# base_image
对opencv_python常用接口的二次开发
建议 opencv version >= 4.5.5(不同opencv版本的python绑定,函数名可能会不同)
# Example
## Create
1. 默认方式创建图片对象
```python
import cv2
from baseImage import Image
from baseImage.constant import Place
Image(data='tests/image/0.png') # 使用默认方式创建
```
2. 通过其他参数,调整图片参数
- 使用place参数,修改数据格式
- Ndarray: 格式为numpy.ndarray格式
- Umat: python的绑定不多,没有ndarray灵活,可以用于opencl加速
- GpuMat: opencv的cuda格式,需要注意显存消耗
- 可以通过常量`Default_Pool`设定缓冲区
```python
import cv2
from baseImage import Setting
cv2.cuda.setBufferPoolUsage(True)
cv2.cuda.setBufferPoolConfig(cv2.cuda.getDevice(), 1024 * 1024 * (3 + 3), 1)
stream = cv2.cuda.Stream()
pool = cv2.cuda.BufferPool(stream)
Setting.Default_Stream = stream
Setting.Default_Pool = pool
```
```python
import cv2
from baseImage import Image
from baseImage.constant import Place
Image(data='tests/image/0.png', place=Place.Ndarray) # 使用numpy
Image(data='tests/image/0.png', place=Place.UMat) # 使用Umat
Image(data='tests/image/0.png', place=Place.GpuMat) # 使用cuda
```
- 使用dtype,修改数据类型
```python
import cv2
import numpy as np
from baseImage.utils.api import cvType_to_npType, npType_to_cvType
from baseImage import Image
Image(data='tests/image/0.png', dtype=np.uint8)
Image(data='tests/image/0.png', dtype=np.int8)
Image(data='tests/image/0.png', dtype=np.uint16)
Image(data='tests/image/0.png', dtype=np.int16)
Image(data='tests/image/0.png', dtype=np.int32)
Image(data='tests/image/0.png', dtype=np.float32)
Image(data='tests/image/0.png', dtype=np.float64)
# cvType_to_npType和npType_to_cvType提供了numpy转opencv数据格式的方法, cv的数据格式意义自行百度
```
- clone,用于处理是否拷贝原数据
```python
import cv2
import numpy as np
from baseImage import Image, Rect
img1 = Image(data='tests/image/0.png')
img2 = Image(img1, clone=False)
img2.rectangle(rect=Rect(0, 0, 200, 200), color=(255, 0, 0), thickness=-1)
img2.imshow('img2')
img1.imshow('img1')
cv2.waitKey(0)
```
## property
1. shape: 获取图片的长、宽、通道数
```python
from baseImage import Image
img = Image(data='tests/image/0.png')
print(img.shape)
# expect output
# (1037, 1920, 3)
```
2. size: 获取图片的长、宽
```python
from baseImage import Image
img = Image(data='tests/image/0.png')
print(img.size)
# expect output
# (1037, 1920)
```
3. channels: 获取图片的通道数量
```python
from baseImage import Image
img = Image(data='tests/image/0.png')
print(img.channels)
# expect output
# 3
```
4. dtype: 获取图片的数据类型
```python
from baseImage import Image
img = Image(data='tests/image/0.png')
print(img.dtype)
# expect output
# numpy.uint8
```
5. place: 获取图片的数据格式
```python
from baseImage import Image
from baseImage.constant import Place
img = Image(data='tests/image/0.png', place=Place.Ndarray)
print(img.place == Place.Ndarray)
# expect output
# True
```
6. data: 获取图片数据
```python
from baseImage import Image
img = Image(data='tests/image/0.png')
print(img.data)
```
## Function
1. dtype_convert: 数据类型转换
- 将修改原图像数据
```python
from baseImage import Image
import numpy as np
img = Image(data='tests/image/0.png', dtype=np.uint8)
