demo files for static processing pipeline

demo-files/demo-requirements.txt

@@ -0,0 +1,18 @@
+scikit_image
+pandas
+scipy
+numpy
+matplotlib
+b2sdk
+colour_demosaicing
+gradio
+ipython
+mlflow
+Pillow
+pytorch_toolbelt
+rawpy
+scikit_learn
+segmentation_models_pytorch
+tifffile
+torch==1.9.0
+torchvision==0.10.0

demo.py

@@ -0,0 +1,54 @@
+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="Lens2Logit - Static processing 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)
+

utils/base.py

@@ -200,8 +200,8 @@ def get_mlflow_model_by_name(experiment_name, run_name,
     
     # 0. mlflow basics
     mlflow.set_tracking_uri(tracking_uri)
-os.environ["AWS_ACCESS_KEY_ID"] = #TODO: add your AWS access key if you want to write your results to our collaborative lab server
-    os.environ["AWS_SECRET_ACCESS_KEY"] = #TODO: add your AWS seceret access key if you want to write your results to our collaborative lab server
+    os.environ["AWS_ACCESS_KEY_ID"] = "#TODO: add your AWS access key if you want to write your results to our collaborative lab server"
+    os.environ["AWS_SECRET_ACCESS_KEY"] = "#TODO: add your AWS seceret access key if you want to write your results to our collaborative lab server"
 
     # # 1. use get_experiment_by_name to get experiment objec
     experiment = mlflow.get_experiment_by_name(experiment_name)