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htmlcss项目实战源码_基于TensorFlow深度学习人脸识别源码级项目实战

news 来源：原创 2024/5/18 0:55:22

人脸年龄识别属于人脸属性识别的范畴，人脸属性识别可对图片中的人脸进行检测定位，并识别出人脸的相关属性(如年龄、性别、表情、种族、颜值等)内容。不同属性识别的算法可以相同，也可以不同。rude-carnie是做年龄识别和性别识别的一个开源项目，基于TensorFlow，源代码网址:http://www.github.com/dpressel/rude-carnie。下面我们基于这个项目源码来讲年龄识别。

1.年龄识别核心代码guess.py

我们可以把年龄划分为几个段['(0, 2)','(4, 6)','(8, 12)','(15, 20)','(25, 32)','(38, 43)','(48, 53)','(60, 100)']，然后基于分类的思想来做年龄预测问题。用下面的脚本命令：

python3 guess.py --model_type inception --model_dir /home/hadoop/chongdianleme/ nianling/22801/inception/22801 --filename /home/hadoop/chongdianleme/data/myimages/baidu1.jpg

基于训练好的年龄模型和人脸图片就能预测出年龄。但是有一个问题，直接这样预测不是很准，因为图片没有经过任何处理。我们可以通过opencv和上面讲到的FaceNet的人脸检测和对齐算法来做。但opencv比较简单，FaceNet的人脸检测和对齐效果比较好，我们可以使用前面提供的http服务接口http://172.17.100.216:8816/detectAndAlignedService来处理，然后把检测和对齐后的人脸图片传给guess.py，这样预测处理的效果精准很多。

另外一个问题是，因为训练的模型用的是开源项目训练好的，是拿外国人的人脸数据训练，这样用来预测我们中国人的年龄会有一些差异，最好的方式是拿我们中国人自己的人脸年龄数据做训练，这样预测才会更好。

