add conf and theme

app.py

@@ -11,7 +11,6 @@ import matplotlib
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
 from scipy.signal import medfilt
-from skimage.measure import block_reduce
 from functools import partial
 from passlib.hash import pbkdf2_sha256
 from tqdm import tqdm
@@ -183,13 +182,11 @@ def inference(x, count_only_api, api_key, img_size=192, seq_len=64, stride_lengt
     
     if median_pred_filter:
         periodicity = medfilt(periodicity, 5)
-        #periodLength = medfilt(periodLength, 5)
+        periodLength = medfilt(periodLength, 5)
     periodicity = sigmoid(periodicity)
     full_marks = sigmoid(full_marks)
     full_marks_mask = np.int32(full_marks > marks_threshold)
-    #full_marks_reduced = block_reduce(full_marks > marks_threshold, (3,), np.max)
     periodicity_mask = np.int32(periodicity > miss_threshold)
-    #periodicity_mask_reduced = block_reduce(periodicity_mask, (3,), np.max)
     numofReps = 0
     count = []
     for i in range(len(periodLength)):
@@ -209,10 +206,15 @@ def inference(x, count_only_api, api_key, img_size=192, seq_len=64, stride_lengt
         marks_count_pred = marks_count_pred / 2
         count = np.array(count) / 2
 
+    confidence = (np.mean(periodicity[periodicity > miss_threshold]) - miss_threshold) / (1 - miss_threshold)
+    self_err = abs(count_pred - marks_count_pred)
+    self_pct_err = self_err / count_pred
+    total_confidence = confidence * (1 - self_pct_err)
+
     if both_feet:
-        count_msg = f"## Reps Count (both feet): {count_pred:.1f}, Marks Count (both feet): {marks_count_pred:.1f}"
+        count_msg = f"## Reps Count (both feet): {count_pred:.1f}, Marks Count (both feet): {marks_count_pred:.1f}, Confidence: {total_confidence:.2f}"
     else:
-        count_msg = f"## Predicted Count (one foot): {count_pred:.1f}, Marks Count (one foot): {marks_count_pred:.1f}"
+        count_msg = f"## Predicted Count (one foot): {count_pred:.1f}, Marks Count (one foot): {marks_count_pred:.1f}, Confidence: {total_confidence:.2f}"
 
     if api_call:
         if count_only_api:
@@ -229,20 +231,23 @@ def inference(x, count_only_api, api_key, img_size=192, seq_len=64, stride_lengt
     jumping_speed = np.copy(jumps_per_second)
     misses = periodicity < miss_threshold
     jumps_per_second[misses] = 0
+    frame_type = np.array(['miss' if miss else 'frame' for miss in misses])
+    frame_type[full_marks > marks_threshold] = 'jump'
     df = pd.DataFrame.from_dict({'period length': periodLength, 
                                  'jumping speed': jumping_speed,
                                 'jumps per second': jumps_per_second,
                                 'periodicity': periodicity,
                                 'miss': misses,
+                                'frame_type': frame_type,
                                 'jumps': full_marks,
-                                'jumps_size': (full_marks + 0.2) * 10,
+                                'jumps_size': (full_marks + 0.1) * 8,
                                 'miss_size': np.clip((1 - periodicity) * 0.9 + 0.1, 1, 10),
                                 'seconds': np.linspace(0, seconds, num=len(periodLength))})
     fig = px.scatter(data_frame=df,
                     x='seconds', 
                     y='jumps per second',
-                    symbol='miss',
-                    symbol_map={False: 'circle', True: 'circle-open'},
+                    symbol='frame_type',
+                    symbol_map={'frame': 'circle', 'miss': 'circle-open', 'jump': 'triangle-down'},
                     color='periodicity',
                     size='jumps_size',
                     size_max=10,
@@ -294,7 +299,7 @@ DESCRIPTION += '\n## AI Counting for Competitive Jump Rope'
 DESCRIPTION += '\nDemo created by [Dylan Plummer](https://dylan-plummer.github.io/). Check out the [NextJump iOS app](https://apps.apple.com/us/app/nextjump-jump-rope-counter/id6451026115).'
 
 
-with gr.Blocks() as demo:
+with gr.Blocks(theme='WeixuanYuan/Soft_dark') as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Column():
         with gr.Row():

requirements.txt

@@ -4,7 +4,6 @@ matplotlib
 plotly
 passlib
 scipy
-scikit-image
 --find-links https://download.pytorch.org/whl/torch_stable.html
 opencv-python-headless==4.7.0.68
 openvino-dev==2022.3.0