print(img.dtype)
img.dtype_convert(dtype=np.float32)
print(img.dtype)
```
2. place_convert: 数据格式转换
- 将修改原图像数据
```python
from baseImage import Image
from baseImage.constant import Place
img = Image(data='tests/image/0.png', place=Place.Ndarray)
print(img.place == Place.Ndarray)
img.place_convert(place=Place.UMat)
print(img.place == Place.Ndarray)
print(img.place == Place.UMat)
```
3. clone: 克隆一个新的图片对象
```python
from baseImage import Image
from baseImage.constant import Place
img = Image(data='tests/image/0.png', place=Place.Ndarray)
img2 = img.clone()
print(img == img2)
```
4. rotate: 旋转图片, 现在只支持opencv自带的三个方向
```python
from baseImage import Image
import cv2
img = Image(data='tests/image/0.png')
img.rotate(code=cv2.ROTATE_180).imshow('180')
img.rotate(code=cv2.ROTATE_90_CLOCKWISE).imshow('90_CLOCKWISE')
img.rotate(code=cv2.ROTATE_90_COUNTERCLOCKWISE).imshow('90_COUNTERCLOCKWISE')
cv2.waitKey(0)
```
5. resize: 缩放图像
```python
from baseImage import Image
img = Image(data='tests/image/0.png')
new_img = img.resize(200, 200)
print(new_img.size)
```
6. cvtColor: 转换图片颜色空间
```python
from baseImage import Image
import cv2
img = Image(data='tests/image/0.png')
new_img = img.cvtColor(cv2.COLOR_BGR2GRAY)
new_img.imshow()
cv2.waitKey(0)
```
7. crop: 裁剪图片
```python
from baseImage import Image, Rect
import cv2
img = Image(data='tests/image/0.png')
new_img = img.crop(rect=Rect(0, 0, 400, 400))
new_img.imshow()
cv2.waitKey(0)
```
8. threshold: 二值化图片
```python
from baseImage import Image
import cv2
img = Image(data='tests/image/0.png')
new_img = img.threshold(thresh=0, maxval=255, code=cv2.THRESH_OTSU)
new_img.imshow()
cv2.waitKey(0)
```
9. rectangle: 在图像上画出矩形
- 会在原图上进行修改
```python
from baseImage import Image, Rect
import cv2
img = Image(data='tests/image/0.png')
img.rectangle(rect=Rect(100, 100, 300, 300), color=(255, 0, 0), thickness=-1)
img.imshow()
cv2.waitKey(0)
```
10. copyMakeBorder: 扩充图片边缘
```python
from baseImage import Image
import cv2
img = Image(data='tests/image/0.png')
new_img = img.copyMakeBorder(top=10, bottom=10, left=10, right=10, borderType=cv2.BORDER_REPLICATE)
new_img.imshow()
cv2.waitKey(0)
```
11. gaussianBlur: 高斯模糊
```python
from baseImage import Image
import cv2
img = Image(data='tests/image/0.png')
new_img = img.gaussianBlur(size=(11, 11), sigma=1.5, borderType=cv2.BORDER_DEFAULT)
new_img.imshow()
cv2.waitKey(0)
```
12. warpPerspective: 透视变换
```python
from baseImage import Image, Size
import cv2
import numpy as np
img = Image(data='tests/image/0.png')
point_1 = np.float32([[0, 0], [100, 0], [0, 200], [100, 200]])
point_2 = np.float32([[0, 0], [50, 0], [0, 100], [50, 100]])
matrix = cv2.getPerspectiveTransform(point_1, point_2)
size = Size(50, 100)
new_img = img.warpPerspective(matrix, size=size)
new_img.imshow()
cv2.waitKey(0)
```
13. bitwise_not: 反转图片颜色
```python
from baseImage import Image
import cv2
img = Image(data='tests/image/0.png')
