@人脸识别代码和一些常见错误

基于opencv的视频人脸识别（中文显示）以及 index 480 is out of bounds for axis 0 with size 480错误的解决

参考了
https://github.com/niehen6174/face-recognition-and-put-in-chinese
上述代码中采用的是海康的摄像头，这里我用的是自己笔记本的摄像头。
直接使用会显示数组溢出，这里会有一些修改的部分
报错 index 480 is out of bounds for axis 0 with size 480的解决见代码后面的修改部分
直接运行下述代码应该不会报错

ft2（处理中文识别）

# -*- coding: utf-8 -*-
# http://blog.csdn.net/zizi7/article/details/70145150'''
##################################################
# tools                                          #
#------------------------------------------------#
# draw chinese text using freetype on python2.x  #                  #
# 2017.4.12                                      #
##################################################
'''import numpy as np
import freetype
import copy
import pdbclass put_chinese_text(object):def __init__(self, ttf):self._face = freetype.Face(ttf)def draw_text(self, image, x, y,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.sizeascender = metrics.ascender/64.0#descender = metrics.descender/64.0#height = metrics.height/64.0#linegap = height - ascender + descenderypos = 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,x,y+ypos, text, text_color)return imgdef 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 = 0pen = freetype.Vector()pen.x = x_pos << 6   # div 64pen.y = y_pos << 6hscale = 1.0matrix = 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.xslot = self._face.glyphbitmap = slot.bitmapcur_pen.x = pen.xcur_pen.y = pen.y - slot.bitmap_top * 64self.draw_ft_bitmap(image, bitmap, cur_pen, color)pen.x += slot.advance.xprev_char = cur_charreturn imagedef 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 >> 6y_pos = pen.y >> 6cols = bitmap.widthrows = bitmap.rowsglyph_pixels = bitmap.bufferfor 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 testimport cv2line = '你好'img = np.zeros([300,300,3])color_ = (0,255,0) # Greenpos = (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)

修改的部分是第44行的，就是这里的数组出现了问题。

img = self.draw_string(image,x,y+ypos, text, text_color)

face_mobilevideo实现人脸识别，按下S键可拍照并保存。

# -*- coding: utf-8 -*-
# 摄像头头像识别
import face_recognition
import cv2
import ft2
from PIL import Image, ImageDraw, ImageFont
import numpy as npcam = cv2.VideoCapture(0)
# 本地图像
kxs_image = face_recognition.load_image_file("kxs.jpg")
kxs_face_encoding = face_recognition.face_encodings(kxs_image)[0]# 本地图像二
wsp_image = face_recognition.load_image_file("wsp.jpg")
wsp_face_encoding = face_recognition.face_encodings(wsp_image)[0]# 本地图片三2
pr_image = face_recognition.load_image_file("pr.jpg")
pr_face_encoding = face_recognition.face_encodings(pr_image)[0]# Create arrays of known face encodings and their names
# 脸部特征数据的集合
known_face_encodings = [kxs_face_encoding,wsp_face_encoding,pr_face_encoding
]# 人物名称的集合
"""""
known_face_names = ["kxs","wsp","panrui"
]
"""""
known_face_names = ["匡","王","潘"
]
face_locations = []
face_encodings = []
face_names = []
process_this_frame = Truewhile(cam.isOpened()):# 读取摄像头画面ret, frame = cam.read()if not ret:#等同于 if ret is not nonebreaksucess, img = cam.read()k = cv2.waitKey(1)if k == 27:# 通过esc键退出摄像cv2.destroyAllWindows()breakelif k == ord("s"):# 通过s键保存图片，并退出。num = 1cv2.imwrite("image%s.jpg" % num, img)num+=1;print("ok")#cv2.destroyAllWindows()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 timeif process_this_frame:# 根据encoding来判断是不是同一个人，是就输出true，不是为flaseface_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unknownmatches = 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 *= 3right *= 3bottom *= 3left *= 3cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 3)print(name)ft = ft2.put_chinese_text('msyh.ttf')#引入ft2中的字体#加上标签xpos=left+10ypos=bottomframe = ft.draw_text(frame,xpos,ypos,name, 20, (255, 255, 255))cv2.imshow('monitor', frame)if cv2.waitKey(1) & 0xFF == 27:breakcam.release()
cv2.destroyAllWindows()

修改的部分是120行左右的

     xpos=left+10ypos=bottomframe = ft.draw_text(frame,xpos,ypos,name, 20, (255, 255, 255))

只输出英文（把上面的输出文字改为下面的代码即可）

cv2.putText(frame, name, (left + 6, bottom - 6), 000, 0.8, (255, 255, 255), 1)  # 这是不输入汉字时可以用的代码

报错

** index 480 is out of bounds for axis 0 with size 480**
就按我上面的修改就不会出错了。

代码文件下载

https://github.com/horsein/-python_face_recognition–/tree/main

调用笔记本的摄像头实现基于opencv的视频人脸识别（中文显示和英文显示）以及 index 480 is out of bounds for axis 0 with size 480错误的解决相关推荐

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调用笔记本的摄像头实现基于opencv的视频人脸识别（中文显示和英文显示）以及 index 480 is out of bounds for axis 0 with size 480错误的解决