Update app.py

app.py

@@ -126,29 +126,29 @@ def predict_beetle(img):
     print("Detecting & classifying beetles...")
     # Split image into smaller images of detected objects
     # image_lst = detect_objects(og_image=img, model=od_model, prompt="bug . insect", device="cpu")
-    # pre_process = pre_process_image(manual_thresh_buffer=0.15, image = img) # use image_dir if directory of image used
-    # pre_process.segment(cluster_num=2, 
-    #                     image_edge_buffer=50)
-    # image_lst = pre_process.col_image_lst
-    # print("Objects detected")
-    # # get predictions for all segments
-    # conf_dict_lst = []
-    # output_lst = []
-    # img_cnt = len(image_lst)
-    # for i in range(0,img_cnt):
-    #     prob_ar = np.array(bc_model.predict(image_lst[i])[2])
-    #     print(f"Beetle classified - {i}")
-    #     unkown_prob = unkown_prob_calc(probs=prob_ar, wedge_threshold=0.85, wedge_magnitude=5, wedge='dynamic')
-    #     prob_ar = np.append(prob_ar, unkown_prob)
-    #     prob_ar = np.around(prob_ar*100, decimals=1)
-    #     # only show the top 5 predictions
-    #     # Sorting the dictionary by value in descending order and taking the top items
-    #     top_num = 3
-    #     conf_dict = {labels[i]: float(prob_ar[i]) for i in range(len(prob_ar))}
-    #     conf_dict = dict(sorted(conf_dict.items(), key=lambda item: item[1], reverse=True)[:top_num])
-    #     conf_dict_lst.append(str(conf_dict)[1:-1]) # remove dictionary brackets
-    #     result = list(zip(image_lst, conf_dict_lst))
-    #     print(f"Classification processed - {i}")
+    pre_process = pre_process_image(manual_thresh_buffer=0.15, image = img) # use image_dir if directory of image used
+    pre_process.segment(cluster_num=2, 
+                        image_edge_buffer=50)
+    image_lst = pre_process.col_image_lst
+    print("Objects detected")
+    # get predictions for all segments
+    conf_dict_lst = []
+    output_lst = []
+    img_cnt = len(image_lst)
+    for i in range(0,img_cnt):
+        prob_ar = np.array(bc_model.predict(image_lst[i])[2])
+        print(f"Beetle classified - {i}")
+        unkown_prob = unkown_prob_calc(probs=prob_ar, wedge_threshold=0.85, wedge_magnitude=5, wedge='dynamic')
+        prob_ar = np.append(prob_ar, unkown_prob)
+        prob_ar = np.around(prob_ar*100, decimals=1)
+        # only show the top 5 predictions
+        # Sorting the dictionary by value in descending order and taking the top items
+        top_num = 3
+        conf_dict = {labels[i]: float(prob_ar[i]) for i in range(len(prob_ar))}
+        conf_dict = dict(sorted(conf_dict.items(), key=lambda item: item[1], reverse=True)[:top_num])
+        conf_dict_lst.append(str(conf_dict)[1:-1]) # remove dictionary brackets
+        result = list(zip(image_lst, conf_dict_lst))
+        print(f"Classification processed - {i}")
     result = list(zip([img], ["labelzzzzz"]))
     return(result)