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Repository: HuangCongQing/OpenCV
Branch: master
Commit: 5713577de082
Files: 21
Total size: 2.1 MB
Directory structure:
gitextract_u125kwrs/
├── .gitignore
├── 00.py
├── 01read_show.py
├── 01项目实战-信用卡数字识别/
│ └── template-matching-ocr/
│ ├── myutils.py
│ └── ocr_template_match.py
├── 02save.py
├── 02项目实战-文档扫描OCR识别/
│ └── Scan/
│ ├── .idea/
│ │ ├── document-scanner.iml
│ │ ├── misc.xml
│ │ ├── modules.xml
│ │ └── workspace.xml
│ ├── scan.py
│ └── test.py
├── LICENSE
├── README.md
├── dataset/
│ └── 2023-02-07-22_46_36.txt
├── practice/
│ └── vis/
│ ├── 01show_bbox.ipynb
│ ├── 02show_line.ipynb
│ └── 03show_text.ipynb
└── 基于python的Opencv项目实战/
├── 图像处理/
│ └── 图像处理-2(直方图&模板匹配).ipynb
└── 图像操作/
├── 图像基本操作.ipynb
└── 图像处理.ipynb
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FILE CONTENTS
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================================================
FILE: .gitignore
================================================
代码+资料
================================================
FILE: 00.py
================================================
import cv2
print(cv2.__version__)
================================================
FILE: 01read_show.py
================================================
# encoding:utf-8
import cv2
img = cv2.imread("./cat.jpg")
cv2.namedWindow("Image显示")
cv2.imshow("Image显示", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# https: // www.cnblogs.com/zangyu/p/5802142.html
================================================
FILE: 01项目实战-信用卡数字识别/template-matching-ocr/myutils.py
================================================
'''
Description: OCR-模板匹配 参考：https://www.bilibili.com/video/BV1oJ411D71z?t=11&p=9
Author: HCQ
Company(School): UCAS
Email: 1756260160@qq.com
Date: 2021-01-23 11:12:41
LastEditTime: 2021-01-25 12:28:10
FilePath: /OpenCV/01项目实战-信用卡数字识别/template-matching-ocr/myutils.py
'''
import cv2
# 轮廓排序
def sort_contours(cnts, method="left-to-right"):
reverse = False
i = 0
if method == "right-to-left" or method == "bottom-to-top":
reverse = True
if method == "top-to-bottom" or method == "bottom-to-top":
i = 1
boundingBoxes = [cv2.boundingRect(c) for c in cnts] #用一个最小的矩形(外接矩形)，把找到的形状包起来x,y,h,w 根据x横坐标就可以排序
(cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes), # sorted
key=lambda b: b[1][i], reverse=reverse)) # reverse
return cnts, boundingBoxes
# resize
def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
dim = None
(h, w) = image.shape[:2]
if width is None and height is None:
return image
if width is None:
r = height / float(h)
dim = (int(w * r), height)
else:
r = width / float(w)
dim = (width, int(h * r))
resized = cv2.resize(image, dim, interpolation=inter)
return resized
================================================
FILE: 01项目实战-信用卡数字识别/template-matching-ocr/ocr_template_match.py
================================================
'''
Description: OCR-模板匹配 参考：https://www.bilibili.com/video/BV1oJ411D71z?t=11&p=9
运行命令： python ocr_template_match.py -i xxx -t xxx
运行命令： python 01项目实战-信用卡数字识别/template-matching-ocr/ocr_template_match.py -i 01项目实战-信用卡数字识别/template-matching-ocr/images/credit_card_01.png -t 01项目实战-信用卡数字识别/template-matching-ocr/images/ocr_a_reference.png
Author: HCQ
Company(School): UCAS
Email: 1756260160@qq.com
Date: 2021-01-23 11:12:41
LastEditTime: 2021-01-25 14:07:18
FilePath: /OpenCV/01项目实战-信用卡数字识别/template-matching-ocr/ocr_template_match.py
'''
# 导入工具包
from imutils import contours
import numpy as np
import argparse
import cv2
import myutils
# 设置参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
help="path to input image")
ap.add_argument("-t", "--template", required=True,
help="path to template OCR-A image")
args = vars(ap.parse_args())
# 指定信用卡类型
FIRST_NUMBER = {
"3": "American Express",
"4": "Visa",
"5": "MasterCard",
"6": "Discover Card"
}
# 绘图展示
def cv_show(name,img):
