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"""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."
) |