Smart-Coop-V2/egg_testing.py at main · CodersCafeTech/Smart-Coop-V2 · GitHub

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#!/usr/bin/env python3
"""
Egg Detection Preview - Browser Stream
Open http://<arduino-ip>:8081 in your browser
"""

import time
import cv2
import numpy as np
from http.server import HTTPServer, BaseHTTPRequestHandler
from threading import Thread

print("\n" + "="*50)
print("  EGG DETECTION PREVIEW")
print("  Browser Stream Version")
print("="*50 + "\n")

# ===== CONFIGURATION =====
CAMERA_DEVICE = '/dev/video-eggs'
MODEL_PATH = 'best_eggs.onnx'
DETECTION_CONFIDENCE = 0.5
STREAM_PORT = 8081

CLASSES = {0: 'egg'}

BOX_COLOR = (0, 255, 255)
TEXT_COLOR = (0, 0, 0)

current_frame = None


class YOLODetector:
    def __init__(self, model_path, classes_dict):
        self.classes = classes_dict
        try:
            self.net = cv2.dnn.readNetFromONNX(model_path)
            self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
            self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)
            self.img_size = 320
            self.enabled = True
            print(f"[DETECTOR] ✓ Model loaded: {model_path}")
        except Exception as e:
            print(f"[DETECTOR] ✗ Failed: {e}")
            self.enabled = False

    def detect(self, frame):
        if not self.enabled:
            return []

        try:
            orig_h, orig_w = frame.shape[:2]

            blob = cv2.dnn.blobFromImage(
                frame, 1/255.0, (self.img_size, self.img_size),
                swapRB=True, crop=False
            )

            self.net.setInput(blob)
            outputs = self.net.forward()
            predictions = outputs[0].T

            scale_x = orig_w / self.img_size
            scale_y = orig_h / self.img_size

            detections = []

            for pred in predictions:
                x_center, y_center, w, h = pred[:4]
                class_scores = pred[4:]
                class_id = np.argmax(class_scores)
                confidence = class_scores[class_id]

                if confidence > DETECTION_CONFIDENCE:
                    x1 = int((x_center - w/2) * scale_x)
                    y1 = int((y_center - h/2) * scale_y)
                    x2 = int((x_center + w/2) * scale_x)
                    y2 = int((y_center + h/2) * scale_y)

                    x1, y1 = max(0, x1), max(0, y1)
                    x2, y2 = min(orig_w, x2), min(orig_h, y2)

                    detections.append({
                        'class_name': self.classes.get(class_id, f'Class_{class_id}'),
                        'confidence': float(confidence),
                        'bbox': (x1, y1, x2, y2)
                    })

            if detections:
                boxes = [d['bbox'] for d in detections]
                scores = [d['confidence'] for d in detections]
                indices = cv2.dnn.NMSBoxes(boxes, scores, DETECTION_CONFIDENCE, 0.5)
                if len(indices) > 0:
                    detections = [detections[i] for i in indices.flatten()]

            return detections

        except Exception as e:
            print(f"[DETECTOR] Error: {e}")
            return []


def draw_boxes(frame, detections):
    for det in detections:
        x1, y1, x2, y2 = det['bbox']
        label = f"{det['class_name']}: {det['confidence']:.0%}"

        cv2.rectangle(frame, (x1, y1), (x2, y2), BOX_COLOR, 2)

        (text_w, text_h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)
        cv2.rectangle(frame, (x1, y1 - text_h - 10), (x1 + text_w + 6, y1), BOX_COLOR, -1)
        cv2.putText(frame, label, (x1 + 3, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, TEXT_COLOR, 2)

    return frame


class StreamHandler(BaseHTTPRequestHandler):
    def do_GET(self):
        if self.path == '/':
            self.send_response(200)
            self.send_header('Content-type', 'text/html')
            self.end_headers()
            self.wfile.write(b'''
                <html><head><title>Egg Detection</title></head>
                <body style="margin:0;background:#000;display:flex;justify-content:center;align-items:center;height:100vh;">
                <img src="/stream" style="max-width:100%;max-height:100%;">
                </body></html>
            ''')
        elif self.path == '/stream':
            self.send_response(200)
            self.send_header('Content-type', 'multipart/x-mixed-replace; boundary=frame')
            self.end_headers()

            while True:
                if current_frame is not None:
                    try:
                        _, jpeg = cv2.imencode('.jpg', current_frame, [cv2.IMWRITE_JPEG_QUALITY, 80])
                        self.wfile.write(b'--frame\r\n')
                        self.wfile.write(b'Content-Type: image/jpeg\r\n\r\n')
                        self.wfile.write(jpeg.tobytes())
                        self.wfile.write(b'\r\n')
                    except:
                        break
                time.sleep(0.03)

    def log_message(self, format, *args):
        pass


def camera_loop(detector):
    global current_frame

    print(f"[CAMERA] Opening {CAMERA_DEVICE}...")
    cap = cv2.VideoCapture(CAMERA_DEVICE, cv2.CAP_V4L2)
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
    cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M','J','P','G'))
    cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)

    if not cap.isOpened():
        print("[CAMERA] ✗ Failed to open camera")
        return

    print("[CAMERA] ✓ Camera opened")

    frame_count = 0
    fps_time = time.time()
    fps = 0
    egg_count = 0
    fail_count = 0

    try:
        while True:
            ret, frame = cap.read()

            if not ret:
                fail_count += 1
                if fail_count % 100 == 0:
                    print(f"[CAMERA] ⚠ Frame read failed {fail_count} times")
                time.sleep(0.01)
                continue

            fail_count = 0
            frame_count += 1

            # Debug: print every 30 frames
            if frame_count % 30 == 0:
                fps = 30 / (time.time() - fps_time)
                fps_time = time.time()
                print(f"[CAMERA] Frame {frame_count} | {fps:.1f} FPS")

            detections = detector.detect(frame)

            if detections:
                frame = draw_boxes(frame, detections)
                if len(detections) != egg_count:
                    egg_count = len(detections)
                    print(f"[DETECTED] 🥚 {egg_count} egg(s)")
            else:
                if egg_count != 0:
                    egg_count = 0

            #cv2.putText(frame, f"Eggs: {len(detections)} | FPS: {fps:.1f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)

            current_frame = frame.copy()
            time.sleep(0.03)

    except Exception as e:
        print(f"[CAMERA] ✗ Error: {e}")
    finally:
        cap.release()
        print("[CAMERA] Released")


def main():
    detector = YOLODetector(MODEL_PATH, CLASSES)

    # Start camera thread
    cam_thread = Thread(target=camera_loop, args=(detector,), daemon=True)
    cam_thread.start()

    # Wait a moment for camera to start
    time.sleep(2)

    if current_frame is None:
        print("[WARNING] No frames yet - camera may be stuck")

    server = HTTPServer(('0.0.0.0', STREAM_PORT), StreamHandler)
    print(f"[STREAM] Open in browser: http://<arduino-ip>:{STREAM_PORT}")
    print("[STREAM] Press Ctrl+C to stop\n")

    try:
        server.serve_forever()
    except KeyboardInterrupt:
        print("\n[STREAM] Stopped")


if __name__ == "__main__":
    main()