cv2.imshow(name, img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# ==========================开始 1 处理模板图片========================================
# 读取一个模板图像
img = cv2.imread(args["template"]) # 参数 01项目实战-信用卡数字识别/template-matching-ocr/images/ocr_a_reference.png
cv_show('img',img)
# 灰度图
ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv_show('ref',ref)
# 二值图像
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
cv_show('ref',ref)
# 计算轮廓
#cv2.findContours()函数接受的参数为二值图，即黑白的（不是灰度图）,cv2.RETR_EXTERNAL只检测外轮廓，cv2.CHAIN_APPROX_SIMPLE只保留终点坐标
#返回的list中每个元素都是图像中的一个轮廓
# 高版本没有第一个参数ref_了，只会返回后两个
# ref_, refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) # 低版本
refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) # 高版本
cv2.drawContours(img,refCnts,-1,(0,0,255),3)
cv_show('img',img)
print (np.array(refCnts).shape) # 多少轮廓
refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0] #排序，从左到右，从上到下
digits = {}
# 遍历每一个轮廓
for (i, c) in enumerate(refCnts):
# 计算外接矩形并且resize成合适大小
(x, y, w, h) = cv2.boundingRect(c)
roi = ref[y:y + h, x:x + w] # x旋转区域
roi = cv2.resize(roi, (57, 88)) # resize
# 每一个数字对应每一个模板
digits[i] = roi
# =======================2 处理信用卡图片================================
# 初始化卷积核
rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))
sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) # 核大小 5x5
#读取输入图像，预处理
image = cv2.imread(args["image"]) # 参数
cv_show('image',image)
image = myutils.resize(image, width=300)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv_show('gray',gray)
#礼帽操作，突出更明亮的区域（字体区域）
tophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel) # rectKernel初始化核
cv_show('tophat',tophat)
#
gradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, #ksize=-1相当于用3*3的
ksize=-1)
gradX = np.absolute(gradX) # 只用x效果更好，随意没用xy梯度
(minVal, maxVal) = (np.min(gradX), np.max(gradX))
gradX = (255 * ((gradX - minVal) / (maxVal - minVal))) # 归一化
gradX = gradX.astype("uint8")
print (np.array(gradX).shape)
cv_show('gradX',gradX)
#通过闭操作（先膨胀，再腐蚀）将数字连在一起，形成四大块 背景也过滤了
gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)
cv_show('gradX',gradX)
#THRESH_OTSU会自动寻找合适的阈值，适合双峰，需把阈值参数设置为0
thresh = cv2.threshold(gradX, 0, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('thresh',thresh)
#再来一个闭操作
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来一个闭操作
cv_show('thresh',thresh)
# 计算轮廓
# 高版本没有第一个参数thresh_了，只会返回后两个
# thresh_, threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
# cv2.CHAIN_APPROX_SIMPLE)
threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = threshCnts # 最终轮廓
cur_img = image.copy()
cv2.drawContours(cur_img,cnts,-1,(0,0,255),3)
cv_show('img',cur_img)
locs = [] # 有价值的区域
# 遍历轮廓
for (i, c) in enumerate(cnts):
# 计算矩形
(x, y, w, h) = cv2.boundingRect(c)
ar = w / float(h) # 长宽比
# 选择合适的区域，根据实际任务来，这里的基本都是四个数字一组
if ar > 2.5 and ar < 4.0:
if (w > 40 and w < 55) and (h > 10 and h < 20):
#符合的留下来
locs.append((x, y, w, h))
# 将符合的轮廓从左到右排序
locs = sorted(locs, key=lambda x:x[0])
output = []
# 遍历每一个轮廓中的数字（四个大轮廓，每个轮廓里面有4个数字）
for (i, (gX, gY, gW, gH)) in enumerate(locs):
# initialize the list of group digits
groupOutput = []
# 根据坐标提取每一个组
group = gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5] # 往外扩大一点范围
cv_show('group',group)
# 预处理（二值化）
group = cv2.threshold(group, 0, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('group',group)
# 计算每一组的轮廓（4个数字，四个轮廓）
# 高版本没有第一个参数group_了，只会返回后两个
# group_,digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
# cv2.CHAIN_APPROX_SIMPLE)
digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
digitCnts = contours.sort_contours(digitCnts,
method="left-to-right")[0]
# 计算每一组中的每一个数值
for c in digitCnts:
# 找到当前数值的轮廓，resize成合适的的大小
(x, y, w, h) = cv2.boundingRect(c)
roi = group[y:y + h, x:x + w]
roi = cv2.resize(roi, (57, 88))
cv_show('roi',roi)
# 计算匹配得分
scores = []
# 在模板中计算每一个得分=============================================================================
for (digit, digitROI) in digits.items():
# 模板匹配（和模板中的10个数字匹配）
result = cv2.matchTemplate(roi, digitROI,