针对年龄识别和性别识别都是用guess.py这个文件，年龄识别是多分类，性别是二分类。我们看下guess.py的源码，如代码8.5所示：

【代码8.5】 guess.py

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionfrom datetime import datetimeimport mathimport timefrom data import inputsimport numpy as npimport tensorflow as tffrom model import select_model, get_checkpointfrom utils import *import osimport jsonimport csv
RESIZE_FINAL = 227#性别有两个
GENDER_LIST =['M','F']#年龄是分段的，可以看成多分类任务
AGE_LIST = ['(0, 2)','(4, 6)','(8, 12)','(15, 20)','(25, 32)','(38, 43)','(48, 53)','(60, 100)']
MAX_BATCH_SZ = 128#模型文件目录
tf.app.flags.DEFINE_string('model_dir', '','Model directory (where training data lives)')#性别和年龄都是用的这个，通过参数age|gender来区分
tf.app.flags.DEFINE_string('class_type', 'age','Classification type (age|gender)')#用cpu还是用GPU来训练
tf.app.flags.DEFINE_string('device_id', '/cpu:0','What processing unit to execute inference on')
tf.app.flags.DEFINE_string('filename', '','File (Image) or File list (Text/No header TSV) to process')
tf.app.flags.DEFINE_string('target', '','CSV file containing the filename processed along with best guess and score')#检查点
tf.app.flags.DEFINE_string('checkpoint', 'checkpoint','Checkpoint basename')
tf.app.flags.DEFINE_string('model_type', 'default','Type of convnet')
tf.app.flags.DEFINE_string('requested_step', '', 'Within the model directory, a requested step to restore e.g., 9000')
tf.app.flags.DEFINE_boolean('single_look', False, 'single look at the image or multiple crops')
tf.app.flags.DEFINE_string('face_detection_model', '', 'Do frontal face detection with model specified')
tf.app.flags.DEFINE_string('face_detection_type', 'cascade', 'Face detection model type (yolo_tiny|cascade)')
FLAGS = tf.app.flags.FLAGSdef one_of(fname, types):return any([fname.endswith('.' + ty) for ty in types])def resolve_file(fname):if os.path.exists(fname): return fnamefor suffix in ('.jpg', '.png', '.JPG', '.PNG', '.jpeg'):
cand = fname + suffixif os.path.exists(cand):return candreturn Nonedef classify_many_single_crop(sess, label_list, softmax_output, coder, images, image_files, writer):try:
num_batches = math.ceil(len(image_files) / MAX_BATCH_SZ)
pg = ProgressBar(num_batches)for j in range(num_batches):
start_offset = j * MAX_BATCH_SZ
end_offset = min((j + 1) * MAX_BATCH_SZ, len(image_files))
batch_image_files = image_files[start_offset:end_offset]
print(start_offset, end_offset, len(batch_image_files))
image_batch = make_multi_image_batch(batch_image_files, coder)
batch_results = sess.run(softmax_output, feed_dict={images:image_batch.eval()})
batch_sz = batch_results.shape[0]for i in range(batch_sz):
output_i = batch_results[i]
best_i = np.argmax(output_i)
best_choice = (label_list[best_i], output_i[best_i])
print('Guess @ 1 %s, prob = %.2f' % best_choice)if writer is not None:
f = batch_image_files[i]
writer.writerow((f, best_choice[0], '%.2f' % best_choice[1]))
pg.update()
pg.done()except Exception as e:
print(e)
print('Failed to run all images')def classify_one_multi_crop(sess, label_list, softmax_output, coder, images, image_file, writer):try:
print('Running file %s' % image_file)
image_batch = make_multi_crop_batch(image_file, coder)
batch_results = sess.run(softmax_output, feed_dict={images:image_batch.eval()})
output = batch_results[0]
batch_sz = batch_results.shape[0]for i in range(1, batch_sz):
output = output + batch_results[i]
output /= batch_sz
best = np.argmax(output)
best_choice = (label_list[best], output[best])
print('Guess @ 1 %s, prob = %.2f' % best_choice)
nlabels = len(label_list)if nlabels > 2:
output[best] = 0
second_best = np.argmax(output)
print('Guess @ 2 %s, prob = %.2f' % (label_list[second_best], output[second_best]))if writer is not None:
writer.writerow((image_file, best_choice[0], '%.2f' % best_choice[1]))except Exception as e:
print(e)
print('Failed to run image %s ' % image_file)def list_images(srcfile):with open(srcfile, 'r') as csvfile:
delim = ',' if srcfile.endswith('.csv') else 't'
reader = csv.reader(csvfile, delimiter=delim)if srcfile.endswith('.csv') or srcfile.endswith('.tsv'):
print('skipping header')
_ = next(reader)return [row[0] for row in reader]def main(argv=None): # pylint: disable=unused-argument
files = []
print("target %s" % FLAGS.target)if FLAGS.face_detection_model:
print('Using face detector (%s) %s' % (FLAGS.face_detection_type, FLAGS.face_detection_model))
face_detect = face_detection_model(FLAGS.face_detection_type, FLAGS.face_detection_model)
face_files, rectangles = face_detect.run(FLAGS.filename)
print(face_files)
files += face_files
config = tf.ConfigProto(allow_soft_placement=True)with tf.Session(config=config) as sess:
label_list = AGE_LIST if FLAGS.class_type == 'age' else GENDER_LIST
nlabels = len(label_list)
print('Executing on %s' % FLAGS.device_id)
model_fn = select_model(FLAGS.model_type)with tf.device(FLAGS.device_id):
images = tf.placeholder(tf.float32, [None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits = model_fn(nlabels, images, 1, False)
init = tf.global_variables_initializer()
requested_step = FLAGS.requested_step if FLAGS.requested_step else None
checkpoint_path = '%s' % (FLAGS.model_dir)
model_checkpoint_path, global_step = get_checkpoint(checkpoint_path, requested_step, FLAGS.checkpoint)
saver = tf.train.Saver()
saver.restore(sess, model_checkpoint_path)
softmax_output = tf.nn.softmax(logits)
coder = ImageCoder()# Support a batch mode if no face detection modelif len(files) == 0:if (os.path.isdir(FLAGS.filename)):for relpath in os.listdir(FLAGS.filename):
abspath = os.path.join(FLAGS.filename, relpath)if os.path.isfile(abspath) and any([abspath.endswith('.' + ty) for ty in ('jpg', 'png', 'JPG', 'PNG', 'jpeg')]):
print(abspath)
files.append(abspath)else:
files.append(FLAGS.filename)# If it happens to be a list file, read the list and clobber the filesif any([FLAGS.filename.endswith('.' + ty) for ty in ('csv', 'tsv', 'txt')]):
files = list_images(FLAGS.filename)
writer = None
output = None if FLAGS.target:
print('Creating output file %s' % FLAGS.target)
output = open(FLAGS.target, 'w')
writer = csv.writer(output)
writer.writerow(('file', 'label', 'score'))
image_files = list(filter(lambda x: x is not None, [resolve_file(f) for f in files]))
print(image_files)if FLAGS.single_look:
classify_many_single_crop(sess, label_list, softmax_output, coder, images, image_files, writer)else:for image_file in image_files:
classify_one_multi_crop(sess, label_list, softmax_output, coder, images, image_file, writer)if output is not None:
output.close()if __name__ == '__main__':
tf.app.run()

