上传新文件

data/1.OpenCV初阶/7.OpenCV中的深度学习/3.人脸检测/detect_faces.py

+import numpy as np
+import cv2
+
+low_confidence=0.5
+image_path='2.jpg'
+proto_txt='deploy.proto.txt'
+model_path='res10_300x300_ssd_iter_140000_fp16.caffemodel'
+# 加载模型
+print("[INFO] loading model...")
+net = cv2.dnn.readNetFromCaffe(proto_txt, model_path)
+# 加载输入图像并为图像构建一个输入 blob
+# 将大小调整为固定的 300x300 像素，然后对其进行标准化
+image = cv2.imread(image_path)
+(h, w) = image.shape[:2]
+blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0,
+	(300, 300), (104.0, 177.0, 123.0))
+# 通过网络传递blob并获得检测和预测
+print("[INFO] computing object detections...")
+net.setInput(blob)
+detections = net.forward()
+# 循环检测
+for i in range(0, detections.shape[2]):
+	# 提取与相关的置信度（即概率）
+	# 预测
+	confidence = detections[0, 0, i, 2]
+	# 通过确保“置信度”来过滤掉弱检测
+	# 大于最小置信度
+	if confidence > low_confidence:
+		# 计算边界框的 (x, y) 坐标
+		box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+		(startX, startY, endX, endY) = box.astype("int")
+		# 绘制人脸的边界框以及概率
+		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)
+# 展示图片并保存
+cv2.imshow("Output", image)
+cv2.imwrite("01.jpg",image)
+cv2.waitKey(0)
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