传统opencv分割多张票据
针对于票据或者是多个零配件等物体通过修改对应参数可以做到大部分近似矩形物体的分割由于无法上传.ipynb文件，各位可以复制进jupyter内查看并debug代码
### 0.导入包 定义显示函数
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"### 0.导入包 定义显示函数"
]
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"source": [
"import cv2 as cv\n",
"import numpy as np\n",
"\n",
"def show_img(img, win_name):\n",
"\n",
" # cv.namedWindow(win_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)\n",
"\n",
" cv.imshow(win_name, img)\n",
" \n",
" cv.waitKey(0)\n",
" cv.destroyAllWindows()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.读取图片 转为灰度图 利用高斯模糊消除噪声"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"img_path = './fapiao_test//2tickets_test3.jpg'\n",
"img = cv.imread(img_path)\n",
"img = cv.resize(img, (600, 900))\n",
"show_img(img, 'img')\n",
"\n",
"gray = cv.cvtColor(img, cv.COLOR_RGB2GRAY)\n",
"\n",
"gray = cv.GaussianBlur(gray, (7, 7), 0)\n",
"show_img(gray, 'gray')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.使用边缘检测"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"edged = cv.Canny(gray, 100, 200)\n",
"show_img(edged, 'edged')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.形态学操作 进行腐蚀膨胀进一步消除噪声"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"kernel = np.ones((7, 7), np.uint8)\n",
"morphed = cv.dilate(edged, kernel, iterations=3)\n",
"morphed = cv.erode(morphed, kernel, iterations=3)\n",
"show_img(morphed, 'morphed')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.寻找轮廓 并排序 找出其中最大的4个轮廓"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"morphed_copy = morphed.copy()\n",
"cnts, _ = cv.findContours(morphed_copy, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)\n",
"if len(cnts) is not 0:\n",
" cnts = sorted(cnts, key=cv.contourArea, reverse=True)[:4]\n",
" print(len(cnts))\n",
"else:\n",
" print(\"Did not find contours\\n\")\n",
"\n",
"# for cnt in cnts:\n",
"# x, y, w, h = cv.boundingRect(cnt)\n",
"# print(x, y, w, h)\n",
"# print('box_area: ', w * h)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.将轮廓处理成矩形 在原图上裁剪"
]
},
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"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for cnt in cnts:\n",
" # 用周长的0.1倍作为阈值，对轮廓做近似处理，使其变成一个矩形\n",
" epsilon = 0.1 * cv.arcLength(cnt, True)\n",
" approx = cv.approxPolyDP(cnt, epsilon, True)\n",
"\n",
" ticket_copy = img.copy()\n",
" cv.drawContours(ticket_copy, , -1, (255, 0, 0), 2)\n",
" show_img(ticket_copy, 'ticket_copy')\n",
"\n",
" # 获取透视变换的原坐标\n",
" if approx.shape is not 4:\n",
" print(\"Found a non-rect\\n\")\n",
" continue\n",
" src_coor = np.reshape(approx, (4, 2))\n",
" src_coor = np.float32(src_coor)\n",
"\n",
" # 右上,左上,左下,右下 坐标\n",
" (tr, tl, bl, br) = src_coor\n",
" print(tl, tl, br, br)\n",
"\n",
" x1,x2 = min(tl, br), max(tl, br)\n",
" y1,y2 = min(tl, br), max(tl, br)\n",
"\n",
" img_result = img\n",
" show_img(img_result, 'img_result')\n",
"\n",
" # # 计算宽\n",
" # w1 = np.sqrt((tr - tl) ** 2 + (tr - tl) ** 2)\n",
" # w2 = np.sqrt((br - bl) ** 2 + (br - bl) ** 2)\n",
" # # 求出比较大的w\n",
" # max_w = max(int(w1), int(w2))\n",
" # # 计算高\n",
" # h1 = np.sqrt((bl - tl) ** 2 + (bl - tl) ** 2)\n",
" # h2 = np.sqrt((br - tr) ** 2 + (br - tr) ** 2)\n",
" # # 求出比较大的h\n",
" # max_h = max(int(h1), int(h2))\n",
"\n",
" # # 透视变换的目标坐标\n",
" # dst_coor = np.array([, , , ], dtype=np.float32)\n",
"\n",
" # # 求转换矩阵\n",
" # trans_mat = cv.getPerspectiveTransform(src_coor, dst_coor)\n",
" # # 进行转换，将图中对应坐标的图片截取出来，并转换到dst_coor大小\n",
" # warped = cv.warpPerspective(img, trans_mat, (max_w, max_h))\n",
"\n",
" # warped = cv.rotate(warped, cv.ROTATE_90_COUNTERCLOCKWISE)\n",
"\n",
" # show_img(warped, 'result')"
]
}
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