Update app.py

app.py

@@ -1,73 +1,3 @@
-import torch
-import cv2
-import pyttsx3
-import random
-
-# Download model from GitHub
-model = torch.hub.load('ultralytics/yolov5', 'yolov5n')
-
-# Initialize video capture
-cap = cv2.VideoCapture('cars.mp4')
-
-# Initialize text-to-speech engine
-engine = pyttsx3.init()
-
-# Simulated GPS location (latitude, longitude)
-gps_location = (37.7749, -122.4194)  # Example coordinates for San Francisco
-
-# Function to speak the detected object
-def speak(text):
-    engine.say(text)
-    engine.runAndWait()
-
-while True:
-    ret, img = cap.read()
-    if not ret:
-        break
-
-    # Perform detection on the image
-    result = model(img)
-    print('result: ', result)
-
-    # Convert detected result to pandas DataFrame
-    data_frame = result.pandas().xyxy[0]
-    print('data_frame:')
-    print(data_frame)
-
-    # Get indexes of all the rows
-    indexes = data_frame.index
-    for index in indexes:
-        # Find the coordinate of top left corner of bounding box
-        x1 = int(data_frame['xmin'][index])
-        y1 = int(data_frame['ymin'][index])
-        # Find the coordinate of bottom right corner of bounding box
-        x2 = int(data_frame['xmax'][index])
-        y2 = int(data_frame['ymax'][index])
-
-        # Find label name and confidence score
-        label = data_frame['name'][index]
-        conf = data_frame['confidence'][index]
-        text = f"{label} {conf:.2f}"
-
-        # Draw bounding box and label on the image
-        cv2.rectangle(img, (x1, y1), (x2, y2), (255, 255, 0), 2)
-        cv2.putText(img, text, (x1, y1 - 5), cv2.FONT_HERSHEY_PLAIN, 2, (255, 255, 0), 2)
-
-        # Context-aware actions based on detected objects
-        if label == "car" and conf > 0.5:
-            # Announce detected car and GPS location
-            speak(f"Car detected at GPS location: {gps_location[0]}, {gps_location[1]}")
-            # Here you can add more context-based features (e.g., alerting, saving data, etc.)
-
-    # Display GPS coordinates on the image
-    gps_text = f"GPS: {gps_location[0]:.4f}, {gps_location[1]:.4f}"
-    cv2.putText(img, gps_text, (10, 30), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0), 2)
-
-    # Show the processed image
-    cv2.imshow('IMAGE', img)
-    if cv2.waitKey(1) & 0xFF == ord('q'):
-        break
-
-# Release resources
-cap.release()
-cv2.destroyAllWindows()
+git clone https://github.com/ultralytics/yolov5.git
+cd yolov5
+pip install -r requirements.txt