Delete app.py~

app.py~

@@ -1,54 +0,0 @@
-import gradio as gr
-#import tensorflow as tf
-import numpy as np
-import json
-from os.path import dirname, realpath, join
-import processing.pipeline_numpy as ppn
-
-
-# Load human-readable labels for ImageNet.
-current_dir = dirname(realpath(__file__))
-
-    
-def process(RawImage, CameraParameters, Debayer, Sharpening, Denoising):
-    raw_img = RawImage
-    if CameraParameters == "Microscope":
-        black_level = [9.834368023181512e-06, 9.834368023181512e-06, 9.834368023181512e-06, 9.834368023181512e-06]
-        white_balance = [-0.6567, 1.9673, 3.5304]
-        colour_matrix = [-2.0338, 0.0933, 0.4157, -0.0286, 2.6464, -0.0574, -0.5516, -0.0947, 2.9308]
-    elif CameraParameters == "Drone":
-        #drone
-        black_level = [0.0625, 0.0626, 0.0625, 0.0626]
-        white_balance = [2.86653646, 1., 1.73079425]
-        colour_matrix = [1.50768983, -0.33571374, -0.17197604, -0.23048614,
-                        1.70698738, -0.47650126, -0.03119153, -0.32803956, 1.35923111]
-    else:
-        print("No valid camera parameter")
-    debayer = Debayer
-    sharpening = Sharpening
-    denoising = Denoising
-    print(np.max(raw_img))
-    raw_img = (raw_img[:,:,0].astype(np.float64)/255.)
-    img = ppn.processing(raw_img, black_level, white_balance, colour_matrix,
-                        debayer=debayer, sharpening=sharpening, denoising=denoising)
-    print(np.max(img))
-    return img
-
-
-iface = gr.Interface(
-    process, 
-    [gr.inputs.Image(),gr.inputs.Radio(["Microscope", "Drone"]),gr.inputs.Dropdown(["bilinear", "malvar2004", "menon2007"]), 
-    gr.inputs.Dropdown(["sharpening_filter", "unsharp_masking"]),
-    gr.inputs.Dropdown(["gaussian_denoising", "median_denoising"])], 
-    "image",
-    capture_session=True,
-    examples=[
-        ["demo-files/car.png"],
-        ["demo-files/micro.png"]
-    ],
-    title="static pipeline demo",
-    description="You can select a sample raw image, the camera parameters and the pipeline configuration to process the raw image.")
-
-if __name__ == "__main__":
-    iface.launch(share=True)
-