Update app.py

app.py

@@ -78,9 +78,9 @@ def Image_Processing(filelist):
         red_edge = np.expand_dims(img_stack[:, :, 2], axis=-1)
         NIR = np.expand_dims(img_stack[:, :, -1], axis=-1)
         composite_img = np.concatenate((green, red_edge, red), axis=-1) * 255
-        gray_img, binary = CCA_Preprocess(composite_img, k=k)
+        # gray_img, binary = CCA_Preprocess(composite_img, k=k)
         
-        preprocessed_img = np.repeat(np.expand_dims(binary, axis=-1), 3, axis=-1) * composite_img
+        # preprocessed_img = np.repeat(np.expand_dims(binary, axis=-1), 3, axis=-1) * composite_img
         # Perform Min-Max normalization 
         # new approach (line 192,193,194) 
         normalized_img = (composite_img - composite_img.min()) / (composite_img.max() - composite_img.min())
@@ -92,7 +92,11 @@ def Image_Processing(filelist):
     stitched_image = image_stitching(color_images)
     stitched_image = Image.fromarray(np.uint8(stitched_image))
     stitched_cv_image = np.array(stitched_image.convert('RGB'))[:, :, ::-1].copy()
-
+    gray_image,binary = CCA_Preprocess(stitched_cv_image,k=k)
+    preprocessed_img = np.repeat(np.expand_dims(binary, axis=-1), 3, axis=-1) * stitched_cv_image
+    normalized_preprocessed_img = (preprocessed_img - preprocessed_img.min()) / (preprocessed_img.max() - preprocessed_img.min())
+    normalized_preprocessed_img *= 255
+    normalized_preprocessed_img = normalized_preprocessed_img.astype(np.uint8)
     temp_stitched_save_path = 'temp_stitched_image.png'
     stitched_image.save(temp_stitched_save_path)
     result = detect_object(yolo_model, temp_stitched_save_path, confidence=0.128)
@@ -105,12 +109,30 @@ def Image_Processing(filelist):
     overlayed_cv_image = np.array(overlayed_image.convert('RGB'))[:, :, ::-1].copy()
     input_images = [(input_images[index],image_names[index]) for index in range(len(input_images))]
     color_images = [(cv2.cvtColor(color_images[index], cv2.COLOR_BGR2RGB),image_names[index]) for index in range(len(color_images))]
-    return input_images,color_images,[(stitched_cv_image,'Processed & Stitched Color Image'),(overlayed_cv_image,'Foreground Separated')]
+    return input_images,color_images,[(stitched_cv_image,'Processed & Stitched Color Image'),
+                                      (normalized_preprocessed_img, 'Foreground Image by Connected Component Analysis'),
+                                      (overlayed_cv_image,'Foreground Image by Segmentation')]
 
-file_input = gr.File(file_count="multiple", label = 'Upload Raw Input Images', show_label = True)
-gallery_raw_inputs = gr.Gallery(label = 'Input Raw Plant Images', show_label = True, height = 512, allow_preview = True, preview = True)
-gallery_color_images = gr.Gallery(label = 'Preprocessed Color Plant Images', show_label = True, height = 512, allow_preview = True, preview = True)
-gallery_output = gr.Gallery(label = 'Plant Analysis', show_label = True, height = 512, allow_preview = True, preview = True)
+file_input = gr.File(file_count="multiple", 
+                     label = 'Upload Raw Input Images', 
+                     show_label = True)
+gallery_raw_inputs = gr.Gallery(label = 'Input Raw Plant Images', 
+                                show_label = True, 
+                                height = 512, 
+                                allow_preview = True, 
+                                preview = True)
+gallery_color_images = gr.Gallery(label = 'Preprocessed Color Plant Images', 
+                                  show_label = True, 
+                                  height = 512, 
+                                  allow_preview = True, 
+                                  preview = True)
+gallery_output = gr.Gallery(label = 'Plant Analysis', 
+                            show_label = True, 
+                            height = 512, 
+                            allow_preview = True, 
+                            preview = True)
 
-iface = gr.Interface(fn = Image_Processing, inputs = file_input, outputs = [gallery_raw_inputs,gallery_color_images,gallery_output])
+iface = gr.Interface(fn = Image_Processing, 
+                     inputs = file_input, 
+                     outputs = [gallery_raw_inputs,gallery_color_images,gallery_output])
 iface.launch(share=True)