2.年龄识别Web工程化代码

年龄识别我们对外提供一个Web接口，guessAgeWeb_chongdianleme.py如代码8.6所示：

【代码8.6】 guessAgeWeb_chongdianleme.py

from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionfrom datetime import datetimeimport mathimport timefrom data import inputsimport numpy as npimport tensorflow as tffrom model import select_model, get_checkpointfrom utils import *import osimport csv#pip3 install flaskfrom flask import Flaskfrom flask import requestimport urllib# pip3 install requestsimport requests, urllib.requestfrom scipy import miscimport argparseimport facenetimport align.detect_faceimport json
RESIZE_FINAL = 227#性别有两个
GENDER_LIST = ['M', 'F']#年龄是分段的，可以看成多分类任务
AGE_LIST = ['(0, 2)', '(4, 6)', '(8, 12)', '(15, 20)', '(25, 32)', '(38, 43)', '(48, 53)', '(60, 100)']
MAX_BATCH_SZ = 128def one_of(fname, types):return any([fname.endswith('.' + ty) for ty in types])def resolve_file(fname):if os.path.exists(fname): return fnamefor suffix in ('.jpg', '.png', '.JPG', '.PNG', '.jpeg'):
cand = fname + suffixif os.path.exists(cand):return candreturn Nonedef list_images(srcfile):with open(srcfile, 'r') as csvfile:
delim = ',' if srcfile.endswith('.csv') else 't'
reader = csv.reader(csvfile, delimiter=delim)if srcfile.endswith('.csv') or srcfile.endswith('.tsv'):
print('skipping header')
_ = next(reader)return [row[0] for row in reader]# 初始化
class_type = 'age'
device_id = "/cpu:0"
model_type = "inception"
requested_step = ""#模型文件目录
model_dir = "/home/hadoop/chongdianleme/nianling/22801/inception/22801"
checkpoint = "checkpoint"
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)with sess.as_default():
label_list = AGE_LIST if class_type == 'age' else GENDER_LIST
nlabels = len(label_list)
model_fn = select_model(model_type)
images = tf.placeholder(tf.float32, [None, RESIZE_FINAL, RESIZE_FINAL, 3])
logits = model_fn(nlabels, images, 1, False)
init = tf.global_variables_initializer()
requested_step = requested_step if requested_step else None
checkpoint_path = '%s' % (model_dir)
model_checkpoint_path, global_step = get_checkpoint(checkpoint_path, requested_step, checkpoint)
saver = tf.train.Saver()
saver.restore(sess, model_checkpoint_path)
softmax_output = tf.nn.softmax(logits)
coder = ImageCoder()def _is_png(filename):return '.png' in filename
app = Flask(__name__)
@app.route('/predictAge', methods=['GET', 'POST'])def prediction():
start = time.time()
imageUrl = request.values.get("imageUrl")#用户信息
device = request.values.get("device")
userid = request.values.get("userid")
urlType = request.values.get("urlType")
imageType = request.values.get("imageType")
files = []#支持本地图片和网络图片if urlType=="local":
filename = imageUrlelse:
baseImageName = os.path.basename(imageUrl)
filename = "/home/hadoop/chongdianleme/ageimage/%s" % baseImageName
filename = filename+imageType
urllib.request.urlretrieve(imageUrl, filename)#通过我们前面讲的FaceNet里的人脸检测和对齐的http接口对图片进行处理，这样识别的年龄更精准
url = "http://172.17.100.216:8816/detectAndAlignedService"
body_value = {"image_file": filename, "alignedImage_files":filename}
data_urlencode = urllib.parse.urlencode(body_value).encode(encoding='UTF8')
request2 = urllib.request.Request(url, data_urlencode)#调用接口
resultJson = urllib.request.urlopen(request2).read().decode('UTF-8')#解析json格式的数据
o = json.loads(resultJson)
ts = o["times"]
i = o["i"]
newFileName = filename.replace(".","_0_.")
files.append(newFileName)
