[
{
"path": ".gitignore",
"content": "代码+资料"
},
{
"path": "00.py",
"content": "import cv2\nprint(cv2.__version__)"
},
{
"path": "01read_show.py",
"content": "# encoding:utf-8\nimport cv2\n\nimg = cv2.imread(\"./cat.jpg\")\ncv2.namedWindow(\"Image显示\")\ncv2.imshow(\"Image显示\", img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n\n# https: // www.cnblogs.com/zangyu/p/5802142.html\n"
},
{
"path": "01项目实战-信用卡数字识别/template-matching-ocr/myutils.py",
"content": "'''\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\nEmail: 1756260160@qq.com\r\nDate: 2021-01-23 11:12:41\r\nLastEditTime: 2021-01-25 12:28:10\r\nFilePath: /OpenCV/01项目实战-信用卡数字识别/template-matching-ocr/myutils.py\r\n'''\r\nimport cv2\r\n# 轮廓排序\r\ndef sort_contours(cnts, method=\"left-to-right\"):\r\n reverse = False\r\n i = 0\r\n\r\n if method == \"right-to-left\" or method == \"bottom-to-top\":\r\n reverse = True\r\n\r\n if method == \"top-to-bottom\" or method == \"bottom-to-top\":\r\n i = 1\r\n 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\r\n (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes), # sorted\r\n key=lambda b: b[1][i], reverse=reverse)) # reverse\r\n\r\n return cnts, boundingBoxes\r\n\r\n# resize \r\ndef resize(image, width=None, height=None, inter=cv2.INTER_AREA):\r\n dim = None\r\n (h, w) = image.shape[:2]\r\n if width is None and height is None:\r\n return image\r\n if width is None:\r\n r = height / float(h)\r\n dim = (int(w * r), height)\r\n else:\r\n r = width / float(w)\r\n dim = (width, int(h * r))\r\n resized = cv2.resize(image, dim, interpolation=inter)\r\n return resized"
},
{
"path": "01项目实战-信用卡数字识别/template-matching-ocr/ocr_template_match.py",
"content": "'''\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 -i xxx -t xxx\n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\nAuthor: HCQ\nCompany(School): UCAS\nEmail: 1756260160@qq.com\nDate: 2021-01-23 11:12:41\nLastEditTime: 2021-01-25 14:07:18\nFilePath: /OpenCV/01项目实战-信用卡数字识别/template-matching-ocr/ocr_template_match.py\n'''\n# 导入工具包\nfrom imutils import contours\nimport numpy as np\nimport argparse\nimport cv2\nimport myutils\n\n# 设置参数\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--image\", required=True,\n\thelp=\"path to input image\")\nap.add_argument(\"-t\", \"--template\", required=True,\n\thelp=\"path to template OCR-A image\")\nargs = vars(ap.parse_args())\n\n# 指定信用卡类型\nFIRST_NUMBER = {\n\t\"3\": \"American Express\",\n\t\"4\": \"Visa\",\n\t\"5\": \"MasterCard\",\n\t\"6\": \"Discover Card\"\n}\n# 绘图展示\ndef cv_show(name,img):\n\tcv2.imshow(name, img)\n\tcv2.waitKey(0)\n\tcv2.destroyAllWindows()\n\n# ==========================开始 1 处理模板图片========================================\n# 读取一个模板图像\nimg = 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\ncv_show('img',img)\n# 灰度图\nref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\ncv_show('ref',ref)\n# 二值图像\nref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]\ncv_show('ref',ref)\n\n# 计算轮廓\n#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\n#返回的list中每个元素都是图像中的一个轮廓\n\n# 高版本没有第一个参数ref_了，只会返回后两个\n# ref_, refCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) # 低版本\nrefCnts, hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) # 高版本\n\ncv2.drawContours(img,refCnts,-1,(0,0,255),3) \ncv_show('img',img)\nprint (np.array(refCnts).shape) # 多少轮廓\nrefCnts = myutils.sort_contours(refCnts, method=\"left-to-right\")[0] #排序，从左到右，从上到下\ndigits = {}\n\n# 遍历每一个轮廓\nfor (i, c) in enumerate(refCnts):\n\t# 计算外接矩形并且resize成合适大小\n\t(x, y, w, h) = cv2.boundingRect(c)\n\troi = ref[y:y + h, x:x + w] # x旋转区域\n\troi = cv2.resize(roi, (57, 88)) # resize\n\n\t# 每一个数字对应每一个模板\n\tdigits[i] = roi\n\n# =======================2 处理信用卡图片================================\n# 初始化卷积核\nrectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 3))\nsqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) # 核大小 5x5\n\n#读取输入图像，预处理\nimage = cv2.imread(args[\"image\"]) # 参数\ncv_show('image',image)\nimage = myutils.resize(image, width=300)\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\ncv_show('gray',gray)\n\n#礼帽操作，突出更明亮的区域（字体区域）\ntophat = cv2.morphologyEx(gray, cv2.MORPH_TOPHAT, rectKernel) # rectKernel初始化核\ncv_show('tophat',tophat) \n# \ngradX = cv2.Sobel(tophat, ddepth=cv2.CV_32F, dx=1, dy=0, #ksize=-1相当于用3*3的\n\tksize=-1)\n\n\ngradX = np.absolute(gradX) # 只用x效果更好，随意没用xy梯度\n(minVal, maxVal) = (np.min(gradX), np.max(gradX))\ngradX = (255 * ((gradX - minVal) / (maxVal - minVal))) # 归一化\ngradX = gradX.astype(\"uint8\")\n\nprint (np.array(gradX).shape)\ncv_show('gradX',gradX)\n\n#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\ngradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel) \ncv_show('gradX',gradX)\n#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\nthresh = cv2.threshold(gradX, 0, 255,\n\tcv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] \ncv_show('thresh',thresh)\n\n#再来一个闭操作\n\nthresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel) #再来一个闭操作\ncv_show('thresh',thresh)\n\n# 计算轮廓\n\n# 高版本没有第一个参数thresh_了，只会返回后两个\n# thresh_, threshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,\n# \tcv2.CHAIN_APPROX_SIMPLE)\nthreshCnts, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,\n\tcv2.CHAIN_APPROX_SIMPLE)\n\ncnts = threshCnts # 最终轮廓\ncur_img = image.copy()\ncv2.drawContours(cur_img,cnts,-1,(0,0,255),3) \ncv_show('img',cur_img)\nlocs = [] # 有价值的区域\n\n# 遍历轮廓\nfor (i, c) in enumerate(cnts):\n\t# 计算矩形\n\t(x, y, w, h) = cv2.boundingRect(c)\n\tar = w / float(h) # 长宽比\n\n\t# 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\n\tif ar > 2.5 and ar < 4.0:\n\n\t\tif (w > 40 and w < 55) and (h > 10 and h < 20):\n\t\t\t#符合的留下来\n\t\t\tlocs.append((x, y, w, h))\n\n# 将符合的轮廓从左到右排序\nlocs = sorted(locs, key=lambda x:x[0])\noutput = []\n\n# 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）\nfor (i, (gX, gY, gW, gH)) in enumerate(locs):\n\t# initialize the list of group digits\n\tgroupOutput = []\n\n\t# 根据坐标提取每一个组\n\tgroup = gray[gY - 5:gY + gH + 5, gX - 5:gX + gW + 5] # 往外扩大一点范围\n\tcv_show('group',group)\n\t# 预处理（二值化）\n\tgroup = cv2.threshold(group, 0, 255,\n\t\tcv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]\n\tcv_show('group',group)\n\t# 计算每一组的轮廓（4个数字，四个轮廓）\n\t# 高版本没有第一个参数group_了，只会返回后两个\n\t# group_,digitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,\n\t# \tcv2.CHAIN_APPROX_SIMPLE)\n\tdigitCnts,hierarchy = cv2.findContours(group.copy(), cv2.RETR_EXTERNAL,\n\t\tcv2.CHAIN_APPROX_SIMPLE)\n\tdigitCnts = contours.sort_contours(digitCnts,\n\t\tmethod=\"left-to-right\")[0]\n\n\t# 计算每一组中的每一个数值\n\tfor c in digitCnts:\n\t\t# 找到当前数值的轮廓，resize成合适的的大小\n\t\t(x, y, w, h) = cv2.boundingRect(c)\n\t\troi = group[y:y + h, x:x + w]\n\t\troi = cv2.resize(roi, (57, 88))\n\t\tcv_show('roi',roi)\n\n\t\t# 计算匹配得分\n\t\tscores = []\n\n\t\t# 在模板中计算每一个得分=============================================================================\n\t\tfor (digit, digitROI) in digits.items():\n\t\t\t# 模板匹配（和模板中的10个数字匹配）\n\t\t\tresult = cv2.matchTemplate(roi, digitROI,\n\t\t\t\tcv2.TM_CCOEFF)\n\t\t\t(_, score, _, _) = cv2.minMaxLoc(result)\n\t\t\tscores.append(score)\n\n\t\t# 得到最合适的数字\n\t\tgroupOutput.append(str(np.argmax(scores))) # score最大的下标\n\n\t# 画出来\n\tcv2.rectangle(image, (gX - 5, gY - 5),\n\t\t(gX + gW + 5, gY + gH + 5), (0, 0, 255), 1)\n\tcv2.putText(image, \"\".join(groupOutput), (gX, gY - 15),\n\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.65, (0, 0, 255), 2)\n\n\t# 得到结果\n\toutput.extend(groupOutput)\n\n# 打印结果\nprint(\"Credit Card Type: {}\".format(FIRST_NUMBER[output[0]]))\nprint(\"Credit Card #: {}\".format(\"\".join(output)))\ncv2.imshow(\"Image\", image)\ncv2.waitKey(0)"
},
{
"path": "02save.py",
"content": "import cv2\nimport numpy as np\n\nimg = cv2.imread(\"./cat.jpg\")\nemptyImage = np.zeros(img.shape, np.uint8)\n\nemptyImage2 = img.copy()\n\nemptyImage3 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n#emptyImage3[...]=0\n\ncv2.imshow(\"EmptyImage\", emptyImage)\ncv2.imshow(\"Image\", img)\ncv2.imshow(\"EmptyImage2\", emptyImage2)\ncv2.imshow(\"EmptyImage3\", emptyImage3)\ncv2.imwrite(\"./img/cat2.jpg\", img, [int(cv2.IMWRITE_JPEG_QUALITY), 5])\ncv2.imwrite(\"./img/cat3.jpg\", img, [int(cv2.IMWRITE_JPEG_QUALITY), 100])\ncv2.imwrite(\"./img/cat.png\", img, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])\ncv2.imwrite(\"./img/cat2.png\", img, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n"
},
{
"path": "02项目实战-文档扫描OCR识别/Scan/.idea/document-scanner.iml",
"content": "\n\n \n \n \n \n \n \n \n \n"
},
{
"path": "02项目实战-文档扫描OCR识别/Scan/.idea/misc.xml",
"content": "\n\n \n"
},
{
"path": "02项目实战-文档扫描OCR识别/Scan/.idea/modules.xml",
"content": "\n\n \n \n \n \n \n"
},
{
"path": "02项目实战-文档扫描OCR识别/Scan/.idea/workspace.xml",
"content": "\n\n \n
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