79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
import cv2 as cv
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import numpy as np
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import dlib
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import joblib
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# 画框函数
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def draw_rect(img, faces):
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for face in faces:
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cv.rectangle(img, (face.left(), face.top()), (face.right(), face.bottom()), (0, 0, 255), 32)
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return img
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# 检测人脸并画框
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def detect_face(img):
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img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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result = detector(img_gray, 1)
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img_result = draw_rect(img, result)
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return img_result
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# 提取人脸特征
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def extract_face_feature(img):
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faces = detector(img)
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if len(faces) > 0:
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face = faces[0]
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landmarks = predictor(img, face)
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face_descriptor = face_rec_model.compute_face_descriptor(img, landmarks)
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return np.array(face_descriptor)
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else:
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return None
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# 加载分类器
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classifier = joblib.load('models/classifier.pkl')
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# 读取测试图片
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positive_sample = cv.imread('cache/positive_test.jpg')
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positive_sample_rgb = cv.cvtColor(positive_sample, cv.COLOR_BGR2RGB)
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negative_sample = cv.imread('cache/love.jpg')
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negative_sample_rgb = cv.cvtColor(negative_sample, cv.COLOR_BGR2RGB)
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# 加载dlib人脸检测模型
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detector = dlib.get_frontal_face_detector()
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predictor = dlib.shape_predictor("models/shape_predictor_68_face_landmarks_GTX.dat")
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face_rec_model = dlib.face_recognition_model_v1('models/dlib_face_recognition_resnet_model_v1.dat')
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# 检测人脸
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positive_result = detect_face(positive_sample)
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negative_result = detect_face(negative_sample)
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# 计算人脸特征
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positive_feature = extract_face_feature(positive_sample_rgb)
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positive_feature = np.array(positive_feature).reshape(1, -1)
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negative_feature = extract_face_feature(negative_sample_rgb)
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negative_feature = np.array(negative_feature).reshape(1, -1)
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# 通过分类器预测
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positive_predict = classifier.predict(positive_feature)
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negative_predict = classifier.predict(negative_feature)
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# 展示
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positive_result_resized = cv.resize(positive_result,
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(int(positive_result.shape[1] * 0.1), int(positive_result.shape[0] * 0.1)))
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positive_result_resized = cv.putText(positive_result_resized, str(positive_predict), (50, 50), cv.FONT_HERSHEY_SIMPLEX,
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1, (0, 0, 255), 2)
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negative_result_resized = cv.resize(negative_result,
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(int(negative_result.shape[1] * 0.1), int(negative_result.shape[0] * 0.1)))
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negative_result_resized = cv.putText(negative_result_resized, str(negative_predict), (50, 50), cv.FONT_HERSHEY_SIMPLEX,
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1, (0, 0, 255), 2)
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cv.imshow('positive_result', positive_result_resized)
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cv.imshow('negative_result', negative_result_resized)
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cv.waitKey(0)
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cv.destroyAllWindows()
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