Create app.py

app.py

@@ -0,0 +1,166 @@
+"""Object detection demo with MobileNet SSD.
+This model and code are based on
+https://github.com/robmarkcole/object-detection-app
+"""
+
+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.parent
+
+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, (300, 300)), 0.007843, (300, 300), 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,
+)
+
+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."
+)