import numpy as np import cv2 def ComputeMinLevel(hist, pnum): index = np.add.accumulate(hist) return np.argwhere(index>pnum * 8.3 * 0.01)[0][0] def ComputeMaxLevel(hist, pnum): hist_0 = hist[::-1] Iter_sum = np.add.accumulate(hist_0) index = np.argwhere(Iter_sum > (pnum * 2.2 * 0.01))[0][0] return 255-index def LinearMap(minlevel, maxlevel): if (minlevel >= maxlevel): return [] else: index = np.array(list(range(256))) screenNum = np.where(index<minlevel,0,index) screenNum = np.where(screenNum> maxlevel,255,screenNum) for i in range(len(screenNum)): if screenNum[i]> 0 and screenNum[i] < 255: screenNum[i] = (i - minlevel) / (maxlevel - minlevel) * 255 return screenNum def CreateNewImg(img): h, w, d = img.shape newimg = np.zeros([h, w, d]) for i in range(d): imghist = np.bincount(img[:, :, i].reshape(1, -1)[0]) minlevel = ComputeMinLevel(imghist, h * w) maxlevel = ComputeMaxLevel(imghist, h * w) screenNum = LinearMap(minlevel, maxlevel) if (screenNum.size == 0): continue for j in range(h): newimg[j, :, i] = screenNum[img[j, :, i]] return newimg if __name__ == '__main__': img = cv2.imread('D:\shujuji\wu\\Image_20230225221250865.jpg') newimg = CreateNewImg(img) cv2.imshow('original_img', img) cv2.imshow('new_img', newimg / 200) cv2.waitKey(0) cv2.destroyAllWindows()修改调整图片大小
时间: 2024-01-23 15:02:32 浏览: 23
抱歉,根据您提供的代码,我无法直接回答您的问题。该代码是用Python和OpenCV库编写的图像处理程序,用于调整图像的亮度和对比度。如果您需要调整图片大小,可以使用OpenCV的resize()函数。例如,可以使用以下代码将图像大小调整为宽度为400像素,高度按比例缩放:
```python
resized_img = cv2.resize(img, (400, int(img.shape[0]*400/img.shape[1])))
```
其中,img是原始图像,resized_img是调整大小后的图像。您可以将其放置在CreateNewImg()函数后面,以获得调整大小后的图像。
相关问题
解释以下这段代码import numpy as np def convection_model(C, rho, u): dCdt = -np.dot(np.gradient(rhouC)) return dCdt
这段代码定义了一个名为"convection_model"的函数,它接受三个参数:C,rho和u。其中,C和rho是矩阵,u是向量。函数内部用到了numpy库中的np.dot()和np.gradient()函数。
函数的主要作用是计算对流模型中的C浓度随时间的变化率。具体来说,它根据C和rho计算出rhouC(即rho * u * C),并利用np.gradient()函数计算出rhouC的梯度。最后,通过np.dot()函数将梯度和负号相乘,得到dCdt(即C浓度随时间的变化率),并将其作为函数的输出返回。
import numpy as np def frame2mat(frameBuffer): """将帧信息转换为numpy的矩阵对象,后续可以通过opencv的cvtColor转换为特定的图像格式""" frame, buffer = frameBuffer bits = np.uint8 if (frame.bits == Bits.BITS_8) else np.uint16 shape = None if frame.format >= ImageFormat.FORMAT_MONO and frame.format <= ImageFormat.FORMAT_BAYER_RG: shape = 1 elif frame.format == ImageFormat.FORMAT_BGR24 or frame.format == ImageFormat.FORMAT_RGB24: shape = 3 elif frame.format == ImageFormat.FORMAT_BGR32 or frame.format == ImageFormat.FORMAT_RGB32: shape = 4 else: return None mat = np.ndarray(shape=(frame.iHeight, frame.iWidth, shape), dtype=bits, buffer=buffer) return mat帮我优化
可以对该函数进行以下优化:
1. 将np.uint8和np.uint16定义为常量,避免在函数调用时重复创建。
2. 通过字典存储格式信息和对应的shape,避免使用大量的if-else语句。
3. 将np.ndarray的shape参数修改为元组类型,避免在函数调用时创建新的元组对象。
修改后的代码如下:
```python
import numpy as np
UINT8 = np.uint8
UINT16 = np.uint16
SHAPE_DICT = {
ImageFormat.FORMAT_MONO: (1,),
ImageFormat.FORMAT_YUV422: (1,),
ImageFormat.FORMAT_BGR24: (3,),
ImageFormat.FORMAT_RGB24: (3,),
ImageFormat.FORMAT_BGR32: (4,),
ImageFormat.FORMAT_RGB32: (4,),
ImageFormat.FORMAT_BAYER_RG: (1,)
}
def frame2mat(frameBuffer):
"""将帧信息转换为numpy的矩阵对象,后续可以通过opencv的cvtColor转换为特定的图像格式"""
frame, buffer = frameBuffer
bits = UINT8 if frame.bits == Bits.BITS_8 else UINT16
shape = SHAPE_DICT.get(frame.format)
if shape is None:
return None
mat = np.ndarray(shape=(frame.iHeight, frame.iWidth, *shape), dtype=bits, buffer=buffer)
return mat
```