解释代码：C = 1 / sum(sum(m_wei)) hist1 = np.zeros(16 ** 3) for i in range(0, a): for j in range(0, b): q_b = math.floor(float(temp[i, j, 0]) / 16) q_g = math.floor(float(temp[i, j, 1]) / 16) q_r = math.floor(float(temp[i, j, 2]) / 16) q_temp1 = q_r * 0.299 + q_g * 0.587 + q_b * 0.114 hist1[int(q_temp1)] = hist1[int(q_temp1)] + m_wei[i, j] hist1 = hist1 * C

数据库管理系统.zip_C#_C#数据库增_hist1r_数据库_管理系统

这个压缩包“数据库管理系统.zip”中可能包含了源代码文件、数据库设计文档、数据库脚本甚至测试数据。通过分析和学习这些内容，开发者可以深入理解如何使用C#进行数据库操作，以及如何构建一个完整的数据库管理系统...

hist_eq.zip_adi dsp_bf5_hist eq_直方图汇编

在本文中，我们将深入探讨与"hist_eq.zip_adi dsp_bf5_hist eq_直方图汇编"相关的技术细节，特别是针对ADI公司的BF535 DSP（数字信号处理器）进行图像直方图均衡化的汇编优化源码。直方图均衡化是一种常用的图像...

import pandas as pd import numpy as np import matplotlib.pyplot as plt import math data = np.load('dataset_em.npy') mu0 = np.array([np.mean(data) - 1, np.mean(data) + 1]) sg0 = np.array([1.0, 1.0]) w0 = [0.5, 0.5] ary_mu = [mu0] ary_sg = [sg0] ary_w = [w0] x = np.arange(-10, 15, 0.1) def pdf_gauss(x, mu, sg): c = 1 / np.sqrt(2 * np.pi) p = c / sg * np.exp(-0.5 * ((x - mu) / sg) ** 2) return p def gauss_mix_pdf(w, x, mu, sg): p = w[0] * pdf_gauss(x, mu[0], sg[0]) + w[1] * pdf_gauss(x, mu[1], sg[1]) return p for j in range(0, 100): mu = ary_mu[j] sg = ary_sg[j] w = ary_w[j] gama = [] for i in (0, 1): b = w[i] * pdf_gauss(data, mu[i], sg[i]) c = gauss_mix_pdf(w, data, mu, sg) gama.append(b / c) gama_sum0 = np.sum(gama[0]) gama_sum1 = np.sum(gama[1]) new_mu = [np.sum(data * gama[0]) / gama_sum0, np.sum(data * gama[1]) / gama_sum1] new_sg = [np.sqrt(np.sum(gama[0] * np.power(data - new_mu[0], 2)) / gama_sum0), np.sqrt(np.sum(gama[1] * np.power(data - new_mu[1], 2)) / gama_sum1)] # 第二个数太大了 new_w = [gama_sum0 / data.shape[0], gama_sum1 / data.shape[0]] ary_mu.extend([new_mu]) ary_sg.extend([new_sg]) ary_w.extend([new_w]) del gama y = gauss_mix_pdf(ary_w[100], x, ary_mu[100], ary_sg[100]) plt.figure() plt.hist(data, bins=100, facecolor="blue", edgecolor="black", alpha=0.7, density=True) plt.plot(x, y) plt.show() 请逐句详细解释这段代码

new_mu = [np.sum(data * gama[0]) / gama_sum0, np.sum(data * gama[1]) / gama_sum1] new_sg = [np.sqrt(np.sum(gama[0] * np.power(data - new_mu[0], 2)) / gama_sum0), np.sqrt(np.sum(gama[1] * np.power...

def get_map(Hist): # 计算概率分布Pr sum_Hist = sum(Hist) Pr = Hist / sum_Hist # 计算累计概率Sk Sk = [] temp_sum = 0 for n in Pr: temp_sum = temp_sum + n Sk.append(temp_sum) Sk = np.array(Sk) # 计算映射关系img_map img_map = [] for m in range(256): temp_map = int(255 * Sk[m] + 0.5) img_map.append(temp_map) img_map = np.array(img_map) return img_map

3. 计算累计概率：计算概率分布Pr的累计概率Sk，即Sk[i] = sum(Pr[0:i+1])，其中i的范围为0到255； 4. 计算映射关系：将累计概率Sk映射到0到255的整数范围内，得到一个映射关系img_map，其中img_map[i]表示灰度级别...

