Update app.py

app.py

@@ -1,166 +1,36 @@
-"""Object detection demo with MobileNet SSD.
-This model and code are based on
-https://github.com/robmarkcole/object-detection-app
-"""
+"""A sample to use WebRTC in sendonly mode to transfer frames
+from the browser to the server and to render frames via `st.image`."""
 
 import logging
 import queue
-from pathlib import Path
-from typing import List, NamedTuple
 
-import av
-import cv2
-import numpy as np
 import streamlit as st
 from streamlit_webrtc import WebRtcMode, webrtc_streamer
 
-from sample_utils.download import download_file
 from sample_utils.turn import get_ice_servers
 
-HERE = Path(__file__).parent
-ROOT = HERE
-
 logger = logging.getLogger(__name__)
 
 
-MODEL_URL = "https://github.com/robmarkcole/object-detection-app/raw/master/model/MobileNetSSD_deploy.caffemodel"  # noqa: E501
-MODEL_LOCAL_PATH = ROOT / "./models/MobileNetSSD_deploy.caffemodel"
-PROTOTXT_URL = "https://github.com/robmarkcole/object-detection-app/raw/master/model/MobileNetSSD_deploy.prototxt.txt"  # noqa: E501
-PROTOTXT_LOCAL_PATH = ROOT / "./models/MobileNetSSD_deploy.prototxt.txt"
-
-CLASSES = [
-    "background",
-    "aeroplane",
-    "bicycle",
-    "bird",
-    "boat",
-    "bottle",
-    "bus",
-    "car",
-    "cat",
-    "chair",
-    "cow",
-    "diningtable",
-    "dog",
-    "horse",
-    "motorbike",
-    "person",
-    "pottedplant",
-    "sheep",
-    "sofa",
-    "train",
-    "tvmonitor",
-    ]
-
-
-class Detection(NamedTuple):
-    class_id: int
-    label: str
-    score: float
-    box: np.ndarray
-
-
-@st.cache_resource  # type: ignore
-def generate_label_colors():
-    return np.random.uniform(0, 255, size=(len(CLASSES), 3))
-
-
-COLORS = generate_label_colors()
-
-download_file(MODEL_URL, MODEL_LOCAL_PATH, expected_size=23147564)
-download_file(PROTOTXT_URL, PROTOTXT_LOCAL_PATH, expected_size=29353)
-
-
-# Session-specific caching
-cache_key = "object_detection_dnn"
-if cache_key in st.session_state:
-    net = st.session_state[cache_key]
-else:
-    net = cv2.dnn.readNetFromCaffe(str(PROTOTXT_LOCAL_PATH), str(MODEL_LOCAL_PATH))
-    st.session_state[cache_key] = net
-
-score_threshold = st.slider("Score threshold", 0.0, 1.0, 0.5, 0.05)
-
-# NOTE: The callback will be called in another thread,
-#       so use a queue here for thread-safety to pass the data
-#       from inside to outside the callback.
-# TODO: A general-purpose shared state object may be more useful.
-result_queue: "queue.Queue[List[Detection]]" = queue.Queue()
-
-
-def video_frame_callback(frame: av.VideoFrame) -> av.VideoFrame:
-    image = frame.to_ndarray(format="bgr24")
-
-    # Run inference
-    # blob = cv2.dnn.blobFromImage(
-    #     cv2.resize(image, (128, 128)), 0.007843, (128, 128), 127.5
-    # )
-    # net.setInput(blob)
-    # output = net.forward()
-
-    # h, w = image.shape[:2]
-
-    # # Convert the output array into a structured form.
-    # output = output.squeeze()  # (1, 1, N, 7) -> (N, 7)
-    # output = output[output[:, 2] >= score_threshold]
-    # detections = [
-    #     Detection(
-    #         class_id=int(detection[1]),
-    #         label=CLASSES[int(detection[1])],
-    #         score=float(detection[2]),
-    #         box=(detection[3:7] * np.array([w, h, w, h])),
-    #     )
-    #     for detection in output
-    # ]
-
-    # # Render bounding boxes and captions
-    # for detection in detections:
-    #     caption = f"{detection.label}: {round(detection.score * 100, 2)}%"
-    #     color = COLORS[detection.class_id]
-    #     xmin, ymin, xmax, ymax = detection.box.astype("int")
-
-    #     cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
-    #     cv2.putText(
-    #         image,
-    #         caption,
-    #         (xmin, ymin - 15 if ymin - 15 > 15 else ymin + 15),
-    #         cv2.FONT_HERSHEY_SIMPLEX,
-    #         0.5,
-    #         color,
-    #         2,
-    #     )
-
-    result_queue.put([]) #detections)
-
-    return av.VideoFrame.from_ndarray(image, format="bgr24")
-
-
 webrtc_ctx = webrtc_streamer(
-    key="object-detection",
-    mode=WebRtcMode.SENDRECV,
-    rtc_configuration={
-        "iceServers": get_ice_servers(),
-        "iceTransportPolicy": "relay",
-    },
-    video_frame_callback=video_frame_callback,
-    media_stream_constraints={"video": True, "audio": False},
-    async_processing=True,
+    key="video-sendonly",
+    mode=WebRtcMode.SENDONLY,
+    rtc_configuration={"iceServers": get_ice_servers()},
+    media_stream_constraints={"video": True},
 )
 
-if st.checkbox("Show the detected labels", value=True):
-    if webrtc_ctx.state.playing:
-        labels_placeholder = st.empty()
-        # NOTE: The video transformation with object detection and
-        # this loop displaying the result labels are running
-        # in different threads asynchronously.
-        # Then the rendered video frames and the labels displayed here
-        # are not strictly synchronized.
-        while True:
-            result = result_queue.get()
-            labels_placeholder.table(result)
-
-st.markdown(
-    "This demo uses a model and code from "
-    "https://github.com/robmarkcole/object-detection-app. "
-    "Many thanks to the project."
-)
+image_place = st.empty()
+
+while True:
+    if webrtc_ctx.video_receiver:
+        try:
+            video_frame = webrtc_ctx.video_receiver.get_frame(timeout=1)
+        except queue.Empty:
+            logger.warning("Queue is empty. Abort.")
+            break
+
+        img_rgb = video_frame.to_ndarray(format="rgb24")
+        image_place.image(img_rgb)
+    else:
+        logger.warning("AudioReciver is not set. Abort.")
+        break