Upload 6 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+demo.mp4 filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+
+
+
+
+
+
+# Base image with Python 3.9
+FROM python:3.9
+
+# For fixing ImportError: libGL.so.1: cannot open shared object file: No such file or directory
+RUN apt-get update
+RUN apt install -y libgl1-mesa-glx
+
+
+# Create a non-root user
+RUN useradd -m -u 1000 user
+
+# Set the working directory
+WORKDIR /app
+
+# Copy requirements.txt and install Python dependencies
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Copy the rest of the application code
+COPY --chown=user . /app
+
+# Switch to the non-root user
+USER user
+
+# Command to run the FastAPI application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+import os
+import cv2
+from fastapi import FastAPI, Request, UploadFile, File
+from fastapi.responses import StreamingResponse, HTMLResponse
+from fastapi.templating import Jinja2Templates
+from typing import Generator
+from ultralytics import YOLO
+import numpy as np
+
+app = FastAPI()
+templates = Jinja2Templates(directory="templates")
+
+# Load the YOLOv8 model
+model = YOLO("yolov8l.pt")
+
+video_path = None
+cap = None
+bird_count = 0
+tracker_initialized = False
+trackers = None
+
+@app.post("/upload_video/")
+async def upload_video(file: UploadFile = File(...)):
+    global cap, tracker_initialized, trackers
+
+    # Save uploaded video file
+    file_location = f"uploads/{file.filename}"
+    with open(file_location, "wb") as f:
+        f.write(file.file.read())
+
+    # Open the uploaded video file
+    cap = cv2.VideoCapture(file_location)
+
+    # Reset tracker and counter
+    tracker_initialized = False
+    trackers = None
+
+    return {"info": f"file '{file.filename}' saved at '{file_location}'"}
+
+def process_video() -> Generator[bytes, None, None]:
+    global bird_count, tracker_initialized, trackers, cap
+    while cap.isOpened():
+        success, frame = cap.read()
+
+        if success:
+            frame_height, frame_width = frame.shape[:2]
+            if not tracker_initialized:
+                results = model(frame)
+                detections = results[0].boxes.data.cpu().numpy()
+                bird_results = [detection for detection in detections if int(detection[5]) == 14]
+
+                try:
+                    trackers = cv2.legacy.MultiTracker_create()
+                except AttributeError:
+                    trackers = cv2.MultiTracker_create()
+
+                for res in bird_results:
+                    x1, y1, x2, y2, confidence, class_id = res
+                    x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+                    if 0 <= x1 < frame_width and 0 <= y1 < frame_height and x2 <= frame_width and y2 <= frame_height:
+                        bbox = (x1, y1, x2 - x1, y2 - y1)
+                        tracker = cv2.legacy.TrackerCSRT_create() if hasattr(cv2, 'legacy') else cv2.TrackerCSRT_create()
+                        trackers.add(tracker, frame, bbox)
+
+                bird_count = len(bird_results)
+                tracker_initialized = True
+            else:
+                success, boxes = trackers.update(frame)
+
+                if success:
+                    bird_count = len(boxes)
+                    for box in boxes:
+                        x, y, w, h = [int(v) for v in box]
+                        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+                        cv2.putText(frame, 'bird', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+                else:
+                    tracker_initialized = False
+
+            ret, buffer = cv2.imencode('.jpg', frame)
+            frame = buffer.tobytes()
+            yield (b'--frame\r\n'
+                   b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n')
+        else:
+            break
+    cap.release()
+
+@app.get("/", response_class=HTMLResponse)
+async def index(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request, "bird_count": bird_count})
+
+@app.get("/video_feed")
+async def video_feed():
+    return StreamingResponse(process_video(), media_type='multipart/x-mixed-replace; boundary=frame')
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

demo.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1f13ec6b61585746d6db66f057bb5aab8106a1f37b581adab03b7a14d4c98a0d
+size 1399765

requirements.txt

@@ -0,0 +1,9 @@
+uvicorn
+jinja2
+opencv-python-headless
+ultralytics
+fastapi 
+uvicorn 
+opencv-python-headless 
+ultralytics 
+jinja2

templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Birds Count</title>
+    <style>
+        body, html {
+            margin: 0;
+            padding: 0;
+            width: 100%;
+            height: 100%;
+            display: flex;
+            flex-direction: column;
+            justify-content: space-between;
+        }
+        .header, .footer {
+            background-color: #216d7c;
+            width: 100%;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            padding: 20px;
+            box-sizing: border-box;
+        }
+        .header-content {
+            display: flex;
+            align-items: center;
+        }
+        .content {
+            flex: 1;
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+            justify-content: center;
+        }
+        img {
+            max-width: 100%;
+            height: auto;
+        }
+        .logo {
+            height: 40px;
+            margin-right: 10px;
+        }
+        .header-text {
+            font-size: 24px;
+            font-weight: bold;
+            color: white;
+        }
+        .footer-content {
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+        }
+        .footer-text {
+            color: white;
+            margin-bottom: 10px;
+        }
+        .social-links img {
+            height: 24px;
+            margin: 0 5px;
+        }
+    </style>
+</head>
+<body>
+    <div class="header">
+        <div class="header-content">
+         <!--   <img src="pyresearch.png" alt="Logo" class="logo">  -->
+            <div class="header-text"> Bird Count Dashboard</div>
+        </div>
+    </div>
+
+    <div class="content">
+        <h2>Current Bird Count: {{ bird_count }}</h2>
+        <img src="{{ url_for('video_feed') }}" width="720" height="480">
+    </div>
+
+    <div class="footer">
+        <div class="footer-content">
+            <div class="footer-text"> 2024 | All rights reserved</div>
+            <div class="social-links">
+                <a href="https://www.facebook.com/Pyresearch" target="_blank">
+                    <img src="" alt="Facebook">
+                </a>
+                <a href="https://www.youtube.com/Pyresearch" target="_blank">
+                    <img src="" alt="YouTube">
+                </a>
+                <a href="https://www.instagram.com/pyresearch/" target="_blank">
+                    <img src="" alt="Instagram">
+                </a>
+                <a href="https://www.tiktok.com/@pyresearch2" target="_blank">
+                    <img src="" alt="TikTok">
+                </a>
+            </div>
+        </div>
+    </div>
+</body>
+</html>
+
+

yolov8l.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18218ea4798da042d9862e6029ca9531adbd40ace19b6c9a75e2e28f1adf30cc
+size 87769683