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

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...

解释代码import numpy as np import matplotlib.pyplot as plt # plt 用于显示图片 import matplotlib.image as mpimg # mpimg 用于读取图片 fig = plt.figure() #matplotlib只支持PNG图像 lena = mpimg.imread('cat.jpg') lena_r=np.zeros(lena.shape) #0通道 lena_r[:,:,0]=lena[:,:,0] ax1=fig.add_subplot(331) ax1.imshow(lena_r)# 显示R通道 lena_g=np.zeros(lena.shape)#1通道 lena_g[:,:,1]=lena[:,:,1] ax4=fig.add_subplot(334) ax4.imshow(lena_g)# 显示G通道 lena_b=np.zeros(lena.shape)#2通道 lena_b[:,:,2]=lena[:,:,2] ax7=fig.add_subplot(337) ax7.imshow(lena_b)# 显示B通道 img_R = lena_r[:,:,0] R_mean=np.mean(img_R) R_std=np.std(img_R) ax2=fig.add_subplot(332) flatten_r=img_R.flatten() weights = np.ones_like(flatten_r)/float(len(flatten_r)) prob_r,bins_r,_=ax2.hist(flatten_r,bins=10,facecolor='r',weights=weights) img_G = lena_g[:,:,1] G_mean=np.mean(img_G) G_std=np.std(img_G) ax5=fig.add_subplot(335) flatten_g=img_G.flatten() prob_g,bins_g,_=ax5.hist(flatten_g,bins=10,facecolor='g',weights=weights) img_B = lena_b[:,:,2] B_mean=np.mean(img_B) B_std=np.std(img_B) ax8=fig.add_subplot(338) flatten_b=img_B.flatten() prob_b,bins_b,_=ax8.hist(flatten_b,bins=10,facecolor='b',weights=weights) ax3=fig.add_subplot(233) rgb_mean=[R_mean,G_mean,B_mean] x_mlabel=['R_mean','G_mean','B_mean'] bar_width=0.5 bars_mean=ax3.bar(x_mlabel,rgb_mean,width=bar_width) colors=['r','g','b'] for bar,color in zip(bars_mean,colors): bar.set_color(color) ax3.set_title('Mean') ax9 = fig.add_subplot(236) rgb_std =[R_std,G_std,B_std] x_mlabel = ['R_std','G_std','B_std'] bar_width = 0.5 bars_std = ax9.bar(x_mlabel,rgb_std,width = bar_width) colors = ['r','g','b'] for bar,color in zip(bars_std,colors): bar.set_color(color) ax9.set_title('Std') # fig.set_tight_layout(True) plt.show()

这段代码主要是读取一张名为"cat.jpg"的图片，并对其RGB三个通道进行分析和统计。首先，将原图的RGB三个通道分别提取出来，然后在左上角、左中和左下角用subplot展示三个通道的图像。接着，分别计算每个通道的...

