objedet.py

import numpy as np
import cv2

prototxt_path = 'MobileNetSSD_deploy.prototxt'
model_path = 'MobileNetSSD_deploy.caffemodel'

min_confidence = 0.2

classes = ['background',
           'aeroplane', 'bicycle', 'bird', 'boat',
           'bottle', 'bus', 'car', 'cat', 'chair',
           'cow', 'diningtable', 'dog', 'horse',
           'motorbike', 'person', 'pottedplant',
           'sheep', 'sofa', 'train', 'tvmonitor']

np.random.seed(543210)
colors = np.random.uniform(0, 255, size=(len(classes), 3))

net = cv2.dnn.readNetFromCaffe(prototxt_path, model_path)

cap = cv2.VideoCapture(0)

while True:
    ret, image = cap.read()

    height, width = image.shape[0], image.shape[1]

    blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007, (300, 300), 130)

    net.setInput(blob)

    detected_objects = net.forward()

    for i in range(detected_objects.shape[2]):
        confidence = detected_objects[0][0][i][2]

        if confidence > min_confidence:
            class_index = int(detected_objects[0, 0, i, 1])
            
            upper_left_x = int(detected_objects[0, 0, i, 3] * width)
            upper_left_y = int(detected_objects[0, 0, i, 4] * height)
            lower_right_x = int(detected_objects[0, 0, i, 5] * width)
            lower_right_y = int(detected_objects[0, 0, i, 6] * height)
            roundConfi = confidence * 100
            predection_text = f"{classes[class_index]}: {roundConfi:.2f}%"

            cv2.rectangle(image, (upper_left_x, upper_left_y), (lower_right_x, lower_right_y), colors[class_index], 3)
            cv2.putText(image, predection_text, (upper_left_x, upper_left_y - 15 if upper_left_y > 30 else upper_left_y + 15),
            cv2.FONT_HERSHEY_SIMPLEX, 0.6, colors[class_index], 2)

            cv2.imshow("Detected", image)
            cv2.waitKey(1)

cv2.destroyAllWindows()
cv2.release()