fixed upload issue

app.py

@@ -12,7 +12,7 @@ from streamlit_toggle import st_toggle_switch
 import pandas as pd
 from tools.nametypes import Stats, Detection
 from pathlib import Path
-from tools.utils import get_ice_servers, download_file, display_match, rgb, format_list
+from tools.utils import get_ice_servers, download_file, display_match, rgb, format_dflist
 from tools.face_recognition import (
     detect_faces,
     align_faces,
@@ -22,9 +22,6 @@ from tools.face_recognition import (
     process_gallery,
 )
 
-# TODO Error Handling!
-
-
 # Set logging level to error (To avoid getting spammed by queue warnings etc.)
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.ERROR)
@@ -54,14 +51,6 @@ with st.sidebar:
         track_color=rgb(50, 50, 50),
     )
 
-    upscale = st_toggle_switch(
-        "Upscale",
-        key="upscale",
-        default_value=True,
-        active_color=rgb(255, 75, 75),
-        track_color=rgb(50, 50, 50),
-    )
-
     st.markdown("## Webcam & Stream")
     resolution = st.selectbox(
         "Webcam Resolution",
@@ -128,6 +117,19 @@ else:
     )
     st.session_state[cache_key] = face_detection_model
 
+# Session-specific caching of the face detection model
+cache_key = "face_detection_model_gal"
+if cache_key in st.session_state:
+    face_detection_model_gal = st.session_state[cache_key]
+else:
+    face_detection_model_gal = mp.solutions.face_mesh.FaceMesh(
+        refine_landmarks=True,
+        min_detection_confidence=detection_confidence,
+        min_tracking_confidence=tracking_confidence,
+        max_num_faces=max_faces,
+    )
+    st.session_state[cache_key] = face_detection_model_gal
+
 stats_queue: "queue.Queue[Stats]" = queue.Queue()
 detections_queue: "queue.Queue[List[Detection]]" = queue.Queue()
 
@@ -173,7 +175,7 @@ def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
 
         # Draw detections
         start = time.time()
-        frame = draw_detections(frame, detections, upscale=upscale)
+        frame = draw_detections(frame, detections)
         stats = stats._replace(drawing=(time.time() - start) * 1000)
 
     # Convert frame back to av.VideoFrame
@@ -183,8 +185,8 @@ def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
     stats = stats._replace(fps=1 / (time.time() - frame_start))
 
     # Send data to other thread
-    detections_queue.put(detections)
-    stats_queue.put(stats)
+    detections_queue.put_nowait(detections)
+    stats_queue.put_nowait(stats)
 
     return frame
 
@@ -197,7 +199,7 @@ gallery = st.sidebar.file_uploader(
     "Upload images to gallery", type=["png", "jpg", "jpeg"], accept_multiple_files=True
 )
 if gallery:
-    gallery = process_gallery(gallery, face_detection_model, face_recognition_model_gal)
+    gallery = process_gallery(gallery, face_detection_model_gal, face_recognition_model_gal)
     st.sidebar.markdown("**Gallery Images**")
     st.sidebar.image(
         [identity.image for identity in gallery],
@@ -241,17 +243,17 @@ detections = st.empty()
 if ctx.state.playing:
     while True:
         # Get stats
-        stats_dataframe = pd.DataFrame([stats_queue.get()])
+        stats_dataframe = pd.DataFrame([stats_queue.get(timeout=10)])
 
         # Write stats to streamlit
         stats.dataframe(stats_dataframe.style.format(thousands=" ", precision=2))
 
         # Get detections
-        detections_data = detections_queue.get()
+        detections_data = detections_queue.get(timeout=10)
         detections_dataframe = (
             pd.DataFrame(detections_data)
             .drop(columns=["face", "face_match"], errors="ignore")
-            .applymap(lambda x: (format_list(x)))
+            .applymap(lambda x: (format_dflist(x)))
         )
 
         # Write detections to streamlit

tools/utils.py

@@ -143,11 +143,11 @@ def download_file(url, model_path: Path, file_hash=None):
 
 
 # Function to format floats within a list
-def format_list(val):
+def format_dflist(val):
     if isinstance(val, list):
-        return [format_list(num) for num in val]
+        return [format_dflist(num) for num in val]
     if isinstance(val, np.ndarray):
-        return np.asarray([format_list(num) for num in val])
+        return np.asarray([format_dflist(num) for num in val])
     if isinstance(val, np.float32):
         return f"{val:.2f}"
     if isinstance(val, float):