Create app.py

app.py

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+# Data Handling
+import numpy as np
+import cv2
+import imutils
+import tensorflow as tf
+from tensorflow import keras
+import gradio as gr
+from tensorflow.keras.models import load_model
+
+model = load_model('./augmented_unet_pretrained.h5', compile=False)
+
+def segmentation(inp):
+                    
+    #inp = cv2.cvtColor(inp, cv2.COLOR_BGR2RGB)      # Input image
+    inp = cv2.resize(inp, (256, 256))               # Resize
+    inp = (inp.astype('float32')) / 255. 
+    test_input = inp
+    # (Must Add cropping for real time images)
+
+    # Predictions
+    prediction_on_test = np.expand_dims(test_input, 0)
+    prediction_on_test = model.predict(prediction_on_test)
+    prediction_on_test = prediction_on_test > 0.5
+    predicted_img = prediction_on_test[0,:,:,0]   
+
+     # EXTRACTING CONTOURS
+   
+    predicted = predicted_img.astype(np.uint8)
+    cnts = cv2.findContours(image=predicted, mode=cv2.RETR_TREE, method=cv2.CHAIN_APPROX_NONE)
+    contours = imutils.grab_contours(cnts)
+    contoured = test_input.copy()
+    contoured = (contoured * 255).astype(np.uint8)
+    cv2.drawContours(image=contoured, contours=contours, contourIdx=-1, color=(255, 0, 0), thickness=1, lineType=cv2.LINE_AA)
+
+
+     # Circumference of detected Mask
+    if contours :
+        a = "Polynya Detected"
+        for i in range(len(contours)):
+            circum = cv2.arcLength(contours[i], True)
+            circum = round(circum,2)
+            b = str(circum) + '\t' + "px"
+    else:
+        #a = print("No Polynya Detected")
+        a = "No Polynya Detected"
+        #b = print(f"Circumference of Polynya : 0.0 px")
+        b = "0.0 px"
+
+    return(contoured, a, b)
+    
+image = gr.Image(label = 'Input Image')
+out1 = gr.Image(label = 'Result')
+out2 = gr.Textbox(label = 'Label')
+out3 = gr.Textbox(label = 'Circumference in Pixel Unit')
+
+interface = gr.Interface(fn = segmentation, inputs = image, outputs = [out1, out2, out3], 
+                         title= 'Detection Of Polynya with Artificial Intelligence')
+                 
+interface.launch()