这是我用于检测和展示性别和年龄的循环。我尝试在matplotlib饼图上绘制它，但每次运行循环时，它都会为每个预测打开一个不同的图表。如何实时更新同一张图表。
这里我正在更新完整的代码
使用Python和OpenCV检测人物年龄和性别的完整代码。
```
这里我正在更新完整的代码
使用Python和OpenCV检测人物年龄和性别的完整代码。
```
import os
import cv2
import dlib
import numpy as np
import argparse
import inception_resnet_v1
import tensorflow as tf
from imutils.face_utils import FaceAligner
from imutils.face_utils import rect_to_bb
import matplotlib.pyplot as plt
def get_args():
parser = argparse.ArgumentParser(description="This script detects faces from web cam input, "
"and estimates age and gender for the detected faces.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--weight_file", type=str, default=None,
help="path to weight file (e.g. weights.18-4.06.hdf5)")
parser.add_argument("--depth", type=int, default=16,
help="depth of network")
parser.add_argument("--width", type=int, default=8,
help="width of network")
args = parser.parse_args()
return args
def draw_label(image, point, label, font=cv2.FONT_HERSHEY_SIMPLEX,
font_scale=1, thickness=2):
size = cv2.getTextSize(label, font, font_scale, thickness)[0]
x, y = point
cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)
cv2.putText(image, label, point, font, font_scale, (255, 255, 255), thickness)
def main(sess,age,gender,train_mode,images_pl):
args = get_args()
depth = args.depth
k = args.width
# for face detection
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
fa = FaceAligner(predictor, desiredFaceWidth=160)
# load model and weights
img_size = 160
# cap video
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
while True:
# get video frame
ret, img = cap.read()
if not ret:
print("error: failed to capture image")
return -1
input_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_h, img_w, _ = np.shape(input_img)
detected = detector(input_img, 1)
faces = np.empty((len(detected), img_size, img_size, 3))
for i, d in enumerate(detected):
x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()
xw1 = max(int(x1 - 0.4 * w), 0)
yw1 = max(int(y1 - 0.4 * h), 0)
xw2 = min(int(x2 + 0.4 * w), img_w - 1)
yw2 = min(int(y2 + 0.4 * h), img_h - 1)
cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
# cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)
faces[i, :, :, :] = fa.align(input_img, gray, detected[i])
# faces[i,:,:,:] = cv2.resize(img[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))
if len(detected) > 0:
# predict ages and genders of the detected faces
ages,genders = sess.run([age, gender], feed_dict={images_pl: faces, train_mode: False})
# draw results
for i, d in enumerate(detected):
label = "{}, {}".format(int(ages[i]), "F" if genders[i] == 0 else "M")
draw_label(img, (d.left(), d.top()), label)
#for i in range(20):
#label = 'M', 'F'
sizes = [15, 30]
explode = (0, 0.1)
fig1, ax1 = plt.subplots()
ax1.pie(sizes, explode=explode, labels=label, autopct='%1.1f%%',
shadow=True, startangle=90)
ax1.axis('equal') # Equal aspect ratio
plt.show()
#break
cv2.imshow("result", img)
key = cv2.waitKey(1)
if key == 27:
break
def load_network(model_path):
sess = tf.Session()
images_pl = tf.placeholder(tf.float32, shape=[None, 160, 160, 3], name='input_image')
images_norm = tf.map_fn(lambda frame: tf.image.per_image_standardization(frame), images_pl)
train_mode = tf.placeholder(tf.bool)
age_logits, gender_logits, _ = inception_resnet_v1.inference(images_norm, keep_probability=0.8,
phase_train=train_mode,
weight_decay=1e-5)
gender = tf.argmax(tf.nn.softmax(gender_logits), 1)
age_ = tf.cast(tf.constant([i for i in range(0, 101)]), tf.float32)
age = tf.reduce_sum(tf.multiply(tf.nn.softmax(age_logits), age_), axis=1)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess.run(init_op)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(model_path)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("restore model!")
else:
pass
return sess,age,gender,train_mode,images_pl
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", "--M", default="./models", type=str, help="Model Path")
args = parser.parse_args()
sess, age, gender, train_mode,images_pl = load_network(args.model_path)
main(sess,age,gender,train_mode,images_pl)
```