解释这一段代码def auto_whiteBalance(img): b, g, r = cv2.split(img) Y = 0.299 * r + 0.587 * g + 0.114 * b Cr = 0.5 * r - 0.419 * g - 0.081 * b Cb = -0.169 * r - 0.331 * g + 0.5 * b Mr = np.mean(Cr) Mb = np.mean(Cb) Dr = np.var(Cr) Db = np.var(Cb) temp_arry = (np.abs(Cb - (Mb + Db * np.sign(Mb))) < 1.5 * Db) & ( np.abs(Cr - (1.5 * Mr + Dr * np.sign(Mr))) < 1.5 * Dr) RL = Y * temp_arry # 选取候选白点数的最亮10%确定为最终白点，并选择其前10%中的最小亮度值 # L_list = list(np.reshape(RL, (RL.shape[0] * RL.shape[1],)).astype(np.int)) L_list = list(np.reshape(RL, (RL.shape[0] * RL.shape[1],)).astype(int)) hist_list = np.zeros(256) min_val = 0 sum = 0 for val in L_list: hist_list[val] += 1 for l_val in range(255, 0, -1): sum += hist_list[l_val] if sum >= len(L_list) * 0.1: min_val = l_val break # 取最亮的前10%为最终的白点 white_index = RL < min_val RL[white_index] = 0 # 计算选取为白点的每个通道的增益 b[white_index] = 0 g[white_index] = 0 r[white_index] = 0 Y_max = np.max(RL) b_gain = Y_max / (np.sum(b) / np.sum(b > 0)) g_gain = Y_max / (np.sum(g) / np.sum(g > 0)) r_gain = Y_max / (np.sum(r) / np.sum(r > 0)) b, g, r = cv2.split(img) b = b * b_gain g = g * g_gain r = r * r_gain # 溢出处理 b[b > 255] = 255 g[g > 255] = 255 r[r > 255] = 255 res_img = cv2.merge((b, g, r)) return res_img

hist_list = np.zeros(256) min_val = 0 sum = 0 for val in L_list: hist_list[val] += 1 for l_val in range(255, 0, -1): sum += hist_list[l_val] if sum >= len(L_list) * 0.1: min_val = l_val break ...

import argparse import logging import re from multiprocessing import Process, Queue from pathlib import Path import numpy as np from skimage import exposure, filters from modules.config import logger from modules.volume import volume_loading_func, volume_saving_func def normalize_intensity( np_volume: np.ndarray, relative_path: Path, logger: logging.Logger ): logger.info(f"[processing start] {relative_path}") nstack = len(np_volume) stack: np.ndarray = np_volume[nstack // 2 - 16 : nstack // 2 + 16] hist_y, hist_x = exposure.histogram(stack[stack > 0]) thr = filters.threshold_otsu(stack[stack > 0]) peak_air = np.argmax(hist_y[hist_x < thr]) + hist_x[0] peak_soil = np.argmax(hist_y[hist_x > thr]) + (thr - hist_x[0]) + hist_x[0] np_volume = np_volume.astype(np.int64) for i in range(len(np_volume)): np_volume[i] = ( (np_volume[i] - peak_air).clip(0) / (peak_soil - peak_air) * 256 / 2 ) logger.info(f"[processing end] {relative_path}") return exposure.rescale_intensity( np_volume, in_range=(0, 255), out_range=(0, 255) ).astype(np.uint8) 请详细解释每一行的代码意思

这是一段 Python 代码，它进行了体积数据的预处理，具体解释如下： import argparse # 用来解析命令行参数 import logging # 用来记录日志 import re # 用来进行正则表达式的匹配 from multiprocessing import ...

def auto_whiteBalance(img): b, g, r = cv2.split(img) Y = 0.299 * r + 0.587 * g + 0.114 * b Cr = 0.5 * r - 0.419 * g - 0.081 * b Cb = -0.169 * r - 0.331 * g + 0.5 * b Mr = np.mean(Cr) Mb = np.mean(Cb) Dr = np.var(Cr) Db = np.var(Cb) temp_arry = (np.abs(Cb - (Mb + Db * np.sign(Mb))) < 1.5 * Db) & ( np.abs(Cr - (1.5 * Mr + Dr * np.sign(Mr))) < 1.5 * Dr) RL = Y * temp_arry # 选取候选白点数的最亮10%确定为最终白点，并选择其前10%中的最小亮度值 # L_list = list(np.reshape(RL, (RL.shape[0] * RL.shape[1],)).astype(np.int)) L_list = list(np.reshape(RL, (RL.shape[0] * RL.shape[1],)).astype(int)) hist_list = np.zeros(256) min_val = 0 sum = 0 for val in L_list: hist_list[val] += 1 for l_val in range(255, 0, -1): sum += hist_list[l_val] if sum >= len(L_list) * 0.1: min_val = l_val break # 取最亮的前10%为最终的白点 white_index = RL < min_val RL[white_index] = 0 # 计算选取为白点的每个通道的增益 b[white_index] = 0 g[white_index] = 0 r[white_index] = 0 Y_max = np.max(RL) b_gain = Y_max / (np.sum(b) / np.sum(b > 0)) g_gain = Y_max / (np.sum(g) / np.sum(g > 0)) r_gain = Y_max / (np.sum(r) / np.sum(r > 0)) b, g, r = cv2.split(img) b = b * b_gain g = g * g_gain r = r * r_gain # 溢出处理 b[b > 255] = 255 g[g > 255] = 255 r[r > 255] = 255 res_img = cv2.merge((b, g, r)) return res_img

