杭州网站建设公司哪家好,黄石企业网站设计,视频素材库网站免费,免费建设网站文章目录 Haar人脸识别方法Haar识别眼鼻口HaarTesseract进行车牌识别深度学习基础知识dnn实现图像分类 Haar人脸识别方法 scaleFactor调整哈尔级联器的人脸选框使其能框住人脸 官方教程指路 每个特征都是通过从黑色矩形下的像素总和减去白色矩形下的像素总和获得的单个值 级… 文章目录 Haar人脸识别方法Haar识别眼鼻口HaarTesseract进行车牌识别深度学习基础知识dnn实现图像分类 Haar人脸识别方法 scaleFactor调整哈尔级联器的人脸选框使其能框住人脸 官方教程指路 每个特征都是通过从黑色矩形下的像素总和减去白色矩形下的像素总和获得的单个值 级联器模型文件位置
# -*- coding: utf-8 -*-
import cv2
import numpy as npcv2.namedWindow(img, cv2.WINDOW_NORMAL)
#第一步创建Haar级联器
facer cv2.CascadeClassifier(./haarcascade_frontalface_default.xml)#第二步导入人脸识别的图片并将其灰度化
img cv2.imread(E:/pic/Pic/11.jpg)
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#第三步进行人脸识别
#[[x,y,w,h]]
faces facer.detectMultiScale(gray, 1.1, 5)for (x, y, w, h) in faces:cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 0), 3)cv2.imshow(img, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows() Haar识别眼鼻口
# -*- coding: utf-8 -*-
import cv2
import numpy as npcv2.namedWindow(img, cv2.WINDOW_NORMAL)
#第一步创建Haar级联器
facer cv2.CascadeClassifier(./haarcascade_frontalface_default.xml)
eye cv2.CascadeClassifier(./haarcascade_eye.xml)
mouse cv2.CascadeClassifier(./haarcascade_mcs_mouth.xml)#第二步导入人脸识别的图片并将其灰度化
img cv2.imread(E:/pic/Pic/11.jpg)
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#第三步进行人脸识别
#[[x,y,w,h]]
faces facer.detectMultiScale(gray, 1.1, 5)
eyes eye.detectMultiScale(gray, 1.1, 5)
mouses mouse.detectMultiScale(gray, 1.1, 5)for (x, y, w, h) in faces:cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 0), 3)for (x, y, w, h) in eyes:cv2.rectangle(img, (x, y), (x w, y h), (255, 255, 0), 3)# for (x, y, w, h) in mouses:
# cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 255), 3)cv2.imshow(img, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows() 识别嘴就会不精确了
# -*- coding: utf-8 -*-
import cv2
import numpy as npcv2.namedWindow(img, cv2.WINDOW_NORMAL)
#第一步创建Haar级联器
facer cv2.CascadeClassifier(./haarcascade_frontalface_default.xml)
eye cv2.CascadeClassifier(./haarcascade_eye.xml)
mouse cv2.CascadeClassifier(./haarcascade_mcs_mouth.xml)#第二步导入人脸识别的图片并将其灰度化
img cv2.imread(E:/pic/Pic/11.jpg)
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#第三步进行人脸识别
#[[x,y,w,h]]
faces facer.detectMultiScale(gray, 1.1, 5)
eyes eye.detectMultiScale(gray, 1.1, 5)
mouses mouse.detectMultiScale(gray, 1.1, 5)for (x, y, w, h) in faces:cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 0), 3)for (x, y, w, h) in eyes:cv2.rectangle(img, (x, y), (x w, y h), (255, 255, 0), 3)for (x, y, w, h) in mouses:cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 255), 3)cv2.imshow(img, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows() 识别鼻子
# -*- coding: utf-8 -*-
import cv2
import numpy as npcv2.namedWindow(img, cv2.WINDOW_NORMAL)
#第一步创建Haar级联器
facer cv2.CascadeClassifier(./haarcascade_frontalface_default.xml)
eye cv2.CascadeClassifier(./haarcascade_eye.xml)
mouse cv2.CascadeClassifier(./haarcascade_mcs_mouth.xml)
nose cv2.CascadeClassifier(./haarcascade_mcs_nose.xml)#第二步导入人脸识别的图片并将其灰度化
img cv2.imread(E:/pic/Pic/11.jpg)
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#第三步进行人脸识别
#[[x,y,w,h]]
faces facer.detectMultiScale(gray, 1.1, 5)
eyes eye.detectMultiScale(gray, 1.1, 5)
mouses mouse.detectMultiScale(gray, 1.1, 5)