cv2.TM_CCOEFF)
(_, score, _, _) = cv2.minMaxLoc(result)
scores.append(score)
# 得到最合适的数字
groupOutput.append(str(np.argmax(scores))) # score最大的下标
# 画出来
cv2.rectangle(image, (gX - 5, gY - 5),
(gX + gW + 5, gY + gH + 5), (0, 0, 255), 1)
cv2.putText(image, "".join(groupOutput), (gX, gY - 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)
# 得到结果
output.extend(groupOutput)
# 打印结果
print("Credit Card Type: {}".format(FIRST_NUMBER[output[0]]))
print("Credit Card #: {}".format("".join(output)))
cv2.imshow("Image", image)
cv2.waitKey(0)
================================================
FILE: 02save.py
================================================
import cv2
import numpy as np
img = cv2.imread("./cat.jpg")
emptyImage = np.zeros(img.shape, np.uint8)
emptyImage2 = img.copy()
emptyImage3 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#emptyImage3[...]=0
cv2.imshow("EmptyImage", emptyImage)
cv2.imshow("Image", img)
cv2.imshow("EmptyImage2", emptyImage2)
cv2.imshow("EmptyImage3", emptyImage3)
cv2.imwrite("./img/cat2.jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), 5])
cv2.imwrite("./img/cat3.jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
cv2.imwrite("./img/cat.png", img, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])
cv2.imwrite("./img/cat2.png", img, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
cv2.waitKey(0)
cv2.destroyAllWindows()
================================================
FILE: 02项目实战-文档扫描OCR识别/Scan/.idea/document-scanner.iml
================================================
<?xml version="1.0" encoding="UTF-8"?>
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<orderEntry type="sourceFolder" forTests="false" />
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<component name="TestRunnerService">
<option name="PROJECT_TEST_RUNNER" value="Unittests" />
</component>
</module>
================================================
FILE: 02项目实战-文档扫描OCR识别/Scan/.idea/misc.xml
================================================
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 2.7.10 (/usr/bin/python)" project-jdk-type="Python SDK" />
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================================================
FILE: 02项目实战-文档扫描OCR识别/Scan/.idea/modules.xml
================================================
<?xml version="1.0" encoding="UTF-8"?>
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</project>
================================================
FILE: 02项目实战-文档扫描OCR识别/Scan/.idea/workspace.xml
================================================
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================================================
FILE: 02项目实战-文档扫描OCR识别/Scan/scan.py
================================================
# 导入工具包
import numpy as np
import argparse
import cv2
# 设置参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True,
help = "Path to the image to be scanned")
args = vars(ap.parse_args())
def order_points(pts):
# 一共4个坐标点
rect = np.zeros((4, 2), dtype = "float32")
# 按顺序找到对应坐标0123分别是 左上，右上，右下，左下
# 计算左上，右下
s = pts.sum(axis = 1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]
# 计算右上和左下
diff = np.diff(pts, axis = 1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
return rect
def four_point_transform(image, pts):
# 获取输入坐标点
rect = order_points(pts)
(tl, tr, br, bl) = rect
# 计算输入的w和h值
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
maxWidth = max(int(widthA), int(widthB))
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
maxHeight = max(int(heightA), int(heightB))
# 变换后对应坐标位置
dst = np.array([
[0, 0],
[maxWidth - 1, 0],
[maxWidth - 1, maxHeight - 1],
[0, maxHeight - 1]], dtype = "float32")
# 计算变换矩阵
M = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
# 返回变换后结果
return warped
def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
dim = None
(h, w) = image.shape[:2]
if width is None and height is None:
return image
if width is None:
r = height / float(h)
dim = (int(w * r), height)
else:
r = width / float(w)
dim = (width, int(h * r))
resized = cv2.resize(image, dim, interpolation=inter)
return resized
# 读取输入
image = cv2.imread(args["image"])
#坐标也会相同变化
ratio = image.shape[0] / 500.0
orig = image.copy()
image = resize(orig, height = 500)
# 预处理
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
# 展示预处理结果
print("STEP 1: 边缘检测")
cv2.imshow("Image", image)
cv2.imshow("Edged", edged)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 轮廓检测
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[1]
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]
# 遍历轮廓
for c in cnts:
# 计算轮廓近似
peri = cv2.arcLength(c, True)
# C表示输入的点集
# epsilon表示从原始轮廓到近似轮廓的最大距离，它是一个准确度参数
# True表示封闭的
approx = cv2.approxPolyDP(c, 0.02 * peri, True)
# 4个点的时候就拿出来
if len(approx) == 4:
screenCnt = approx
break
# 展示结果
print("STEP 2: 获取轮廓")
cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
cv2.imshow("Outline", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 透视变换
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
# 二值处理
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
ref = cv2.threshold(warped, 100, 255, cv2.THRESH_BINARY)[1]
cv2.imwrite('scan.jpg', ref)
# 展示结果
print("STEP 3: 变换")
cv2.imshow("Original", resize(orig, height = 650))
cv2.imshow("Scanned", resize(ref, height = 650))
cv2.waitKey(0)
================================================
FILE: 02项目实战-文档扫描OCR识别/Scan/test.py
================================================
# https://digi.bib.uni-mannheim.de/tesseract/
# 配置环境变量如E:\Program Files (x86)\Tesseract-OCR
# tesseract -v进行测试
# tesseract XXX.png 得到结果
# pip install pytesseract
# anaconda lib site-packges pytesseract pytesseract.py
# tesseract_cmd 修改为绝对路径即可
from PIL import Image
import pytesseract
import cv2
import os
preprocess = 'blur' #thresh
image = cv2.imread('scan.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
if preprocess == "thresh":
gray = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
if preprocess == "blur":
gray = cv2.medianBlur(gray, 3)
filename = "{}.png".format(os.getpid())
cv2.imwrite(filename, gray)
text = pytesseract.image_to_string(Image.open(filename))
print(text)
os.remove(filename)
cv2.imshow("Image", image)
cv2.imshow("Output", gray)
cv2.waitKey(0)
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2019 重庆同学
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# OpenCV
1. 人机互动 2、物体识别 3、图像分割 4、人脸识别 5、动作识别 6、运动跟踪 7、机器人 8、运动分析 9、机器视觉 10、结构分析 11、汽车安全驾驶
官网： [opencv](https://github.com/opencv)/**[opencv](https://github.com/opencv/opencv)**
环境dev：win7/Ubuntu18.04，python35/36 + opencv3.4/4.1 + VScode
**OpenCV在Python中导入名称是cv2**
## Installation
两种方法呢
* 直接pip命令安装-直接命令法
```
pip3 install opencv-python
pip3 install opencv-contrib-python
pip3 install pytesseract
```
* 下载whl文件法
1. 先去官网 https://www.lfd.uci.edu/~gohlke/pythonlibs/#opencv，下载相应Python版本的OpenCV的whl文件，如本人下载的opencv_python‑3.4.1‑cp36‑cp36m‑win_amd64.whl，
2. 然后在whl文件所在目录下，命令`pip install opencv_python‑3.4.1‑cp36‑cp36m‑win_amd64.whl` 进行安装即可
## 基于python的Opencv项目实战
* 视频：https://www.bilibili.com/video/BV1oJ411D71z
* 代码&笔记：[基于python的Opencv项目实战](基于python的Opencv项目实战)
* [图像操作(图像读取 色彩空间转换 展示图像信息 读取视频 边界填充 数值计算 图像融合 )](基于python的Opencv项目实战/图像操作/图像基本操作.ipynb)
* 图像处理
* [图像阈值 图像平滑 形态学 图像梯度 边缘检测 图像金字塔 轮廓检测 傅里叶变换](基于python的Opencv项目实战/图像操作/图像处理.ipynb)
* [直方图&模板匹配](基于python的Opencv项目实战/图像处理/图像处理-2(直方图&模板匹配).ipynb)
* [01项目实战-信用卡数字识别/template-matching-ocr](01项目实战-信用卡数字识别/template-matching-ocr)
* 运行命令：`python 01项目实战-信用卡数字识别/template-matching-ocr/ocr_template_match.py -i 01项目实战-信用卡数字识别/template-matching-ocr/images/credit_card_01.png -t 01项目实战-信用卡数字识别/template-matching-ocr/images/ocr_a_reference.png`
* [02项目实战-文档扫描OCR识别](02项目实战-文档扫描OCR识别)
* TODO
## License
Copyright (c) [双愚](https://github.com/HuangCongQing/OpenCV). All rights reserved.
Licensed under the [MIT](https://github.com/HuangCongQing/OpenCV/blob/master/LICENSE) License.