new_img = img.bitwise_not()
new_img.imshow()
cv2.waitKey(0)
```
14. imshow: 以GUI显示图片
```python
from baseImage import Image
import cv2
img = Image(data='tests/image/0.png')
img.imshow('img1')
cv2.waitKey(0)
```
15. imwrite: 将图片保存到指定路径
```python
from baseImage import Image
import cv2
img = Image(data='tests/image/0.png').cvtColor(cv2.COLOR_BGR2GRAY)
img.imwrite('tests/image/0_gray.png')
```
16. split: 拆分图像通道
- 会直接返回拆分后的数据,不是Image类型
```python
from baseImage import Image
img = Image(data='tests/image/0.png')
img_split = img.split()
```
## Extra
1. SSIM: 图片结构相似性
- resize: 图片缩放大小
```python
from baseImage import SSIM, Image
ssim = SSIM(resize=(600, 600))
img1 = Image('tests/image/0.png')
img2 = Image('tests/image/0.png')
print(ssim.ssim(im1=img1, im2=img2))
```
2. image_diff: 基于SSIM的图片差异对比
```python
from baseImage import ImageDiff, Image
import cv2
diff = ImageDiff()
img1 = Image('tests/image/0.png')
img2 = Image('tests/image/1.png')
cnts = diff.diff(img1, img2)
imageA = img1.data.copy()
imageB = img2.data.copy()
print(len(cnts))
for c in cnts:
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(imageA, (x, y), (x + w, y + h), (0, 0, 255), 2)
cv2.rectangle(imageB, (x, y), (x + w, y + h), (0, 0, 255), 2)
cv2.imshow("Original", imageA)
cv2.imshow("Modified", imageB)
cv2.waitKey(0)
```
Raw data
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"author": "hakaboom",
"author_email": "1534225986@qq.com",
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"description": "# base_image\r\n\u5bf9opencv_python\u5e38\u7528\u63a5\u53e3\u7684\u4e8c\u6b21\u5f00\u53d1\r\n\r\n\u5efa\u8bae opencv version >= 4.5.5(\u4e0d\u540copencv\u7248\u672c\u7684python\u7ed1\u5b9a,\u51fd\u6570\u540d\u53ef\u80fd\u4f1a\u4e0d\u540c)\r\n\r\n# Example\r\n\r\n## Create\r\n1. \u9ed8\u8ba4\u65b9\u5f0f\u521b\u5efa\u56fe\u7247\u5bf9\u8c61\r\n```python\r\nimport cv2\r\nfrom baseImage import Image\r\nfrom baseImage.constant import Place\r\n \r\nImage(data='tests/image/0.png') # \u4f7f\u7528\u9ed8\u8ba4\u65b9\u5f0f\u521b\u5efa\r\n```\r\n\r\n2. \u901a\u8fc7\u5176\u4ed6\u53c2\u6570,\u8c03\u6574\u56fe\u7247\u53c2\u6570\r\n\r\n- \u4f7f\u7528place\u53c2\u6570,\u4fee\u6539\u6570\u636e\u683c\u5f0f\r\n - Ndarray: \u683c\u5f0f\u4e3anumpy.ndarray\u683c\u5f0f\r\n - Umat: python\u7684\u7ed1\u5b9a\u4e0d\u591a,\u6ca1\u6709ndarray\u7075\u6d3b,\u53ef\u4ee5\u7528\u4e8eopencl\u52a0\u901f\r\n - GpuMat: opencv\u7684cuda\u683c\u5f0f,\u9700\u8981\u6ce8\u610f\u663e\u5b58\u6d88\u8017\r\n - \u53ef\u4ee5\u901a\u8fc7\u5e38\u91cf`Default_Pool`\u8bbe\u5b9a\u7f13\u51b2\u533a\r\n\r\n ```python\r\n import cv2\r\n from baseImage import Setting\r\n \r\n cv2.cuda.setBufferPoolUsage(True)\r\n cv2.cuda.setBufferPoolConfig(cv2.cuda.getDevice(), 1024 * 1024 * (3 + 3), 1)\r\n \r\n stream = cv2.cuda.Stream()\r\n pool = cv2.cuda.BufferPool(stream)\r\n \r\n Setting.Default_Stream = stream\r\n Setting.Default_Pool = pool\r\n ```\r\n\r\n```python\r\nimport cv2\r\nfrom baseImage