noses nose.detectMultiScale(gray, 1.1, 5)for (x, y, w, h) in faces:cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 0), 3)for (x, y, w, h) in eyes:cv2.rectangle(img, (x, y), (x w, y h), (255, 255, 0), 3)for (x, y, w, h) in mouses:cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 255), 3)for (x, y, w, h) in noses:cv2.rectangle(img, (x, y), (x w, y h), (0, 0, 255), 3)cv2.imshow(img, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows() 只要不测口还是比较准确的
# -*- coding: utf-8 -*-
import cv2
import numpy as npcv2.namedWindow(img, cv2.WINDOW_NORMAL)
#第一步创建Haar级联器
facer cv2.CascadeClassifier(./haarcascade_frontalface_default.xml)
eye cv2.CascadeClassifier(./haarcascade_eye.xml)
mouse cv2.CascadeClassifier(./haarcascade_mcs_mouth.xml)
nose cv2.CascadeClassifier(./haarcascade_mcs_nose.xml)#第二步导入人脸识别的图片并将其灰度化
img cv2.imread(E:/pic/Pic/11.jpg)
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#第三步进行人脸识别
#[[x,y,w,h]]
faces facer.detectMultiScale(gray, 1.1, 5)
# eyes eye.detectMultiScale(gray, 1.1, 5)
# mouses mouse.detectMultiScale(gray, 1.1, 5)
# noses nose.detectMultiScale(gray, 1.1, 5)i 0for (x, y, w, h) in faces:cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 0), 3)roi_img img[y: yh, x:xw]eyes eye.detectMultiScale(roi_img, 1.1, 5)for (x, y, w, h) in eyes:cv2.rectangle(roi_img, (x, y), (x w, y h), (255, 255, 0), 3)noses nose.detectMultiScale(roi_img, 1.1, 5)for (x, y, w, h) in noses:cv2.rectangle(roi_img, (x, y), (x w, y h), (0, 0, 255), 3)# mouses mouse.detectMultiScale(roi_img, 1.1, 5)# for (x, y, w, h) in mouses:# cv2.rectangle(roi_img, (x, y), (x w, y h), (0, 255, 255), 3)# i 1# winname face str(i)# cv2.imshow(winname, roi_img)# for (x, y, w, h) in mouses:
# cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 255), 3)# for (x, y, w, h) in noses:
# cv2.rectangle(img, (x, y), (x w, y h), (0, 0, 255), 3)cv2.imshow(img, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows() 测口准确度太低
# -*- coding: utf-8 -*-
import cv2
import numpy as npcv2.namedWindow(img, cv2.WINDOW_NORMAL)
#第一步创建Haar级联器
facer cv2.CascadeClassifier(./haarcascade_frontalface_default.xml)
eye cv2.CascadeClassifier(./haarcascade_eye.xml)
mouse cv2.CascadeClassifier(./haarcascade_mcs_mouth.xml)
nose cv2.CascadeClassifier(./haarcascade_mcs_nose.xml)#第二步导入人脸识别的图片并将其灰度化
img cv2.imread(E:/pic/Pic/11.jpg)
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#第三步进行人脸识别
#[[x,y,w,h]]
faces facer.detectMultiScale(gray, 1.1, 5)
# eyes eye.detectMultiScale(gray, 1.1, 5)
# mouses mouse.detectMultiScale(gray, 1.1, 5)
# noses nose.detectMultiScale(gray, 1.1, 5)i 0for (x, y, w, h) in faces:cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 0), 3)roi_img img[y: yh, x:xw]eyes eye.detectMultiScale(roi_img, 1.1, 5)for (x, y, w, h) in eyes:cv2.rectangle(roi_img, (x, y), (x w, y h), (255, 255, 0), 3)noses nose.detectMultiScale(roi_img, 1.1, 5)for (x, y, w, h) in noses:cv2.rectangle(roi_img, (x, y), (x w, y h), (0, 0, 255), 3)mouses mouse.detectMultiScale(roi_img, 1.1, 5)for (x, y, w, h) in mouses:cv2.rectangle(roi_img, (x, y), (x w, y h), (0, 255, 255), 3)# i 1# winname face str(i)# cv2.imshow(winname, roi_img)# for (x, y, w, h) in mouses:
# cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 255), 3)# for (x, y, w, h) in noses:
# cv2.rectangle(img, (x, y), (x w, y h), (0, 0, 255), 3)cv2.imshow(img, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows() HaarTesseract进行车牌识别 安装很简单这里贴一个安装教程
配置出现问题的可以看看这篇博客