================================================
FILE: dataset/2023-02-07-22_46_36.txt
================================================
356405 1 1 9 26 118 107 255 5 3 28.327288 3.553004 -26.717430
369174 1 1 -25 34 122 99 255 2 2 38.559292 -15.835225 -30.340343
374041 1 1 -23 39 124 117 255 3 2 42.190552 25.653763 -29.472292
381224 1 1 -32 36 126 112 255 1 2 48.003067 15.497784 -33.290531
393272 1 1 -29 54 130 125 255 2 1 56.948067 45.403759 -30.476133
412354 1 1 -26 43 136 94 255 2 1 72.138451 -28.693081 -32.268276
404451 1 1 -31 45 134 119 255 1 1 66.008598 31.879519 -32.621178
410078 1 1 -32 59 136 127 255 1 1 70.601868 49.118534 -33.317238
416491 1 1 -32 66 138 130 255 1 1 75.190765 57.164684 -33.653549
417248 1 1 -30 44 138 119 255 1 1 75.907547 30.578882 -32.666927
417989 1 1 -32 33 138 103 255 1 2 76.772820 -6.803614 -32.973663
431669 1 1 -31 54 143 124 255 1 1 86.951439 43.490513 -32.691147
424476 1 1 -33 58 140 126 255 1 1 81.370392 47.843109 -33.683994
440508 1 1 -20 75 146 135 255 3 1 94.337715 67.993233 -32.384380
437213 1 1 -33 34 145 109 255 1 2 91.352646 8.151801 -33.783737
435643 1 1 -32 42 144 117 255 1 1 90.508392 27.477747 -32.932198
445895 1 1 -29 72 148 78 255 2 1 98.442528 -65.344940 -32.174683
441954 1 1 -31 43 146 94 255 1 1 95.109467 -28.642881 -32.414982
445921 1 1 -31 58 148 85 255 1 1 98.678329 -49.138176 -32.416153
459603 1 1 -22 32 152 107 255 3 2 108.988739 2.358341 -32.817055
450835 1 1 -24 37 149 115 255 3 2 102.105904 22.080008 -30.044304
451659 1 1 -28 49 150 121 255 2 1 102.966118 37.039494 -32.164280
453244 1 1 -33 43 150 117 255 1 1 104.334503 27.506050 -33.663639
455523 1 1 -31 57 151 85 255 1 1 105.683861 -47.761414 -32.315712
465917 1 1 -20 59 154 84 255 3 1 114.282875 -51.858551 -29.598215
469879 1 1 -30 81 155 74 255 1 1 116.997658 -75.190231 -31.099030
472355 1 1 -25 39 156 94 255 2 2 119.100594 -28.041435 -28.058170
480291 1 1 -28 74 159 77 255 2 1 125.445877 -67.703423 -30.710955
474766 1 1 -30 38 157 97 255 1 2 120.903931 -21.227102 -31.767727
476274 1 1 -29 84 158 72 255 2 1 121.944908 -78.619164 -30.422203
475739 1 1 -31 92 157 143 255 1 1 121.263802 87.041252 -32.007008
481168 1 1 -31 37 159 97 255 1 2 126.059738 -19.478386 -31.892456
486104 1 1 -30 72 161 133 255 1 1 129.454178 65.212982 -31.692595
486053 1 1 -30 46 161 120 255 1 1 129.925491 33.226265 -32.261318
486154 1 1 -31 101 161 147 255 1 1 129.728302 96.424797 -32.020245
502083 1 1 -13 57 166 128 255 5 1 142.270874 52.416275 -23.522245
494956 1 1 -24 101 164 147 255 3 1 136.261154 97.504860 -26.352594
494065 1 1 -27 50 163 123 255 2 1 135.810562 40.678677 -29.505653
512396 1 1 -18 31 170 105 255 4 2 150.328918 -2.382168 -31.693666
498733 1 1 -30 52 165 88 255 1 1 139.698761 -41.751480 -31.253775
501054 1 1 -28 95 166 67 255 2 1 141.705627 -90.887863 -29.821276
504569 1 1 -17 107 167 151 255 4 1 144.335449 105.646332 -21.273010
510972 1 1 -16 109 169 152 255 4 1 148.864548 107.698647 -16.970018
514875 1 1 -12 48 170 126 255 5 1 151.887207 46.922947 -13.402631
517261 1 1 -11 40 171 122 255 5 1 154.223465 38.347336 -13.084762
517927 1 1 -19 53 172 87 255 4 1 154.921448 -45.005325 -28.543715
522851 1 1 -14 39 173 119 255 5 2 158.364746 32.029282 -23.676428
521109 1 1 -30 64 172 82 255 1 1 157.012146 -56.635349 -31.377432
523490 1 1 -30 74 173 77 255 1 1 158.802628 -68.170586 -30.972445
524421 1 1 -26 31 174 111 255 2 2 159.664185 13.221807 -28.307915
525377 1 1 -16 112 174 153 255 4 1 160.380051 111.019226 -16.875196
530852 1 1 -14 39 176 120 255 5 2 164.898254 32.815666 -21.231853
533845 1 1 -11 98 177 65 255 5 1 167.327866 -96.590073 -20.406582
531774 1 1 -18 111 176 152 255 4 1 165.410965 109.154205 -21.728746
536368 1 1 -29 36 178 97 255 2 2 169.294830 -19.818279 -30.370035
554818 1 1 12 11 184 111 255 5 4 183.278687 11.551304 1.359617
537223 1 1 -26 32 178 112 255 2 2 169.632782 14.603360 -28.580215
544503 1 1 -16 67 180 133 255 4 1 175.184631 64.824745 -16.995951
546179 1 1 -20 114 181 153 255 3 1 176.664886 111.945419 -24.007124
554051 1 1 -17 41 183 120 255 4 1 182.677826 32.493649 -25.910984