image_files = list(filter(lambda x: x is not None, [resolve_file(f) for f in files]))
print(image_files)
finalAge = 0
avgBest = 0.00
bestProb = 0.00
avgSecond = 0.00
secondProb = 0.00for image_file in image_files:try:
print('Running file %s' % image_file)#image_batch = make_multi_crop_batch(image_file, coder) #startwith tf.gfile.FastGFile(filename, 'rb') as f:
image_data = f.read()# Convert any PNG to JPEG's for consistency.if _is_png(filename):
print('Converting PNG to JPEG for %s' % filename)
image_data = coder.png_to_jpeg(image_data)
image = coder.decode_jpeg(image_data)
crops = []
print('Running multi-cropped image')
h = image.shape[0]
w = image.shape[1]
hl = h - RESIZE_FINAL
wl = w - RESIZE_FINAL
crop = tf.image.resize_images(image, (RESIZE_FINAL, RESIZE_FINAL))
crops.append(standardize_image(crop))
crops.append(tf.image.flip_left_right(crop))
corners = [(0, 0), (0, wl), (hl, 0), (hl, wl), (int(hl / 2), int(wl / 2))]for corner in corners:
ch, cw = corner
cropped = tf.image.crop_to_bounding_box(image, ch, cw, RESIZE_FINAL, RESIZE_FINAL)
crops.append(standardize_image(cropped))
flipped = tf.image.flip_left_right(cropped)
crops.append(standardize_image(flipped))
image_batch = tf.stack(crops)#end
batch_results = sess.run(softmax_output, feed_dict={images:image_batch.eval(session=sess)})
output = batch_results[0]
batch_sz = batch_results.shape[0]for i in range(1, batch_sz):
output = output + batch_results[i]
output /= batch_sz
best = np.argmax(output)
ageClass = label_list[best] # (25, 32)
bestAgeArr = ageClass.replace(" ", "").replace("(", "").replace(")", "").split(",")#AGE_LIST = ['(0, 2)', '(4, 6)', '(8, 12)', '(15, 20)', '(25, 32)', '(38, 43)', '(48, 53)', '(60, 100)'] #因为训练的模型用的是开源项目训练好的，是拿外国人的人脸数据训练， # 这样用来预测我们中国人的年龄会有一些差异，最好的方式是拿我们中国人自己的人脸年龄数据做训练，这样预测才会更好。 # 所以针对外国人训练数据预测我们中国人需要对年龄做一些经验上的特殊处理if int(bestAgeArr[1]) == 53:
avgBest= 56elif int(bestAgeArr[1]) == 43:
avgBest = 45elif int(bestAgeArr[1]) == 20:
avgBest = 22.66elif int(bestAgeArr[1]) == 6:
avgBest = 6.66else:
avgBest = (int(bestAgeArr[0]) + int(bestAgeArr[1])) / 1.66
bestProb = output[best]
best_choice = (label_list[best], output[best])
print('Guess @ 1 %s, prob = %.2f' % best_choice)
nlabels = len(label_list)if nlabels > 2:
output[best] = 0
second_best = np.argmax(output)
secondAgeClass = label_list[second_best] # (25, 32)
secondAgeArr = secondAgeClass.replace(" ", "").replace("(", "").replace(")", "").split(",")if int(secondAgeArr[1])== 53:
avgSecond = 56elif int(secondAgeArr[1])== 43:
avgSecond = 45elif int(secondAgeArr[1])== 20:
avgSecond = 22.66elif int(secondAgeArr[1])== 6:
avgSecond = 6.66else:
avgSecond = (int(secondAgeArr[0]) + int(secondAgeArr[1])) / 1.66
secondProb = output[second_best]
print('Guess @ 2 %s, prob = %.2f' % (label_list[second_best], output[second_best]))except Exception as e:import traceback
traceback.print_exc()
print('Failed to run image %s ' % image_file)if avgSecond > 0:#基于加权平均法计算最终的合适的年龄
finalAge = (avgBest * bestProb + avgSecond * secondProb) / (bestProb + secondProb)else:
finalAge = avgBest
print("finalAge %s " % finalAge)
end = time.time()
times = str(end - start)#返回年龄等json格式数据
result = {"finalAge": finalAge,"times": times}
out = json.dumps(result, ensure_ascii=False)
print("out={0}".format(out))return outif __name__ == '__main__':#指定ip地址和端口号
app.run(host='172.17.100.216', port=8818)