ultralytics 8.2.50 new Streamlit live inference Solution (#14210)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: RizwanMunawar <chr043416@gmail.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>

ultralytics/solutions/streamlit_inference.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import io
+import time
+
+import cv2
+import torch
+
+
+def inference():
+    """Runs real-time object detection on video input using Ultralytics YOLOv8 in a Streamlit application."""
+
+    # Scope imports for faster ultralytics package load speeds
+    import streamlit as st
+
+    from ultralytics import YOLO
+
+    # Hide main menu style
+    menu_style_cfg = """<style>MainMenu {visibility: hidden;}</style>"""
+
+    # Main title of streamlit application
+    main_title_cfg = """<div><h1 style="color:#FF64DA; text-align:center; font-size:40px; 
+                             font-family: 'Archivo', sans-serif; margin-top:-50px;margin-bottom:20px;">
+                    Ultralytics YOLOv8 Streamlit Application
+                    </h1></div>"""
+
+    # Subtitle of streamlit application
+    sub_title_cfg = """<div><h4 style="color:#042AFF; text-align:center; 
+                    font-family: 'Archivo', sans-serif; margin-top:-15px; margin-bottom:50px;">
+                    Experience real-time object detection on your webcam with the power of Ultralytics YOLOv8! 🚀</h4>
+                    </div>"""
+
+    # Set html page configuration
+    st.set_page_config(page_title="Ultralytics Streamlit App", layout="wide", initial_sidebar_state="auto")
+
+    # Append the custom HTML
+    st.markdown(menu_style_cfg, unsafe_allow_html=True)
+    st.markdown(main_title_cfg, unsafe_allow_html=True)
+    st.markdown(sub_title_cfg, unsafe_allow_html=True)
+
+    # Add ultralytics logo in sidebar
+    with st.sidebar:
+        logo = "https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg"
+        st.image(logo, width=250)
+
+    # Add elements to vertical setting menu
+    st.sidebar.title("User Configuration")
+
+    # Add video source selection dropdown
+    source = st.sidebar.selectbox(
+        "Video",
+        ("webcam", "video"),
+    )
+
+    vid_file_name = ""
+    if source == "video":
+        vid_file = st.sidebar.file_uploader("Upload Video File", type=["mp4", "mov", "avi", "mkv"])
+        if vid_file is not None:
+            g = io.BytesIO(vid_file.read())  # BytesIO Object
+            vid_location = "ultralytics.mp4"
+            with open(vid_location, "wb") as out:  # Open temporary file as bytes
+                out.write(g.read())  # Read bytes into file
+            vid_file_name = "ultralytics.mp4"
+    elif source == "webcam":
+        vid_file_name = 0
+
+    # Add dropdown menu for model selection
+    yolov8_model = st.sidebar.selectbox(
+        "Model",
+        (
+            "YOLOv8n",
+            "YOLOv8s",
+            "YOLOv8m",
+            "YOLOv8l",
+            "YOLOv8x",
+            "YOLOv8n-Seg",
+            "YOLOv8s-Seg",
+            "YOLOv8m-Seg",
+            "YOLOv8l-Seg",
+            "YOLOv8x-Seg",
+            "YOLOv8n-Pose",
+            "YOLOv8s-Pose",
+            "YOLOv8m-Pose",
+            "YOLOv8l-Pose",
+            "YOLOv8x-Pose",
+        ),
+    )
+    model = YOLO(f"{yolov8_model.lower()}.pt")  # Load the yolov8 model
+    class_names = list(model.names.values())  # Convert dictionary to list of class names
+
+    # Multiselect box with class names and get indices of selected classes
+    selected_classes = st.sidebar.multiselect("Classes", class_names, default=class_names[:3])
+    selected_ind = [class_names.index(option) for option in selected_classes]
+
+    if not isinstance(selected_ind, list):  # Ensure selected_options is a list
+        selected_ind = list(selected_ind)
+
+    conf_thres = st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.25, 0.01)
+    nms_thres = st.sidebar.slider("NMS Threshold", 0.0, 1.0, 0.45, 0.01)
+
+    col1, col2 = st.columns(2)
+    org_frame = col1.empty()
+    ann_frame = col2.empty()
+
+    fps_display = st.sidebar.empty()  # Placeholder for FPS display
+
+    if st.sidebar.button("Start"):
+        videocapture = cv2.VideoCapture(vid_file_name)  # Capture the video
+
+        if not videocapture.isOpened():
+            st.error("Could not open webcam.")
+
+        stop_button = st.button("Stop")  # Button to stop the inference
+
+        prev_time = 0
+        while videocapture.isOpened():
+            success, frame = videocapture.read()
+            if not success:
+                st.warning("Failed to read frame from webcam. Please make sure the webcam is connected properly.")
+                break
+
+            curr_time = time.time()
+            fps = 1 / (curr_time - prev_time)
+            prev_time = curr_time
+
+            # Store model predictions
+            results = model(frame, conf=float(conf_thres), iou=float(nms_thres), classes=selected_ind)
+            annotated_frame = results[0].plot()  # Add annotations on frame
+
+            # display frame
+            org_frame.image(frame, channels="BGR")
+            ann_frame.image(annotated_frame, channels="BGR")
+
+            if stop_button:
+                videocapture.release()  # Release the capture
+                torch.cuda.empty_cache()  # Clear CUDA memory
+                st.stop()  # Stop streamlit app
+
+            # Display FPS in sidebar
+            fps_display.metric("FPS", f"{fps:.2f}")
+
+        # Release the capture
+        videocapture.release()
+
+    # Clear CUDA memory
+    torch.cuda.empty_cache()
+
+    # Destroy window
+    cv2.destroyAllWindows()
+
+
+# Main function call
+if __name__ == "__main__":
+    inference()