558655 1 1 -15 92 185 67 255 4 1 186.820084 -90.430153 -20.377075
560446 1 1 -26 39 186 118 255 2 2 187.694733 29.298435 -27.185402
563464 1 1 -18 87 187 70 255 4 1 190.304321 -84.634682 -21.734791
630111 1 1 -16 71 209 135 255 4 1 242.326279 69.544579 -17.901327
632500 1 1 -15 64 209 132 255 4 1 243.939896 62.813786 -16.640633
639586 1 1 -29 31 212 102 255 2 2 249.750092 -8.413772 -30.047022
================================================
FILE: practice/vis/01show_bbox.ipynb
================================================
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================================================
FILE: practice/vis/02show_line.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 函数demo\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# -*- coding:utf-8 -*-\n",
"# By：Eastmount\n",
"import cv2\n",
"import numpy as np\n",
"\n",
"#创建黑色图像\n",
"img = np.zeros((256,256,3), np.uint8)\n",
"\n",
"#绘制直线\n",
"cv2.line(img, (0,0), (255,255), (55,255,155), 5)\n",
"\n",
"#显示图像\n",
"cv2.imshow(\"line\", img)\n",
"\n",
"#等待显示\n",
"cv2.waitKey(0)\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 实践"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(4056, 3040, 3) <class 'numpy.ndarray'>\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"import cv2\n",
"import numpy as np\n",
"import random\n",
"\n",
"img_path = \"/home/hcq/data/01project/wire_dataset/20230112camera_lidar_南京现场采集/20230112/766B0D0B001E46A7A29D3B36B8B76291.jpg\"\n",
"\n",
"line_path = \"/home/hcq/python/OpenCV/dataset/2023-02-07-22_46_36.txt\"\n",
"\n",
"img = cv2.imread(img_path, flags=1) # flags=1 读取彩色图像(BGR) 默认numpy\n",
"print(img.shape, type(img)) # (4056, 3040, 3)\n",
"\n",
"\n",
"\n",
"def draw_rectangle(img, center_x, center_y, width, height):\n",
" # 从左上角坐标为（20,20），右下角坐标为（150,250），绘制的矩形颜色为蓝色（255,0,0），粗细为2。\n",
" cv2.rectangle(img, (int(center_x+width/2),int(center_y+height/2)), (int(center_x+width),int(center_y+height)), (255,0,0), 2)\n",
"\n",
"\n",
"\n",
"def draw_line(img, x1, y1, x2, y2):\n",
" cv2.line(img, (x1,y1), (x2,y2), (55,255,155), 5)\n",
"\n",
"\n",
"with open(line_path, \"r\") as f:\n",
" for line in f.readlines():\n",
" line = line.split(' ')\n",
" # 中心点\n",
" center_x = line[-3]\n",
" center_y = line[-2]\n",
" center_x = int(float(center_x))+random.randint(10,999)\n",
" center_y = int(float(center_y)+random.randint(10,999)+1000)\n",
"\n",
" width = 1000\n",
" height = 1000\n",
" cv2.putText(img, '{:.2f}'.format(100*random.random()) , (int(center_x+width/2),int(center_y+height/2)), cv2.FONT_HERSHEY_TRIPLEX, 2, (150, 0, 180), 2)\n",
" draw_rectangle(img, center_x, center_y, width, height)\n",
" target_x_list = [int(img.shape[1]/2), 1000, 300]\n",
" target_x = target_x_list[random.randint(0,2)]\n",
" target_y = random.randint(10,999)\n",
"\n",
" draw_line(img,int(center_x+width/2),int(center_y+height/2), target_x,target_y )\n",
"\n",
" # print(line)\n",
"\n",
"#显示图像\n",
"# cv2.imshow(\"line\", img)\n",
"\n",
"\n",
"#绘制矩形\n",
"# 从左上角坐标为（20,20），右下角坐标为（150,250），绘制的矩形颜色为蓝色（255,0,0），粗细为2。\n",
"# cv2.rectangle(img, (20,20), (150,250), (255,0,0), 2)\n",
"# #绘制直线\n",
"# cv2.line(img, (0,0), (255,255), (55,255,155), 5)\n",
"\n",
"\n",
"# 保存\n",
"cv2.imwrite('./result.jpg', img) # 保存图像文件\n",
"\n",
"#等待显示\n",
"# cv2.waitKey(0)\n",
"# cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"44\n"
]
}
],
"source": [
"a = '44.244'\n",
"print(int(float(a)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.13 ('pcdet')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"nbformat": 4,
"nbformat_minor": 2
}
================================================
FILE: practice/vis/03show_text.ipynb
================================================
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"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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================================================
FILE: 基于python的Opencv项目实战/图像处理/图像处理-2(直方图&模板匹配).ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import cv2 #opencv读取的格式是BGR\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt#Matplotlib是RGB\n",
