import numpy as np
import cv2
c=0.5
net = cv2.dnn.readNetFromCaffe("C:/Users/Administrator/Downloads/face-recognition-using-deep-learning-master/face-recognition-using-deep-learning-master/face_detection_model/deploy.prototxt","C:/Users/Administrator/Downloads/face-recognition-using-deep-learning-master/face-recognition-using-deep-learning-master/face_detection_model/res10_300x300_ssd_iter_140000.caffemodel ")
image = cv2.imread("C:/Users/Administrator/Desktop/caffemodel//id1.png")
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,
	(300, 300), (104.0, 177.0, 123.0))
print("[INFO] computing object detections...")
net.setInput(blob)
detections = net.forward()
for i in range(0, detections.shape[2]):
	confidence = detections[0, 0, i, 2]
	if confidence > c :
		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)
		y = startY - 10 if startY - 10 > 10 else startY + 10
		cv2.rectangle(image, (startX, startY), (endX, endY),
			(0, 0, 255), 2)
		cv2.putText(image, text, (startX, y),
			cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
cv2.imshow("Output", image)
cv2.waitKey(0)