import Image\r\nfrom baseImage.constant import Place\r\n \r\nImage(data='tests/image/0.png', place=Place.Ndarray) # \u4f7f\u7528numpy\r\nImage(data='tests/image/0.png', place=Place.UMat) # \u4f7f\u7528Umat\r\nImage(data='tests/image/0.png', place=Place.GpuMat) # \u4f7f\u7528cuda\r\n```\r\n\r\n- \u4f7f\u7528dtype,\u4fee\u6539\u6570\u636e\u7c7b\u578b\r\n```python\r\nimport cv2\r\nimport numpy as np\r\nfrom baseImage.utils.api import cvType_to_npType, npType_to_cvType\r\nfrom baseImage import Image\r\n \r\nImage(data='tests/image/0.png', dtype=np.uint8)\r\nImage(data='tests/image/0.png', dtype=np.int8)\r\nImage(data='tests/image/0.png', dtype=np.uint16)\r\nImage(data='tests/image/0.png', dtype=np.int16)\r\nImage(data='tests/image/0.png', dtype=np.int32)\r\nImage(data='tests/image/0.png', dtype=np.float32)\r\nImage(data='tests/image/0.png', dtype=np.float64)\r\n# cvType_to_npType\u548cnpType_to_cvType\u63d0\u4f9b\u4e86numpy\u8f6copencv\u6570\u636e\u683c\u5f0f\u7684\u65b9\u6cd5, cv\u7684\u6570\u636e\u683c\u5f0f\u610f\u4e49\u81ea\u884c\u767e\u5ea6\r\n```\r\n\r\n- clone,\u7528\u4e8e\u5904\u7406\u662f\u5426\u62f7\u8d1d\u539f\u6570\u636e\r\n```python\r\nimport cv2\r\nimport numpy as np\r\nfrom baseImage import Image, Rect\r\n\r\nimg1 = Image(data='tests/image/0.png')\r\nimg2 = Image(img1, clone=False)\r\nimg2.rectangle(rect=Rect(0, 0, 200, 200), color=(255, 0, 0), thickness=-1)\r\nimg2.imshow('img2')\r\nimg1.imshow('img1')\r\ncv2.waitKey(0)\r\n```\r\n\r\n## property\r\n1. shape: \u83b7\u53d6\u56fe\u7247\u7684\u957f\u3001\u5bbd\u3001\u901a\u9053\u6570\r\n```python\r\nfrom baseImage import Image\r\n\r\nimg = Image(data='tests/image/0.png')\r\nprint(img.shape)\r\n# expect output\r\n# (1037, 1920, 3)\r\n```\r\n2. size: \u83b7\u53d6\u56fe\u7247\u7684\u957f\u3001\u5bbd\r\n```python\r\nfrom baseImage import Image\r\n\r\nimg = Image(data='tests/image/0.png')\r\nprint(img.size)\r\n# expect output\r\n# (1037, 1920)\r\n```\r\n3. channels: \u83b7\u53d6\u56fe\u7247\u7684\u901a\u9053\u6570\u91cf\r\n```python\r\nfrom baseImage import Image\r\n\r\nimg = Image(data='tests/image/0.png')\r\nprint(img.channels)\r\n# expect output\r\n# 3\r\n```\r\n4. dtype: \u83b7\u53d6\u56fe\u7247\u7684\u6570\u636e\u7c7b\u578b\r\n```python\r\nfrom baseImage import Image\r\n\r\nimg = Image(data='tests/image/0.png')\r\nprint(img.dtype)\r\n# expect output\r\n# numpy.uint8\r\n```\r\n5. place: \u83b7\u53d6\u56fe\u7247\u7684\u6570\u636e\u683c\u5f0f\r\n```python\r\nfrom baseImage import Image\r\nfrom baseImage.constant import Place\r\n\r\nimg = Image(data='tests/image/0.png', place=Place.Ndarray)\r\nprint(img.place == Place.Ndarray)\r\n# expect output\r\n# True\r\n```\r\n6. data: \u83b7\u53d6\u56fe\u7247\u6570\u636e\r\n```python\r\nfrom baseImage import Image\r\n\r\nimg = Image(data='tests/image/0.png')\r\nprint(img.data)\r\n```\r\n\r\n## Function\r\n1. dtype_convert: \u6570\u636e\u7c7b\u578b\u8f6c\u6362\r\n - \u5c06\u4fee\u6539\u539f\u56fe\u50cf\u6570\u636e\r\n```python\r\nfrom baseImage import Image\r\nimport numpy as np\r\n\r\nimg = Image(data='tests/image/0.png', dtype=np.uint8)\r\nprint(img.dtype)\r\nimg.dtype_convert(dtype=np.float32)\r\nprint(img.dtype)\r\n```\r\n2. place_convert: \u6570\u636e\u683c\u5f0f\u8f6c\u6362\r\n - \u5c06\u4fee\u6539\u539f\u56fe\u50cf\u6570\u636e\r\n```python\r\nfrom baseImage import Image\r\nfrom baseImage.constant import Place\r\n\r\nimg = Image(data='tests/image/0.png', place=Place.Ndarray)\r\nprint(img.place == Place.Ndarray)\r\nimg.place_convert(place=Place.UMat)\r\nprint(img.place == Place.Ndarray)\r\nprint(img.place == Place.UMat)\r\n```\r\n\r\n3. clone: \u514b\u9686\u4e00\u4e2a\u65b0\u7684\u56fe\u7247\u5bf9\u8c61\r\n```python\r\nfrom baseImage import Image\r\nfrom baseImage.constant import Place\r\n\r\nimg = Image(data='tests/image/0.png', place=Place.Ndarray)\r\nimg2 = img.clone()\r\nprint(img == img2)\r\n```\r\n\r\n4. rotate: \u65cb\u8f6c\u56fe\u7247, \u73b0\u5728\u53ea\u652f\u6301opencv\u81ea\u5e26\u7684\u4e09\u4e2a\u65b9\u5411\r\n```python\r\nfrom baseImage import Image\r\nimport cv2\r\n\r\nimg = Image(data='tests/image/0.png')\r\nimg.rotate(code=cv2.ROTATE_180).imshow('180')\r\nimg.rotate(code=cv2.ROTATE_90_CLOCKWISE).imshow('90_CLOCKWISE')\r\nimg.rotate(code=cv2.ROTATE_90_COUNTERCLOCKWISE).imshow('90_COUNTERCLOCKWISE')\r\ncv2.waitKey(0)\r\n```\r\n\r\n5. resize: \u7f29\u653e\u56fe\u50cf\r\n```python\r\nfrom baseImage import Image\r\n\r\nimg = Image(data='tests/image/0.png')\r\nnew_img = img.resize(200, 200)\r\nprint(new_img.size)\r\n```\r\n\r\n6. cvtColor: \u8f6c\u6362\u56fe\u7247\u989c\u8272\u7a7a\u95f4\r\n```python\r\nfrom baseImage import Image\r\nimport cv2\r\n\r\nimg = Image(data='tests/image/0.png')\r\nnew_img = img.cvtColor(cv2.COLOR_BGR2GRAY)\r\nnew_img.imshow()\r\ncv2.waitKey(0)\r\n```\r\n\r\n7. crop: \u88c1\u526a\u56fe\u7247\r\n```python\r\nfrom baseImage import Image, Rect\r\nimport cv2\r\n\r\nimg = Image(data='tests/image/0.png')\r\nnew_img = img.crop(rect=Rect(0, 0, 400, 400))\r\nnew_img.imshow()\r\ncv2.waitKey(0)\r\n```\r\n\r\n8. threshold: \u4e8c\u503c\u5316\u56fe\u7247\r\n```python\r\nfrom baseImage import Image\r\nimport cv2\r\n\r\nimg = Image(data='tests/image/0.png')\r\nnew_img = img.threshold(thresh=0, maxval=255, code=cv2.THRESH_OTSU)\r\nnew_img.imshow()\r\ncv2.waitKey(0)\r\n```\r\n\r\n9. rectangle: \u5728\u56fe\u50cf\u4e0a\u753b\u51fa\u77e9\u5f62\r\n - \u4f1a\u5728\u539f\u56fe\u4e0a\u8fdb\u884c\u4fee\u6539\r\n```python\r\nfrom baseImage import Image, Rect\r\nimport cv2\r\n\r\nimg = Image(data='tests/image/0.png')\r\nimg.rectangle(rect=Rect(100, 100, 300, 300), color=(255, 0, 0), thickness=-1)\r\nimg.imshow()\r\ncv2.waitKey(0)\r\n```\r\n\r\n10. copyMakeBorder: \u6269\u5145\u56fe\u7247\u8fb9\u7f18\r\n```python\r\nfrom baseImage import Image\r\nimport cv2\r\n\r\nimg = Image(data='tests/image/0.png')\r\nnew_img = img.copyMakeBorder(top=10, bottom=10, left=10, right=10, borderType=cv2.BORDER_REPLICATE)\r\nnew_img.imshow()\r\ncv2.waitKey(0)\r\n```\r\n\r\n11. gaussianBlur: \u9ad8\u65af\u6a21\u7cca\r\n```python\r\nfrom baseImage import Image\r\nimport cv2\r\n\r\nimg = Image(data='tests/image/0.png')\r\nnew_img = img.gaussianBlur(size=(11, 11), sigma=1.5, borderType=cv2.BORDER_DEFAULT)\r\nnew_img.imshow()\r\ncv2.waitKey(0)\r\n```\r\n\r\n12. warpPerspective: \u900f\u89c6\u53d8\u6362\r\n```python\r\nfrom baseImage import Image, Size\r\nimport cv2\r\nimport numpy as np\r\n\r\nimg = Image(data='tests/image/0.png')\r\npoint_1 = np.float32([[0, 0], [100, 0], [0, 200], [100, 200]])\r\npoint_2 = np.float32([[0, 0], [50, 0], [0, 100], [50, 100]])\r\nmatrix = cv2.getPerspectiveTransform(point_1, point_2)\r\nsize = Size(50, 100)\r\n\r\nnew_img = img.warpPerspective(matrix, size=size)\r\nnew_img.imshow()\r\ncv2.waitKey(0)\r\n```\r\n13. bitwise_not: \u53cd\u8f6c\u56fe\u7247\u989c\u8272\r\n```python\r\nfrom baseImage import Image\r\nimport cv2\r\n\r\nimg = Image(data='tests/image/0.png')\r\nnew_img = img.bitwise_not()\r\nnew_img.imshow()\r\ncv2.waitKey(0)\r\n```\r\n\r\n14. imshow: \u4ee5GUI\u663e\u793a\u56fe\u7247\r\n```python\r\nfrom baseImage import Image\r\nimport cv2\r\n\r\nimg = Image(data='tests/image/0.png')\r\nimg.imshow('img1')\r\ncv2.waitKey(0)\r\n```\r\n\r\n15. imwrite: \u5c06\u56fe\u7247\u4fdd\u5b58\u5230\u6307\u5b9a\u8def\u5f84\r\n```python\r\nfrom baseImage import Image\r\nimport cv2\r\n\r\nimg = Image(data='tests/image/0.png').cvtColor(cv2.COLOR_BGR2GRAY)\r\nimg.imwrite('tests/image/0_gray.png')\r\n```\r\n\r\n16. split: \u62c6\u5206\u56fe\u50cf\u901a\u9053\r\n - \u4f1a\u76f4\u63a5\u8fd4\u56de\u62c6\u5206\u540e\u7684\u6570\u636e,\u4e0d\u662fImage\u7c7b\u578b\r\n```python\r\nfrom baseImage import Image\r\n\r\nimg = Image(data='tests/image/0.png')\r\nimg_split = img.split()\r\n```\r\n\r\n## Extra\r\n1. SSIM: \u56fe\u7247\u7ed3\u6784\u76f8\u4f3c\u6027\r\n - resize: \u56fe\u7247\u7f29\u653e\u5927\u5c0f \r\n```python\r\nfrom baseImage import SSIM, Image\r\n\r\nssim = SSIM(resize=(600, 600))\r\nimg1 = Image('tests/image/0.png')\r\nimg2 = Image('tests/image/0.png')\r\nprint(ssim.ssim(im1=img1, im2=img2))\r\n```\r\n\r\n2. image_diff: \u57fa\u4e8eSSIM\u7684\u56fe\u7247\u5dee\u5f02\u5bf9\u6bd4\r\n```python\r\nfrom baseImage import ImageDiff, Image\r\nimport cv2\r\n\r\ndiff = ImageDiff()\r\n\r\nimg1 = Image('tests/image/0.png')\r\nimg2 = Image('tests/image/1.png') \r\ncnts = diff.diff(img1, img2)\r\nimageA = img1.data.copy()\r\nimageB = img2.data.copy()\r\nprint(len(cnts))\r\nfor c in cnts:\r\n (x, y, w, h) = cv2.boundingRect(c)\r\n cv2.rectangle(imageA, (x, y), (x + w, y + h), (0, 0, 255), 2)\r\n cv2.rectangle(imageB, (x, y), (x + w, y + h), (0, 0, 255), 2)\r\n\r\ncv2.imshow(\"Original\", imageA)\r\ncv2.imshow(\"Modified\", imageB)\r\ncv2.waitKey(0)\r\n\r\n```\r\n",
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