B,G,R=cv2.split(im)

dirname = 'C:/Users/hp/segmenteverygrain-main/segmenteverygrain/Segmentation/images/' # write grayscale mask to PNG file cv2.imwrite(dirname + fname.split('/')[-1][:-4] + '_mask.png', mask_all) # Define a colormap using matplotlib num_classes = len(all_grains) cmap = plt.get_cmap('viridis', num_classes) # Map each class label to a unique color using the colormap vis_mask = cmap(labels.astype(np.uint16))[:,:,:3] * 255 vis_mask = vis_mask.astype(np.uint8) # Save the mask as a PNG file cv2.imwrite(dirname + fname.split('/')[-1][:-4] + '_labels.png', vis_mask) # Save the image as a PNG file cv2.imwrite(dirname + fname.split('/')[-1][:-4] + '_image.png', cv2.cvtColor(big_im, cv2.COLOR_BGR2RGB))

cv2.imwrite(dirname + fname.split('/')[-1][:-4] + '_image.png', cv2.cvtColor(big_im, cv2.COLOR_BGR2RGB)) 这段代码首先将灰度掩码 mask_all 保存为 PNG 文件，命名方式为原始文件名去除扩展名后加上 '_...

from pdb import set_trace as st import os import numpy as np import cv2 import argparse parser = argparse.ArgumentParser('create image pairs') parser.add_argument('--fold_A', dest='fold_A', help='input directory for image A', type=str, default='./dataset/blurred') parser.add_argument('--fold_B', dest='fold_B', help='input directory for image B', type=str, default='./dataset/sharp') parser.add_argument('--fold_AB', dest='fold_AB', help='output directory', type=str, default='../dataset/out') parser.add_argument('--num_imgs', dest='num_imgs', help='number of images',type=int, default=1000000) parser.add_argument('--use_AB', dest='use_AB', help='if true: (0001_A, 0001_B) to (0001_AB)',action='store_true') args = parser.parse_args() for arg in vars(args): print('[%s] = ' % arg, getattr(args, arg)) splits = os.listdir(args.fold_A) for sp in splits: img_fold_A = os.path.join(args.fold_A, sp) img_fold_B = os.path.join(args.fold_B, sp) img_list = os.listdir(img_fold_A) if args.use_AB: img_list = [img_path for img_path in img_list if '_A.' in img_path] num_imgs = min(args.num_imgs, len(img_list)) print('split = %s, use %d/%d images' % (sp, num_imgs, len(img_list))) img_fold_AB = os.path.join(args.fold_AB, sp) if not os.path.isdir(img_fold_AB): os.makedirs(img_fold_AB) print('split = %s, number of images = %d' % (sp, num_imgs)) for n in range(num_imgs): name_A = img_list[n] path_A = os.path.join(img_fold_A, name_A) if args.use_AB: name_B = name_A.replace('_A.', '_B.') else: name_B = name_A path_B = os.path.join(img_fold_B, name_B) if os.path.isfile(path_A) and os.path.isfile(path_B): name_AB = name_A if args.use_AB: name_AB = name_AB.replace('_A.', '.') # remove _A path_AB = os.path.join(img_fold_AB, name_AB) im_A = cv2.imread(path_A, cv2.IMREAD_COLOR) im_B = cv2.imread(path_B, cv2.IMREAD_COLOR) im_AB = np.concatenate([im_A, im_B], 1) cv2.imwrite(path_AB, im_AB),运行上述代码，提示错误：NotADirectoryError: [WinError 267] 目录名称无效。: 'D:\Users\Administrator\PycharmProjects\pythonProject\DeblurGAN-master\datasets\blurred\1.jpg'

根据错误提示，看起来是指定的目录名称无效。具体来说，是出现了一个名为'1.jpg'的文件，而在代码中尝试将其作为目录来处理。你需要检查代码中是否有将文件名误认为是目录名的情况，可以使用os.path.isdir()函数来...

