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predict_cam.py
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predict_cam.py
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# %% coding=utf-8
import os
import dlib # 机器学习的库 Dlib
import cv2 # 图像处理的库 OpenCv
import time
import timeit
import requests
import statistics
import webbrowser
import pandas as pd
from sklearn.externals import joblib
"""
调用摄像头，进行人脸捕获，和 68 个特征点的追踪
Author: coneypo
Blog: http://www.cnblogs.com/AdaminXie
GitHub: https://github.com/coneypo/Dlib_face_detection_from_camera
Created at 2018-02-26
Updated at 2019-01-28
todo 需要添加人脸对齐
"""
# 储存截图的目录
path_screenshots = "img/screenshots/"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('model/shape_predictor_68_face_landmarks.dat')
model = joblib.load('model/beauty.pkl')
# 创建 cv2 摄像头对象
cap = cv2.VideoCapture(0)
# cap.set(propId, value)
# 设置视频参数，propId 设置的视频参数，value 设置的参数值
cap.set(3, 480)
# 截图 screenshots 的计数器
cnt = 0
best_score = 0
best_img = None
time_cost_list = []
def prepare_input(img, face):
f_width = abs(face.right() - face.left())
f_height = abs(face.bottom() - face.top())
shape = predictor(img, face)
# print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1)))
face_shape = {}
for i in range(0, 67):
for j in range(i + 1, 68):
face_shape[str(i) + '_' + str(j) + '_x'] = abs(shape.part(i).x - shape.part(j).x) / f_width
face_shape[str(i) + '_' + str(j) + '_y'] = abs(shape.part(i).y - shape.part(j).y) / f_height
# print(str(i) + '_' + str(j))
# shape_size.append(face_shape)
df_image = pd.DataFrame.from_dict([face_shape])
return df_image
def gen_upload(img_path):
cap.release()
with open(img_path, 'rb') as f1:
files = [
('sc', f1)
]
# flask 服务地址
resp = requests.post('http://172.16.254.164:5000/upload_gen', files=files)
path = os.path.abspath('data/reports/temp.htm')
url = 'file://' + path
with open(path, 'w') as f:
f.write(str(resp.content))
webbrowser.open(url)
# cap.isOpened() 返回 true/false 检查初始化是否成功
while cap.isOpened():
# cap.read()
# 返回两个值：
# 一个布尔值 true/false，用来判断读取视频是否成功/是否到视频末尾
# 图像对象，图像的三维矩阵
flag, im_rd = cap.read()
# 每帧数据延时 1ms，延时为 0 读取的是静态帧
k = cv2.waitKey(1)
# 取灰度
img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)
# start point
start = timeit.default_timer()
# 人脸数
face = detector(img_gray, 1)
# print(len(faces))
# 待会要写的字体
font = cv2.FONT_HERSHEY_SIMPLEX
# 标 68 个点
pred = 0
if len(face) != 0:
# 检测到人脸
#for i in range(len(faces)):
#landmarks = np.matrix([[p.x, p.y] for p in predictor(im_rd, faces[i]).parts()])
#for p in predictor(im_rd, faces[i]).parts():
# for idx, point in enumerate(landmarks):
# # 68 点的坐标
# pos = (point[0, 0], point[0, 1])
#
# # 利用 cv2.circle 给每个特征点画一个圈，共 68 个
# cv2.circle(im_rd, pos, 2, color=(139, 0, 0))
#
# # 利用 cv2.putText 输出 1-68
# cv2.putText(im_rd, str(idx + 1), pos, font, 0.2, (187, 255, 255), 1, cv2.LINE_AA)
for i, d in enumerate(face):
df_image = prepare_input(im_rd, d)
pred = model.predict(df_image)
if pred > best_score:
best_score = pred
if best_img is not None:
os.remove(best_img)
best_img = path_screenshots + "screenshot" + "_" + str(cnt) + "_" + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + ".jpg"
cv2.imwrite(best_img, im_rd)
#cv2.putText(im_rd, "face score: " + str(pred), (20, 50), font, 1, (0, 0, 0), 1, cv2.LINE_AA)
#cv2.putText(im_rd, "faces: " + str(len(faces)), (20, 50), font, 1, (0, 0, 0), 1, cv2.LINE_AA)
# end point
stop = timeit.default_timer()
time_cost_list.append(stop - start)
#print("%-15s %f" % ("Time cost:", (stop - start)))
else:
# 没有检测到人脸
cv2.putText(im_rd, "no face", (20, 50), font, 1, (0, 0, 0), 1, cv2.LINE_AA)
# 添加说明
im_rd = cv2.putText(im_rd, "best score : " + str(best_score), (20, 350), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
im_rd = cv2.putText(im_rd, "press 'G': gen report", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
im_rd = cv2.putText(im_rd, "press 'Q': quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
# 按下 'g' 键生成报告
if k == ord('g'):
cnt += 1
print(best_img)
im_rd = cv2.imread(best_img)
im_rd = cv2.putText(im_rd, "generating report...", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
gen_upload(best_img)
# 按下 'q' 键退出
if k == ord('q'):
break
# 窗口显示
# 参数取 0 可以拖动缩放窗口，为 1 不可以
# cv2.namedWindow("camera", 0)
cv2.namedWindow("camera", 1)
cv2.imshow("camera", im_rd)
# 释放摄像头
cap.release()
# 删除建立的窗口
cv2.destroyAllWindows()
print("%-15s" % "Result:")
print("%-15s %f" % ("Max time:", (max(time_cost_list))))
print("%-15s %f" % ("Min time:", (min(time_cost_list))))
print("%-15s %f" % ("Average time:", statistics.mean(time_cost_list)))