app_dnn.py

import numpy as np
import cv2

# Load the pre-trained model
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe('deploy.prototxt.txt', 'res10_300x300_ssd_iter_140000.caffemodel')

# Open a connection to the webcam (use 0 for the default camera)
cap = cv2.VideoCapture(0)

count = 0

while True:
    # Read a frame from the webcam
    ret, frame = cap.read()

    # Resize the frame to a fixed 300x300 pixels and normalize it
    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))

    # Pass the blob through the network and obtain the detections
    net.setInput(blob)
    detections = net.forward()

    # Loop over the detections
    for i in range(0, detections.shape[2]):
        confidence = detections[0, 0, i, 2]

        # Filter out weak detections
        if confidence > 0.5:  # Adjust the confidence threshold as needed
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            text = "{:.2f}%".format(confidence * 100) + ' Count ' + str(count)
            y = startY - 10 if startY - 10 > 10 else startY + 10
            cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 0, 255), 2)
            cv2.putText(frame, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)

    # Display the output frame
    cv2.imshow("Webcam", frame)

    # Break the loop if 'q' key is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the webcam and close all OpenCV windows
cap.release()
cv2.destroyAllWindows()