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+# -*- coding:utf-8 -*-
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+import os
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+
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+
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+import json
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+import tensorflow as tf
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+import src.facenet
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+import src.align.detect_face
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+import numpy as np
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+from scipy import misc
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+import face_mysql
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+
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+
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+class face_reconition:
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+ def __init__(self):
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+ pass
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+
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+ def prewhiten(self, x):
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+ mean = np.mean(x)
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+ std = np.std(x)
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+ std_adj = np.maximum(std, 1.0 / np.sqrt(x.size))
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+ y = np.multiply(np.subtract(x, mean), 1 / std_adj)
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+ return y
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+
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+ # 根据路径获取该文件夹中所有的图片
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+ def get_image_paths(self, inpath):
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+ paths = []
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+ for file in os.listdir(inpath):
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+ if os.path.isfile(os.path.join(inpath, file)):
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+ if file.lower().endswith(('.png', '.jpg', '.jpeg')) is False:
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+ continue
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+
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+ paths.append(os.path.join(inpath, file))
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+
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+ return (paths)
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+
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+
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+ # 将一个文件夹下的所有图片转化为json 方法二 只能是传入文件夹 并存入数据库
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+ def images_to_vectors(self, inpath, outjson_path, modelpath):
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+ results = dict()
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+
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+ with tf.Graph().as_default():
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+ with tf.Session() as sess:
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+ src.facenet.load_model(modelpath)
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+ # Get input and output tensors
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+ images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
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+ embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
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+ phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
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+
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+ image_paths = self.get_image_paths(inpath)
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+ for image_path in image_paths:
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+ # 获取图片中的人脸数
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+ img = misc.imread(os.path.expanduser(image_path), mode='RGB')
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+ images = self.image_array_align_data(img,image_path)
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+ #判断是否检测出人脸 检测不出 就跳出此循环
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+ if images.shape[0] == 1 : continue
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+ feed_dict = {images_placeholder: images, phase_train_placeholder: False}
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+
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+ emb_array = sess.run(embeddings, feed_dict=feed_dict)
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+
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+ filename_base, file_extension = os.path.splitext(image_path)
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+ for j in range(0, len(emb_array)):
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+ results[filename_base + "_" + str(j)] = emb_array[j].tolist()
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+ face_mysql_instant = face_mysql.face_mysql()
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+ face_mysql_instant.insert_facejson(filename_base + "_" + str(j),
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+ ",".join(str(li) for li in emb_array[j].tolist()))
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+
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+ # All done, save for later!
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+ json.dump(results, open(outjson_path, "w"))
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+ # 返回图像中所有人脸的向量
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+
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+ def image_array_align_data(self, image_arr,image_path, image_size=160, margin=32, gpu_memory_fraction=1.0,
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+ detect_multiple_faces=True):
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+ minsize = 20 # minimum size of face
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+ threshold = [0.6, 0.7, 0.7] # three steps's threshold
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+ factor = 0.709 # scale factor
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+
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+ print('Creating networks and loading parameters')
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+ with tf.Graph().as_default():
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+ gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
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+ sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
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+ with sess.as_default():
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+ pnet, rnet, onet = src.align.detect_face.create_mtcnn(sess, None)
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+
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+ img = image_arr
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+ bounding_boxes, _ = src.align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
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+ nrof_faces = bounding_boxes.shape[0]
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+
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+ nrof_successfully_aligned = 0
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+ if nrof_faces > 0:
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+ det = bounding_boxes[:, 0:4]
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+ det_arr = []
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+ img_size = np.asarray(img.shape)[0:2]
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+ if nrof_faces > 1:
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+ if detect_multiple_faces:
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+ for i in range(nrof_faces):
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+ det_arr.append(np.squeeze(det[i]))
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+ else:
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+ bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])
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+ img_center = img_size / 2
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+ offsets = np.vstack(
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+ [(det[:, 0] + det[:, 2]) / 2 - img_center[1], (det[:, 1] + det[:, 3]) / 2 - img_center[0]])
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+ offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
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+ index = np.argmax(
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+ bounding_box_size - offset_dist_squared * 2.0) # some extra weight on the centering
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+ det_arr.append(det[index, :])
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+ else:
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+ det_arr.append(np.squeeze(det))
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+
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+ images = np.zeros((nrof_faces, image_size, image_size, 3))
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+ for i, det in enumerate(det_arr):
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+ det = np.squeeze(det)
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+ bb = np.zeros(4, dtype=np.int32)
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+ bb[0] = np.maximum(det[0] - margin / 2, 0)
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+ bb[1] = np.maximum(det[1] - margin / 2, 0)
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+ bb[2] = np.minimum(det[2] + margin / 2, img_size[1])
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+ bb[3] = np.minimum(det[3] + margin / 2, img_size[0])
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+ cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
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+ # 进行图片缩放 cv2.resize(img,(w,h))
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+ scaled = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
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+ nrof_successfully_aligned += 1
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+
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+ # print(scaled)
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+ # scaled=self.prewhiten(scaled)
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+ # 保存检测的头像
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+ filename_base = './img/'
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+ filename = os.path.basename(image_path)
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+ filename_name, file_extension = os.path.splitext(filename)
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+ output_filename_n = "{}/{}_{}{}".format(filename_base, filename_name, i, file_extension)
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+ misc.imsave(output_filename_n, scaled)
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+
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+
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+ scaled = src.facenet.prewhiten(scaled)
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+ scaled = src.facenet.crop(scaled, False, 160)
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+ scaled = src.facenet.flip(scaled, False)
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+
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+ images[i] = scaled
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+ if nrof_faces > 0:
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+ return images
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+ else:
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+ #如果没有检测到人脸 直接返回一个1*3的0矩阵 多少维度都行 只要能和是不是一个图片辨别出来就行
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+ return np.zeros((1,3))
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+
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+
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+
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+if __name__ == "__main__":
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+ face_reconition = face_reconition()
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+
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+ images_path = './img/img'
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+ #模型地址
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+ modelpath = '/export/zang/facenet/models/facenet/20170512-110547'
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+ out_path = './img/pic.json'
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+ face_reconition.images_to_vectors(images_path, out_path, modelpath)
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zangruirui