opencv的puttxt（）函数不能汉字输出，这也是困惑好多人都问题，经过几天的查资料，改代码终于成功实现opencv汉字输出。
是通过写一段代码，能够转码，封装一下再调用，从而实现汉字输出。
以下这个是ft2.py 实现转码的代码
# -*- coding: utf-8 -*-
# http://blog.csdn.net/zizi7/article/details/70145150
import numpy as np
import freetype
import copy
import pdb
class put_chinese_text(object):
def __init__(self, ttf):
self._face = freetype.Face(ttf)
def draw_text(self, image, pos, text, text_size, text_color):
'''
draw chinese(or not) text with ttf
:param image: image(numpy.ndarray) to draw text
:param pos: where to draw text
:param text: the context, for chinese should be unicode type
:param text_size: text size
:param text_color:text color
:return: image
'''
self._face.set_char_size(text_size * 64)
metrics = self._face.size
ascender = metrics.ascender/64.0
#descender = metrics.descender/64.0
#height = metrics.height/64.0
#linegap = height - ascender + descender
ypos = int(ascender)
if not isinstance(text, str):
#对于Python 2中的unicode和Python 3中的str，对于Python 2中的str/bytes和Python 3中的bytes的二进制文件
text = text.decode('utf-8')
img = self.draw_string(image, pos[0], pos[1]+ypos, text, text_color)
return img
def draw_string(self, img, x_pos, y_pos, text, color):
'''
draw string
:param x_pos: text x-postion on img
:param y_pos: text y-postion on img
:param text: text (unicode)
:param color: text color
:return: image
'''
prev_char = 0
pen = freetype.Vector()
pen.x = x_pos << 6 # div 64
pen.y = y_pos << 6
hscale = 1.0
matrix = freetype.Matrix(int((hscale)*0x10000), int(0.2*0x10000),\
int(0.0*0x10000), int(1.1*0x10000))
cur_pen = freetype.Vector()
pen_translate = freetype.Vector()
image = copy.deepcopy(img)
for cur_char in text:
self._face.set_transform(matrix, pen_translate)
self._face.load_char(cur_char)
kerning = self._face.get_kerning(prev_char, cur_char)
pen.x += kerning.x
slot = self._face.glyph
bitmap = slot.bitmap
cur_pen.x = pen.x
cur_pen.y = pen.y - slot.bitmap_top * 64
self.draw_ft_bitmap(image, bitmap, cur_pen, color)
pen.x += slot.advance.x
prev_char = cur_char
return image
def draw_ft_bitmap(self, img, bitmap, pen, color):
'''
draw each char
:param bitmap: bitmap
:param pen: pen
:param color: pen color e.g.(0,0,255) - red
:return: image
'''
x_pos = pen.x >> 6
y_pos = pen.y >> 6
cols = bitmap.width
rows = bitmap.rows
glyph_pixels = bitmap.buffer
for row in range(rows):
for col in range(cols):
if glyph_pixels[row*cols + col] != 0:
img[y_pos + row][x_pos + col][0] = color[0]
img[y_pos + row][x_pos + col][1] = color[1]
img[y_pos + row][x_pos + col][2] = color[2]
if __name__ == '__main__':
# just for test
import cv2
line = '你好'
img = np.zeros([300,300,3])
color_ = (0,255,0) # Green
pos = (3, 3)
text_size = 24
#ft = put_chinese_text('wqy-zenhei.ttc')
ft = put_chinese_text('msyh.ttf')
image = ft.draw_text(img, pos, line, text_size, color_)
cv2.imshow('diplay', image)
cv2.waitKey(0)里面包含了几个函数，其中需要一个字体 msyh.ttf在同一文件夹下
以下是实现人脸识别的代码
# -*- coding: utf-8 -*-
# 摄像头头像识别
import face_recognition
import cv2
import ft2
source = "rtsp://admin:[email protected]/Streaming/Channels/1"
cam = cv2.VideoCapture(source)
# 本地图像
zwh_image = face_recognition.load_image_file("zwh.jpg")
zwh_face_encoding = face_recognition.face_encodings(zwh_image)[0]
# 本地图像二
chenduling_image = face_recognition.load_image_file("chenduling.jpg")
chenduling_face_encoding = face_recognition.face_encodings(chenduling_image)[0]
# 本地图片三
liujunbo_image = face_recognition.load_image_file("liujunbo.jpg")
liujunbo_face_encoding = face_recognition.face_encodings(liujunbo_image)[0]
# Create arrays of known face encodings and their names
# 脸部特征数据的集合
known_face_encodings = [
zwh_face_encoding,
chenduling_face_encoding,
liujunbo_face_encoding
]
# 人物名称的集合
known_face_names = [
"张文豪",
"陈都灵",
"刘军波"
]
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
while(cam.isOpened()):
# 读取摄像头画面
ret, frame = cam.read()
if not ret:
#等同于 if ret is not none
break
# 改变摄像头图像的大小，图像小，所做的计算就少
small_frame = cv2.resize(frame, (0, 0), fx=0.33, fy=0.33)
# opencv的图像是BGR格式的，而我们需要是的RGB格式的，因此需要进行一个转换。
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# 根据encoding来判断是不是同一个人，是就输出true，不是为flase
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# 默认为unknown
matches = face_recognition.compare_faces(known_face_encodings, face_encoding,tolerance=0.48)
#阈值太低容易造成无法成功识别人脸，太高容易造成人脸识别混淆 默认阈值tolerance为0.6
#print(matches)
name = "Unknown"
# if match[0]:
# name = "michong"
# If a match was found in known_face_encodings, just use the first one.
if True in matches:
first_match_index = matches.index(True)
name = known_face_names[first_match_index]
face_names.append(name)
process_this_frame = not process_this_frame
# 将捕捉到的人脸显示出来
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
#由于我们检测到的帧被缩放到1/4大小，所以要缩小面位置
top *= 3
right *= 3
bottom *= 3
left *= 3
# 矩形框
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 3)
ft = ft2.put_chinese_text('msyh.ttf')
#引入ft2中的字体
#加上标签
#cv2.rectangle(frame, (left, bottom - 20), (right, bottom), (0, 0, 255), cv2.FILLED)
# cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.8, (255, 255, 255), 1)这是不输入汉字时可以用的代码
frame = ft.draw_text(frame, (left+10 , bottom ), name, 20, (255, 255, 255))
#cv2.imshow("frame",image)会出来两个框一个monitor 一个frame后者显示image但只有frame为true时才会显示
#def draw_text(self, image, pos, text, text_size, text_color)
cv2.imshow('monitor', frame)
if cv2.waitKey(1) & 0xFF == 27:
break
cam.release()
cv2.destroyAllWindows()
最基本的要装上python和opencv，距离远不能看出人脸的解决方法 ：可以通过调整获取每帧图像的大小可以进行改造，代价就是会变卡，延迟高，这就需要通过调整摄像头的参数进行修改分辨率视频帧率 码率上限 视频编码来进行降低分辨率。
人脸混淆较近看不出人脸的解决方法：修改阈值 阈值太低容易造成无法成功识别人脸，太高容易造成人脸识别混淆 默认阈值tolerance为0.6 亚洲人一般要用低点
https://github.com/niehen6174/face-recognition-and-put-in-chinese/tree/master/code
上面链接有完整代码 字体和一个照片文件