Create app1.py

app1.py

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+import cv2
+import numpy as np
+
+# Load the SSD model and configuration
+model_path = 'ssd_mobilenet_v2_coco.pb'   # Path to the pre-trained SSD model
+config_path = 'deploy.prototxt'           # Path to the deploy prototxt file
+
+# Load the class labels from the COCO dataset
+CLASSES = [
+    'background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 
+    'truck', 'boat', 'traffic light', 'fire hydrant', 'none', 'stop sign', 'parking meter', 
+    'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 
+    'giraffe', 'none', 'backpack', 'umbrella', 'none', 'handbag', 'tie', 'suitcase', 
+    'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 
+    'skateboard', 'surfboard', 'tennis racket', 'bottle', 'none', 'wine glass', 'cup', 
+    'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 
+    'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 
+    'bed', 'dining table', 'toilet', 'none', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 
+    'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 
+    'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'
+]
+
+# Initialize the OpenCV DNN network
+net = cv2.dnn.readNetFromTensorflow(model_path, config_path)
+
+# Function to process the video frame and detect objects
+def detect_objects_in_frame(frame):
+    # Get the image shape
+    height, width = frame.shape[:2]
+    
+    # Prepare the frame for the model (mean subtraction and resizing)
+    blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (127.5, 127.5, 127.5), swapRB=True, crop=False)
+    
+    # Set the blob as input to the network
+    net.setInput(blob)
+    
+    # Run the forward pass to get predictions
+    detections = net.forward()
+
+    # Loop through all the detections
+    for i in range(detections.shape[2]):
+        confidence = detections[0, 0, i, 2]
+
+        if confidence > 0.5:  # Set a threshold for object detection
+            # Get the class index and the bounding box coordinates
+            class_id = int(detections[0, 0, i, 1])
+            left = int(detections[0, 0, i, 3] * width)
+            top = int(detections[0, 0, i, 4] * height)
+            right = int(detections[0, 0, i, 5] * width)
+            bottom = int(detections[0, 0, i, 6] * height)
+            
+            # Draw the bounding box and label on the frame
+            label = f"{CLASSES[class_id]}: {confidence:.2f}"
+            cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
+            cv2.putText(frame, label, (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+    return frame
+
+# Capture video from a file or camera (0 for the default camera)
+cap = cv2.VideoCapture(0)  # Use 0 for webcam or provide a path to a video file
+
+# Check if the video capture is initialized correctly
+if not cap.isOpened():
+    print("Error: Could not open video stream.")
+    exit()
+
+while True:
+    # Read a new frame from the video feed
+    ret, frame = cap.read()
+    
+    if not ret:
+        print("Error: Failed to read frame from video stream.")
+        break
+    
+    # Detect objects in the current frame
+    output_frame = detect_objects_in_frame(frame)
+
+    # Display the resulting frame
+    cv2.imshow("Detected Objects in Video", output_frame)
+    
+    # Break the loop if the user presses the 'Esc' key
+    if cv2.waitKey(1) & 0xFF == 27:  # 27 is the keycode for 'Esc'
+        break