感谢提供代码。根据您的代码，问题可能出现在加载图像数据的部分。请确保您正确地加载了图像数据，并且可以通过cv2.imshow()函数显示图像。如果您遇到了无法加载图像数据的问题，请尝试检查图像路径是否正确，并...

test_mean_max = np.max(test_mean) test_mean_min = np.min(test_mean) bins = 10 fig = plt.figure(figsize=(8, 6)) ax = plt.subplot(111) ax.hist(test_mean, bins=bins, range=(test_mean_min, test_mean_max), density=True, rwidth=0.9) mu, sigma = test_mean.mean(), test_mean.std() x = np.linspace(test_mean_min, test_mean_max, 100000) y = 1 / (sigmanp.sqrt(2np.pi)) * np.exp(- (x-mu)**2 / (2*sigma**2)) ax.plot(x, y) plt.show()为什么已经归一化，y轴的值有大于1的值

在这段代码中，density=True参数会将直方图的值归一化为概率密度，即每个箱子内的计数除以箱子宽度和所有计数的总和。因此，直方图的y轴表示的是概率密度，不是计数。但是，代码中添加的曲线是正态分布的概率密度...

详细分析一下python代码:import torch.optim as optim criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.01, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10, verbose=True, min_lr=0) loss_hist, acc_hist = [], [] loss_hist_val, acc_hist_val = [], [] for epoch in range(140): running_loss = 0.0 correct = 0 for data in train_loader: batch, labels = data batch, labels = batch.to(device), labels.to(device) optimizer.zero_grad() outputs = net(batch) loss = criterion(outputs, labels) loss.backward() optimizer.step() # compute training statistics _, predicted = torch.max(outputs, 1) correct += (predicted == labels).sum().item() running_loss += loss.item() avg_loss = running_loss / len(train_set) avg_acc = correct / len(train_set) loss_hist.append(avg_loss) acc_hist.append(avg_acc) # validation statistics net.eval() with torch.no_grad(): loss_val = 0.0 correct_val = 0 for data in val_loader: batch, labels = data batch, labels = batch.to(device), labels.to(device) outputs = net(batch) loss = criterion(outputs, labels) _, predicted = torch.max(outputs, 1) correct_val += (predicted == labels).sum().item() loss_val += loss.item() avg_loss_val = loss_val / len(val_set) avg_acc_val = correct_val / len(val_set) loss_hist_val.append(avg_loss_val) acc_hist_val.append(avg_acc_val) net.train() scheduler.step(avg_loss_val) print('[epoch %d] loss: %.5f accuracy: %.4f val loss: %.5f val accuracy: %.4f' % (epoch + 1, avg_loss, avg_acc, avg_loss_val, avg_acc_val))

这段代码是一个基于PyTorch的神经网络训练过程。代码中使用了torch.optim模块中Adam优化器和ReduceLROnPlateau学习率调度器。其中，Adam优化器用于优化网络的参数，而ReduceLROnPlateau调度器用于自动调整学习率以...

