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医学图像预处理 1）搭建运行环境，运行Brain Tumor Detection代码中裁剪功能 预先安装好相关模块，安装Jupyter Notebook交互式计算环境 conda install jupyter 首先导入相关包 import tensorflow as tf from tensorflow.keras.layers import Conv2D, Input, ZeroPadding2D, BatchNormalization, Activation, MaxPooling2D, Flatten, Dense from tensorflow.keras.models import Model, load_model from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint from sklearn.model_selection import train_test_split from sklearn.metrics import f1_score from sklearn.utils import shuffle import cv2 import imutils import numpy as np import matplotlib.pyplot as plt import time from os import listdir %matplotlib inline 因为这里只是进行图形预处理，对Brain Tumor图片进行裁剪，主要用到的包是cv2, imutils, numpy, matplotlib.pyplot 然后是运行定义裁剪脑轮廓(查找大脑的顶部、底部、左端、右端的极点)的函数（crop_brain_contour） def crop_brain_contour(image, plot=False): #import imutils #import cv2 #from matplotlib import pyplot as plt # Convert the image to grayscale, and blur it slightly gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray = cv2.GaussianBlur(gray, (5, 5), 0) # Threshold the image, then perform a series of erosions + # dilations to remove any small regions of noise thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1] thresh = cv2.erode(thresh, None, iterations=2) thresh = cv2.dilate(thresh, None, iterations=2) # Find contours in thresholded image, then grab the largest one cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = imutils.grab_contours(cnts) c = max(cnts, key=cv2.contourArea) # Find the extreme points extLeft = tuple(c[c[:, :, 0].argmin()][0]) extRight = tuple(c[c[:, :, 0].argmax()][0]) extTop = tuple(c[c[:, :, 1].argmin()][0]) extBot = tuple(c[c[:, :, 1].argmax()][0]) # crop new image out of the original image using the four extreme points (left, right, top, bottom) new_image = image[extTop[1]:extBot[1], extLeft[0]:extRight[0]] if plot: plt.figure() plt.subplot(1, 2, 1) plt.imshow(image) plt.tick_params(axis='both', which='both', top=False, bottom=False, left=False, right=False, labelbottom=False, labeltop=False, labelleft=False, labelright=False) plt.title('Original Image') plt.subplot(1, 2, 2) plt.imshow(new_image) plt.tick_params(axis='both', which='both', top=False, bottom=False, left=False, right=False, labelbottom=False, labeltop=False, labelleft=False, labelright=False) plt.title('Cropped Image') plt.show() return new_image 下面我简单说一下我对这个函数的理解（当然函数中已有原作者的解释）： ...

1.jpg 2.jpg 1.对1.jpg 分别作灰度化, 模糊化处理. 代码如下 """ @Project: imageProcessing @Author: loyio @Date: 3/20/21 """ from PIL import Image, ImageFilter if __name__ == '__main__': # greyscale imgfileOne = "Sample/1" sample_img = Image.open(imgfileOne+".jpg").convert('L') sample_img.save(imgfileOne+"_processed_gray.jpg") # Blur # sample_img = Image.open(imgfileOne+".jpg").filter(ImageFilter.BLUR) sample_img = Image.open(imgfileOne+".jpg").filter(ImageFilter.BoxBlur(5)) sample_img.save(imgfileOne + "_processed_blur.jpg") 图片效果如下 灰度处理 模糊处理 2.对2.jpg生成手绘效果 代码如下 """ @Project: imageProcessing @Author: loyio @Date: 3/20/21 """ from PIL import Image, ImageFilter import numpy as np if __name__ == '__main__': # Paint imgfileTwo = "Sample/2" sample_img_ary = np.asarray(Image.open(imgfileTwo+".jpg").convert('L')).astype('float') depth = 10. grad_x, grad_y = np.gradient(sample_img_ary) grad_x = grad_x * depth / 100. grad_y = grad_y * depth / 100. A = np.sqrt(grad_x ** 2 + grad_y ** 2 + 1.) uni_x = grad_x / A uni_y = grad_y / A uni_z = 1. / A vec_el = np.pi / 2.2 vec_az = np.pi / 4. dx = np.cos(vec_el) * np.cos(vec_az) dy = np.cos(vec_el) * np.sin(vec_az) dz = np.sin(vec_el) sample_processed_ary = (255 * (dx*uni_x + dy*uni_y + dz*uni_z)).clip(0, 255) im = Image.fromarray(sample_processed_ary.astype('uint8')) im.save(imgfileTwo+"_processed_handpaint.jpg") 首先将图片灰度化，然后转换为float类型，存放在numpy array中 通过np.gradient求灰度图像的梯度（即灰度的变化率），将其赋值给grad_x, grad_y ，并根据深度级别计算新的梯度，同时归一化。将其控制在（0，1） 为x,y轴梯度构建三维归一化单位坐标系。 建立光源效果，可以将np.cos(vec_el)分析为单位射线在地平面上的投影长度。 dx，dy和dz是光源在x / y / z方向上的影响。 梯度与光源相互作用，最终将梯度转换为灰度。即sample_processed_ary = 255 * (dx*uni_x + dy*uni_y + dz*uni_z) 为避免越过边界，最后还要调用函数.clip(0,255) 图片效果如下 ...