"%matplotlib inline "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def cv_show(img,name):\n",
" cv2.imshow(name,img)\n",
" cv2.waitKey()\n",
" cv2.destroyAllWindows()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 直方图"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### cv2.calcHist(images,channels,mask,histSize,ranges)\n",
"\n",
"- images: 原图像图像格式为 uint8 或 float32。**当传入函数时应 用中括号 [] 括来例如[img]**\n",
"- channels: 同样用中括号括来它会告函数我们统幅图 像的直方图。如果入图像是灰度图它的值就是 [0]如果是彩色图像 的传入的参数可以是 [0][1][2] 它们分别对应着 BGR。 \n",
"- mask: 掩模图像（统计一部分）。统整幅图像的直方图就把它为 None。但是如 果你想统图像某一分的直方图的你就制作一个掩模图像并 使用它。\n",
"- histSize:BIN 的数目。也应用中括号括来\n",
"- ranges: 像素值范围常为 [0 - 256] "
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(256, 1)"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img = cv2.imread('cat.jpg',0) #0表示灰度图\n",
"hist = cv2.calcHist([img],[0],None,[256],[0,256]) # 当传入函数时应 用中括号 [] 括来例如img\n",
"hist.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"plt.hist(img.ravel(),256); # ravel()方法将数组维度拉成一维数组 ravel()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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5WQdo2tSMAM4CRgAMhvOc6Gi9A9gVAYiI0A/4PXv0aCE7GwYN0tNBU6ZogfjwQ2jWQ8cA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\nLBSRcGCh/TPAICDc/hoPTAI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7Q4B/ANe0uYZfd/7KiZwTrl3k46PN2ubNg3373GvAWaDG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\nRQlQ/g9HYrA2vIZBfgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x216fcef24e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = cv2.imread('cat.jpg') \n",
"color = ('b','g','r')\n",
"for i,col in enumerate(color): \n",
" histr = cv2.calcHist([img],[i],None,[256],[0,256]) \n",
" plt.plot(histr,color = col) \n",
" plt.xlim([0,256]) \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### mask掩码操作（框定区域）"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(414, 500)\n"
]
}
],
"source": [
"# 创建mast\n",
"mask = np.zeros(img.shape[:2], np.uint8) # img.shape[:2] 长宽\n",
"print (mask.shape)\n",
"mask[100:300, 100:400] = 255 # 要保存的区域设置为255\n",
"cv_show(mask,'mask')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"img = cv2.imread('cat.jpg', 0)\n",
"cv_show(img,'img')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"masked_img = cv2.bitwise_and(img, img, mask=mask)# bitwise_and 执行与操作\n",
"cv_show(masked_img,'masked_img')"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hist_full = cv2.calcHist([img], [0], None, [256], [0, 256])\n",
"hist_mask = cv2.calcHist([img], [0], mask, [256], [0, 256]) # mask掩码操作"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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m4+LFizzzzDMUi0VcLhdTU1P82Z/9GRsbGxSLRYbDIdeuXWNsbAzTNMlkMiws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gitextract_u125kwrs/
├── .gitignore
├── 00.py
├── 01read_show.py
├── 01项目实战-信用卡数字识别/
│ └── template-matching-ocr/
│ ├── myutils.py
│ └── ocr_template_match.py
├── 02save.py
├── 02项目实战-文档扫描OCR识别/
│ └── Scan/
│ ├── .idea/
│ │ ├── document-scanner.iml
│ │ ├── misc.xml
│ │ ├── modules.xml
│ │ └── workspace.xml
│ ├── scan.py
│ └── test.py
├── LICENSE
├── README.md
├── dataset/
│ └── 2023-02-07-22_46_36.txt
├── practice/
│ └── vis/
│ ├── 01show_bbox.ipynb
│ ├── 02show_line.ipynb
│ └── 03show_text.ipynb
└── 基于python的Opencv项目实战/
├── 图像处理/
│ └── 图像处理-2(直方图&模板匹配).ipynb
└── 图像操作/
├── 图像基本操作.ipynb
└── 图像处理.ipynb
SYMBOL INDEX (6 symbols across 3 files) FILE: 01项目实战-信用卡数字识别/template-matching-ocr/myutils.py function sort_contours (line 12) | def sort_contours(cnts, method="left-to-right"): function resize (line 28) | def resize(image, width=None, height=None, inter=cv2.INTER_AREA): FILE: 01项目实战-信用卡数字识别/template-matching-ocr/ocr_template_match.py function cv_show (line 35) | def cv_show(name,img): FILE: 02项目实战-文档扫描OCR识别/Scan/scan.py function order_points (line 12) | def order_points(pts): function four_point_transform (line 29) | def four_point_transform(image, pts): function resize (line 57) | def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
Condensed preview — 21 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,226K chars).