import cv2 import numpy as np import os # 提取图像的HOG特征 def get_hog_features(image): hog = cv2.HOGDescriptor() hog_features = hog.compute(image) return hog_features # 加载训练数据集 train_data = [r"I:\18Breakageratecalculation\SVM run\detection_cut\whole\train128"] train_labels = [r"I:\18Breakageratecalculation\SVM run\detection_cut\whole\train128\labels.txt"] num_samples = 681 for i in range(num_samples): img = cv2.imread(str(i).zfill(3)+'.jpg') hog_features = get_hog_features(image) hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) color_hist = cv2.calcHist([hsv_image], [0, 1], None, [180, 256], [0, 180, 0, 256]) color_features = cv2.normalize(color_hist, color_hist).flatten() train_data.append(hog_features) train_labels.append(labels[i]) # 训练SVM模型 svm = cv2.ml.SVM_create() svm.setType(cv2.ml.SVM_C_SVC) svm.setKernel(cv2.ml.SVM_LINEAR) svm.train(np.array(train_data), cv2.ml.ROW_SAMPLE, np.array(train_labels)) # 对测试图像进行分类 test_image = cv2.imread('I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\split\result2\maskslic2_roi.png', 0) test_features = get_hog_features(test_image) result = svm.predict(test_features.reshape(1,-1)) # 显示分割结果 result_image = np.zeros(test_image.shape, np.uint8) for i in range(test_image.shape[0]): for j in range(test_image.shape[1]): if result[i,j] == 1: result_image[i,j] = 255 cv2.imshow('I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\split\result2\Result.png', result_image) cv2.waitKey(0) cv2.destroyAllWindows()

这段代码似乎存在一些问题。以下是一些可能需要修改的地方： 1. 在加载训练数据时，train_data和train_labels应该是列表类型，而不是字符串类型。您可以使用os.listdir()函数来获取文件夹中的所有图像文件名...

import cv2 import numpy as np # 提取图像的HOG特征 def get_hog_features(image): hog = cv2.HOGDescriptor() hog_features = hog.compute(image) return hog_features # 加载训练数据集 train_data = [r"I:\18Breakageratecalculation\SVM run\detection_cut\whole\train128"] train_labels = [r"I:\18Breakageratecalculation\SVM run\detection_cut\whole\train128\labels.txt"] for i in range(num_samples): image = cv2.imread('image_'+str(i)+'.jpg', 0) hog_features = get_hog_features(image) hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) color_hist = cv2.calcHist([hsv_image], [0, 1], None, [180, 256], [0, 180, 0, 256]) color_features = cv2.normalize(color_hist, color_hist).flatten() train_data.append(hog_features) train_labels.append(labels[i]) # 训练SVM模型 svm = cv2.ml.SVM_create() svm.setType(cv2.ml.SVM_C_SVC) svm.setKernel(cv2.ml.SVM_LINEAR) svm.train(np.array(train_data), cv2.ml.ROW_SAMPLE, np.array(train_labels)) # 对测试图像进行分类 test_image = cv2.imread('I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\split\result2\maskslic2_roi.png', 0) test_features = get_hog_features(test_image) result = svm.predict(test_features.reshape(1,-1)) # 显示分割结果 result_image = np.zeros(test_image.shape, np.uint8) for i in range(test_image.shape[0]): for j in range(test_image.shape[1]): if result[i,j] == 1: result_image[i,j] = 255 cv2.imshow('I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\split\result2\Result.png', result_image) cv2.waitKey(0) cv2.destroyAllWindows()

这段代码中也没有定义num_samples这个变量，所以在执行for i in range(num_samples):这个循环时，会报错NameError: name 'num_samples' is not defined。你需要在代码中定义并赋值num_samples这个变量，例如...

帮我补全下面的代码，绘制laplace和gauss的柱状分布图并保存epsilon = 100 vals_laplace = [np.random.laplace(loc=0, scale=1/epsilon) for x in range(100000)] delta = 1e-5 sigma = np.sqrt(2 * np.log(1.25 / delta)) * 1 / epsilon vals_gauss = [np.random.normal(loc=0, scale=sigma) for x in range(100000)]

代码如下： python import matplotlib.pyplot as plt fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5)) # 绘制Laplace分布的柱状图 ax1.hist(vals_laplace, bins=50, color='blue', alpha=0.5) ax1.set_...

# 创建转换映射 transfer_map = np.zeros((180, 256), dtype=np.uint8) for src_bin in range(180): source_prob = source_hist[src_bin] cumulative_prob = source_prob target_bin = 0 while cumulative_prob < target_hist[src_bin] and target_bin < 255: target_prob = target_hist[target_bin] cumulative_prob += target_prob target_bin += 1 transfer_map[src_bin] = target_bin

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