlation = ndimage.correlate(luma, kernel, mode='constant', cval=0.0, origin=0) | ||
|
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| smoothened = correlation >> 20 | ||
|
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| # Sharpen the image with upsharp mask | ||
| # Strength is tuneable with the sharpen_strength parameter] | ||
| sh_str = self.parm_sha["sharpen_strength"] | ||
| print(" - Sharpen - strength = ", sh_str) | ||
| strength = int(sh_str * (2**10)) | ||
|
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| edge = luma - smoothened | ||
|
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| sharpen = strength * edge | ||
| sharpened = sharpen >> 10 | ||
|
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| out_y = luma + sharpened | ||
| self.img[:, :, 0] = np.uint8(np.clip(out_y, 0, 255)) | ||
| return self.img | ||
|
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||
| def save(self): | ||
| """ | ||
| Function to save module output | ||
| """ | ||
| if self.is_save: | ||
| self.save_out_obj.save_output_array_yuv( | ||
| self.platform["in_file"], | ||
| self.img, | ||
| "Out_sharpen_", | ||
| self.platform, | ||
| ) | ||
|
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| def execute(self): | ||
| """ | ||
| Applying sharpening to input image | ||
| """ | ||
| print("Sharpen = " + str(self.enable)) | ||
|
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| if self.enable is True: | ||
| start = time.time() | ||
| s_out = self.apply_sharpen() | ||
| print(f" Execution time: {time.time() - start:.3f}s") | ||
| self.img = s_out | ||
|
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| self.save() | ||
| return self.img |