[
{
"path": ".gitignore",
"chars": 5,
"preview": "代码+资料"
},
{
"path": "00.py",
"chars": 33,
"preview": "import cv2\nprint(cv2.__version__)"
},
{
"path": "01read_show.py",
"chars": 204,
"preview": "# encoding:utf-8\nimport cv2\n\nimg = cv2.imread(\"./cat.jpg\")\ncv2.namedWindow(\"Image显示\")\ncv2.imshow(\"Image显示\", img)\ncv2.wai"
},
{
"path": "01项目实战-信用卡数字识别/template-matching-ocr/myutils.py",
"chars": 1292,
"preview": "'''\r\nDescription: OCR-模板匹配 参考：https://www.bilibili.com/video/BV1oJ411D71z?t=11&p=9\r\nAuthor: HCQ\r\nCompany(School): UCAS\r"
},
{
"path": "01项目实战-信用卡数字识别/template-matching-ocr/ocr_template_match.py",
"chars": 5870,
"preview": "'''\nDescription: OCR-模板匹配 参考：https://www.bilibili.com/video/BV1oJ411D71z?t=11&p=9\n运行命令： python ocr_template_match.py "
},
{
"path": "02save.py",
"chars": 674,
"preview": "import cv2\nimport numpy as np\n\nimg = cv2.imread(\"./cat.jpg\")\nemptyImage = np.zeros(img.shape, np.uint8)\n\nemptyImage2 = i"
},
{
"path": "02项目实战-文档扫描OCR识别/Scan/.idea/document-scanner.iml",
"chars": 398,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<module type=\"PYTHON_MODULE\" version=\"4\">\n <component name=\"NewModuleRootManager"
},
{
"path": "02项目实战-文档扫描OCR识别/Scan/.idea/misc.xml",
"chars": 206,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"ProjectRootManager\" version=\"2\" project-"
},
{
"path": "02项目实战-文档扫描OCR识别/Scan/.idea/modules.xml",
"chars": 284,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"ProjectModuleManager\">\n <modules>\n "
},
{
"path": "02项目实战-文档扫描OCR识别/Scan/.idea/workspace.xml",
"chars": 11368,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<project version=\"4\">\n <component name=\"ChangeListManager\">\n <list default=\"t"
},
{
"path": "02项目实战-文档扫描OCR识别/Scan/scan.py",
"chars": 2964,
"preview": "# 导入工具包\nimport numpy as np\nimport argparse\nimport cv2\n\n# 设置参数\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--im"
},
{
"path": "02项目实战-文档扫描OCR识别/Scan/test.py",
"chars": 886,
"preview": "# https://digi.bib.uni-mannheim.de/tesseract/\r\n# 配置环境变量如E:\\Program Files (x86)\\Tesseract-OCR\r\n# tesseract -v进行测试\r\n# tess"
},
{
"path": "LICENSE",
"chars": 1061,
"preview": "MIT License\n\nCopyright (c) 2019 重庆同学\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof th"
},
{
"path": "README.md",
"chars": 1580,
"preview": "# OpenCV\n\n1. 人机互动 2、物体识别 3、图像分割 4、人脸识别 5、动作识别 6、运动跟踪 7、机器人 8、运动分析 9、机器视觉 10、结构分析 11、汽车安全驾驶\n\n官网： [opencv](https://github."
},
{
"path": "dataset/2023-02-07-22_46_36.txt",
"chars": 4341,
"preview": "356405 1 1 9 26 118 107 255 5 3 28.327288 3.553004 -26.717430\n369174 1 1 -25 34 122 99 255 2 2 38.559292 -15.835225 -30."
},
{
"path": "practice/vis/01show_bbox.ipynb",
"chars": 518,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": "
},
{
"path": "practice/vis/02show_line.ipynb",
"chars": 4603,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n "
},
{
"path": "practice/vis/03show_text.ipynb",
"chars": 518,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": "
},
{
"path": "基于python的Opencv项目实战/图像处理/图像处理-2(直方图&模板匹配).ipynb",
"chars": 1063234,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outp"
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{
"path": "基于python的Opencv项目实战/图像操作/图像基本操作.ipynb",
"chars": 440581,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 图像基本操作\"\n ]\n },\n {\n \"cell_t"
},
{
"path": "基于python的Opencv项目实战/图像操作/图像处理.ipynb",
"chars": 619047,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 灰度图\"\n ]\n },\n {\n \"cell_typ"
}
]
About this extraction
This page contains the full source code of the HuangCongQing/OpenCV GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 21 files (2.1 MB), approximately 541.0k tokens, and a symbol index with 6 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.