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import gradio as gr |
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import numpy as np |
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import json |
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from os.path import dirname, realpath, join |
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import processing.pipeline_numpy as ppn |
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current_dir = dirname(realpath(__file__)) |
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def process(RawImage, CameraParameters, Debayer, Sharpening, Denoising): |
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raw_img = RawImage |
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if CameraParameters == "Microscope": |
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black_level = [9.834368023181512e-06, 9.834368023181512e-06, 9.834368023181512e-06, 9.834368023181512e-06] |
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white_balance = [-0.6567, 1.9673, 3.5304] |
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colour_matrix = [-2.0338, 0.0933, 0.4157, -0.0286, 2.6464, -0.0574, -0.5516, -0.0947, 2.9308] |
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elif CameraParameters == "Drone": |
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black_level = [0.0625, 0.0626, 0.0625, 0.0626] |
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white_balance = [2.86653646, 1., 1.73079425] |
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colour_matrix = [1.50768983, -0.33571374, -0.17197604, -0.23048614, |
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1.70698738, -0.47650126, -0.03119153, -0.32803956, 1.35923111] |
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else: |
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black_level = [0.0625, 0.0626, 0.0625, 0.0626] |
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white_balance = [2.86653646, 1., 1.73079425] |
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colour_matrix = [1.50768983, -0.33571374, -0.17197604, -0.23048614, |
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1.70698738, -0.47650126, -0.03119153, -0.32803956, 1.35923111] |
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debayer = Debayer |
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sharpening = Sharpening |
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denoising = Denoising |
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print(np.max(raw_img)) |
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raw_img = (raw_img[:,:,:].astype(np.float64)/255.) |
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img = ppn.processing(raw_img, black_level, white_balance, colour_matrix, |
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debayer=debayer, sharpening=sharpening, denoising=denoising) |
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print(np.max(img)) |
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return img |
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iface = gr.Interface( |
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process, |
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[gr.inputs.Image(),gr.inputs.Radio(["Microscope", "Drone"]),gr.inputs.Dropdown(["bilinear", "malvar2004", "menon2007"]), |
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gr.inputs.Dropdown(["sharpening_filter", "unsharp_masking"]), |
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gr.inputs.Dropdown(["gaussian_denoising", "median_denoising"])], |
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"image", |
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capture_session=True, |
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examples=[ |
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["demo-files/car.png"], |
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["demo-files/micro.png"] |
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], |
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title="static pipeline demo", |
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description="You can select a sample raw image, the camera parameters and the pipeline configuration to process the raw image.") |
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iface.launch() |
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