测试一下识别文字还是很准的
# -*- coding: utf-8 -*-
import cv2
import numpy as np# cv2.namedWindow(img, cv2.WINDOW_NORMAL)
#第一步创建Haar级联器
carplate cv2.CascadeClassifier(./haarcascade_russian_plate_number.xml)#第二步导入带车牌的图片并将其灰度化
img cv2.imread(./chinacar.jpeg)
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#第三步进行车牌定位
#[[x,y,w,h]]
carplates carplate.detectMultiScale(gray, 1.1, 5)for (x, y, w, h) in carplates:cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 0), 3)cv2.imshow(img, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()# -*- coding: utf-8 -*-
import cv2
import numpy as np# cv2.namedWindow(img, cv2.WINDOW_NORMAL)
#第一步创建Haar级联器
carplate cv2.CascadeClassifier(./haarcascade_russian_plate_number.xml)#第二步导入带车牌的图片并将其灰度化
img cv2.imread(./chinacar.jpeg)
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#第三步进行车牌定位
#[[x,y,w,h]]
carplates carplate.detectMultiScale(gray, 1.1, 5)for (x, y, w, h) in carplates:cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 0), 3)# 对获取到的车牌进行预处理
# 1.提取ROI
roi gray[y: yh, x:xw]
# 2.进行二值化
ret, roi_bin cv2.threshold(roi, 10, 255, cv2.THRESH_BINARY cv2.THRESH_OTSU)cv2.imshow(img, img)
cv2.imshow(roi_bin, roi_bin)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()# -*- coding: utf-8 -*-
import cv2
import numpy as np# 引入tesseract库
import pytesseract# cv2.namedWindow(img, cv2.WINDOW_NORMAL)
#第一步创建Haar级联器
carplate cv2.CascadeClassifier(./haarcascade_russian_plate_number.xml)#第二步导入带车牌的图片并将其灰度化
img cv2.imread(./chinacar.jpeg)
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#第三步进行车牌定位
#[[x,y,w,h]]
carplates carplate.detectMultiScale(gray, 1.1, 5)for (x, y, w, h) in carplates:cv2.rectangle(img, (x, y), (x w, y h), (0, 255, 0), 3)# 对获取到的车牌进行预处理
# 1.提取ROI
roi gray[y: yh, x:xw]
# 2.进行二值化
ret, roi_bin cv2.threshold(roi, 10, 255, cv2.THRESH_BINARY cv2.THRESH_OTSU)pytesseract.pytesseract.tesseract_cmd rD:\Program Files\Tesseract_OCR\tesseract.exe
print(pytesseract.image_to_string(roi, langchi_simeng, config--psm 8 --oem 3))cv2.imshow(img, img)
cv2.imshow(roi_bin, roi_bin)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()具体实现还需要进一步优化
深度学习基础知识
深度学习是计算机视觉最为重要的方法
dnn实现图像分类 # -*- coding: utf-8 -*-
import cv2
from cv2 import dnn
import numpy as np# 1.导入模型创建神经网络
# 2.读取图片转成张量
# 3.将张量输入到网络中并进行预测
# 4.得到结果显示# 导入模型创建神经网络
config ./bvlc_googlenet.prototxt
model ./bvlc_googlenet.caffemodel
net dnn.readNetFromCaffe(config, model)# 读取图片转成张量
img cv2.imread(./smallcat.jpeg)
blob dnn.blobFromImage(img, 1.0, (224, 224), (104, 117, 123))# 将张量输入到网络中并进行预测
net.setInput(blob)
r net.forward()# 读取类目
classes []
path ./synset_words.txt
with open(path, rt) as f:classes [x [x.find( ) 1:] for x in f]order sorted(r[0], reverseTrue)
z list(range(3))for i in list(range(0, 3)):z[i] np.where(r[0] order[i])[0][0]print(No., i 1, matches:, classes[z[i]], end)print(category row is at:, z[i] 1, , posibility:, order[i])之后我会持续更新如果喜欢我的文章请记得一键三连哦点赞关注收藏你的每一个赞每一份关注每一次收藏都将是我前进路上的无限动力 ↖(▔▽▔)↗感谢支持
