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eyemove_ment/mobile.py

+import cv2
+
+# Load the pre-trained model
+net = cv2.dnn.readNetFromCaffe("mobilenet_iter_73000.caffemodel")
+
+# Load the video
+cap = cv2.VideoCapture(0)
+
+while True:
+    # Read the frame
+    _, frame = cap.read()
+
+    # Get the dimensions of the frame
+    height, width = frame.shape[:2]
+
+    # Create a blob from the frame
+    blob = cv2.dnn.blobFromImage(frame, 0.007843, (width, height), (127.5, 127.5, 127.5), False)
+
+    # Pass the blob through the model
+    net.setInput(blob)
+    detections = net.forward()
+
+    # Loop over the detections
+    for i in range(detections.shape[2]):
+        # Get the confidence of the detection
+        confidence = detections[0, 0, i, 2]
+
+        # Filter out weak detections
+        if confidence > 0.5:
+            # Get the class label of the detection
+            class_id = int(detections[0, 0, i, 1])
+
+            # Check if the class label is a mobile phone
+            if class_id == 7:
+                # Get the bounding box of the detection
+                x1 = int(detections[0, 0, i, 3] * width)
+                y1 = int(detections[0, 0, i, 4] * height)
+                x2 = int(detections[0, 0, i, 5] * width)
+                y2 = int(detections[0, 0, i, 6] * height)
+
+                # Draw a rectangle around the detection
+                cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 2)
+
+    # Show the frame
+    cv2.imshow("Mobile Phone Detection", frame)
+
+    # Exit if the user presses 'q'
+    if cv2.waitKey(1) == ord('q'):
+        break