updating requirements.txt

app.py

@@ -88,59 +88,59 @@ def doo(video, progress=gr.Progress()):
     feature_data_path = os.path.join(feature_dir, exp_name)
     
     
-#     #path to saved models
-#     no_motility_model_path = "models/motility/no/collagen_optical_flow_median_bkg_more_data_90k/"
-#     low_motility_model_path = "models/motility/low/collagen_optical_flow_median_bkg_more_data_90k/"
-#     mid_motility_model_path = "models/motility/mid/collagen_optical_flow_median_bkg_more_data_90k/"
-#     high_motility_model_path = "models/motility/high/collagen_optical_flow_median_bkg_more_data_90k/"
+    #path to saved models
+    no_motility_model_path = "models/motility/no/collagen_optical_flow_median_bkg_more_data_90k/"
+    low_motility_model_path = "models/motility/low/collagen_optical_flow_median_bkg_more_data_90k/"
+    mid_motility_model_path = "models/motility/mid/collagen_optical_flow_median_bkg_more_data_90k/"
+    high_motility_model_path = "models/motility/high/collagen_optical_flow_median_bkg_more_data_90k/"
     
     
-#     # Clear previous results and data
-#     if os.path.exists(final_data_dir):
-#         shutil.rmtree(final_data_dir)
-#     if os.path.exists(raw_frame_dir):
-#         shutil.rmtree(raw_frame_dir)
-#     if os.path.exists(feature_dir):
-#         shutil.rmtree(feature_dir)
+    # Clear previous results and data
+    if os.path.exists(final_data_dir):
+        shutil.rmtree(final_data_dir)
+    if os.path.exists(raw_frame_dir):
+        shutil.rmtree(raw_frame_dir)
+    if os.path.exists(feature_dir):
+        shutil.rmtree(feature_dir)
     
-#     # Read video and store frames separately for object detection model
-#     video_dir = read_video(video, raw_frame_dir, progress=gr.Progress())   
-#     # Process raw frames and store in acceptable format
-#     progress(1 / 3, desc=f"Processing Frames {1}/{3}")
-#     video_num = process_data(video_dir, raw_frame_dir, final_data_dir, out_sub_dir)    
-#     progress(3 / 3, desc=f"Processing Frames {3}/{3}")
-#     # generate features for raw frames for the object detector model
-#     progress(1 / 3, desc=f"Generating Features {1}/{3}")
-#     create_test_data(target_data_sub_dir, feature_dir, exp_name, test_video_list)
-#     progress(3 / 3, desc=f"Features Generated {3}/{3}")
+    # Read video and store frames separately for object detection model
+    video_dir = read_video(video, raw_frame_dir, progress=gr.Progress())   
+    # Process raw frames and store in acceptable format
+    progress(1 / 3, desc=f"Processing Frames {1}/{3}")
+    video_num = process_data(video_dir, raw_frame_dir, final_data_dir, out_sub_dir)    
+    progress(3 / 3, desc=f"Processing Frames {3}/{3}")
+    # generate features for raw frames for the object detector model
+    progress(1 / 3, desc=f"Generating Features {1}/{3}")
+    create_test_data(target_data_sub_dir, feature_dir, exp_name, test_video_list)
+    progress(3 / 3, desc=f"Features Generated {3}/{3}")
     
 
-#     progress(1 / 3, desc=f"Loading Models {1}/{3}")
-#     # Run Object Detection Code
-#     for video_num in [1]:
-#         #To genearate testing files for all motilities
-#         run_inference(video_num=video_num, output_dir=no_motility_model_path, 
-#                       annotations_test="All", test_dir=feature_data_path, register_dataset=True)
-#         progress(3 / 3, desc=f"Models Loaded{3}/{3}")
-#         run_inference(video_num=video_num, output_dir=mid_motility_model_path, 
-#                       annotations_test="Motility-mid", test_dir=feature_data_path, register_dataset=False)
-#         progress(1 / 3, desc=f"Running Bacteria Detection {1}/{3}")
+    progress(1 / 3, desc=f"Loading Models {1}/{3}")
+    # Run Object Detection Code
+    for video_num in [1]:
+        #To genearate testing files for all motilities
+        run_inference(video_num=video_num, output_dir=no_motility_model_path, 
+                      annotations_test="All", test_dir=feature_data_path, register_dataset=True)
+        progress(3 / 3, desc=f"Models Loaded{3}/{3}")
+        run_inference(video_num=video_num, output_dir=mid_motility_model_path, 
+                      annotations_test="Motility-mid", test_dir=feature_data_path, register_dataset=False)
+        progress(1 / 3, desc=f"Running Bacteria Detection {1}/{3}")
         
-#         run_inference(video_num=video_num, output_dir=high_motility_model_path, 
-#                       annotations_test="Motility-high", test_dir=feature_data_path, register_dataset=False)
-#         progress(2 / 3, desc=f"Running Bacteria Detection {2}/{3}")
+        run_inference(video_num=video_num, output_dir=high_motility_model_path, 
+                      annotations_test="Motility-high", test_dir=feature_data_path, register_dataset=False)
+        progress(2 / 3, desc=f"Running Bacteria Detection {2}/{3}")
         
-#         run_inference(video_num=video_num, output_dir=low_motility_model_path, 
-#                       annotations_test="Motility-low", test_dir=feature_data_path, register_dataset=False)
-#         progress(3 / 3, desc=f"Running Bacteria Detection {3}/{3}")
+        run_inference(video_num=video_num, output_dir=low_motility_model_path, 
+                      annotations_test="Motility-low", test_dir=feature_data_path, register_dataset=False)
+        progress(3 / 3, desc=f"Running Bacteria Detection {3}/{3}")
         
-#     # Tracking where GT is present
-#     progress(0 / 3, desc=f"Tracking {0}/{3}")
+    # Tracking where GT is present
+    progress(0 / 3, desc=f"Tracking {0}/{3}")
     for video_num in [1]:
-#         gen_tracking_data(video_num=video_num, data_path=feature_data_path, filter_thresh=0.3)
-#         progress(1 / 3, desc=f"Tracking {1}/{3}")
-#         track_bacteria(video_num=video_num, max_age=35, max_interpolation=35, data_path=feature_data_path)
-#         progress(2 / 3, desc=f"Tracking {2}/{3}")
+        gen_tracking_data(video_num=video_num, data_path=feature_data_path, filter_thresh=0.3)
+        progress(1 / 3, desc=f"Tracking {1}/{3}")
+        track_bacteria(video_num=video_num, max_age=35, max_interpolation=35, data_path=feature_data_path)
+        progress(2 / 3, desc=f"Tracking {2}/{3}")
         folder_path = analyse_tracking(video_num=video_num, data_feature_path=feature_data_path, data_root_path=final_data_dir, plot=True)
         progress(3 / 3, desc=f"Tracking {3}/{3}")
         output_video = gen_tracking_video(video_num=video_num, fps=60, data_path=feature_data_path)

requirements.txt

@@ -29,7 +29,7 @@ openpyxl
 pandas==1.1.5
 plotly==5.11.0
 scikit-image
-scikit-learn==1.12
+#scikit-learn==1.12
 scipy==1.5.4
 seaborn==0.11.2
 torch==1.9.1