下面这段代码在做什么？ def resize_norm_img(img, max_wh_ratio, rec_image_shape='3,48,320'): rec_image_shape = [int(v) for v in rec_image_shape.split(",")] imgC, imgH, imgW = rec_image_shape assert imgC == img.shape[2] max_wh_ratio = max(max_wh_ratio, imgW / imgH) imgW = int((imgH * max_wh_ratio)) imgW = max(min(imgW, 1280), 16) h, w = img.shape[:2] ratio = w / float(h) ratio_imgH = math.ceil(imgH * ratio) ratio_imgH = max(ratio_imgH, 16) if ratio_imgH > imgW: resized_w = imgW else: resized_w = int(ratio_imgH) resized_image = cv2.resize(img, (resized_w, imgH)) resized_image = resized_image.astype('float32') resized_image = resized_image.transpose((2, 0, 1)) / 255 resized_image -= 0.5 resized_image /= 0.5 padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) padding_im[:, :, 0:resized_w] = resized_image return padding_im

这段代码是用来调整图像大小和归一化图像像素值的。函数的输入参数包括原始图片（img）、图像高宽比（max_wh_ratio）、...最后，函数对调整后的图像进行归一化、转置和填充操作，并返回修改后的图像（padding_im）。

import cv2 import numpy as np import PIL.ImageDraw SCALE = 0.65156853729882650681169151675877 # m/px def add_chinese_text(img, text, position, textColor, textSize): img = PIL.Image.fromarray(img) draw = PIL.ImageDraw.Draw(img) fontStyle = PIL.ImageFont.truetype('simsun.ttc', textSize, encoding='utf-8') draw.text(position, text, textColor, font=fontStyle, stroke_width=1) return np.asarray(img) def main(): # 读取verts with open('verts.txt', 'r', encoding='utf8') as f: verts = f.readlines() verts = list(map(lambda x: x.split(), verts)) verts = list(map(lambda x: [x[0], int(x[1]), int(x[2])], verts)) print(f'{verts=}') # 读取edges with open('edges.txt', 'r', encoding='utf8') as f: edges = f.readlines() edges = list(map(lambda x: x.split(), edges)) edges = list(map(lambda x: [int(x[0]), int(x[1])], edges)) print(f'{edges=}') # 显示地图 im = cv2.imread('map.png') for edge in edges: v1 = verts[edge[0]] # 边关联的点1 v2 = verts[edge[1]] # 边关联的点2 weight = int((((v1[1] - v2[1]) 2 + (v1[2] - v2[2]) 2) ** 0.5) * SCALE) # 权重为两点欧氏距离 cv2.line(im, (v1[1], v1[2]), (v2[1], v2[2]), (255, 0, 0), 5) # 绘制边的直线 im = add_chinese_text(im, str(weight), ((v1[1] + v2[1]) // 2, (v1[2] + v2[2]) // 2), (255, 0, 255), 30) # 绘制边的权重 for i, vert in enumerate(verts): cv2.circle(im, (vert[1], vert[2]), 20, (255, 0, 0), 2) # 绘制顶点圆圈 im = add_chinese_text(im, str(i), (vert[1] - 15, vert[2] - 15), (255, 0, 255), 30) # 绘制顶点圈中序号数字 im = add_chinese_text(im, vert[0], (vert[1] - 40, vert[2] + 25), (255, 0, 255), 30) # 绘制顶点下方文字 cv2.namedWindow('map', cv2.WINDOW_KEEPRATIO) cv2.imshow('map', im) cv2.waitKey() if name == 'main': main()

1. cv2：这是一个用于计算机视觉的库，可以用来处理图像和视频数据。 2. numpy：这是一个用于科学计算的库，主要用于处理数组和矩阵的运算。 3. PIL.ImageDraw：这是 Python Imaging Library (PIL) 中的一个模块，...

代码解释# Process detections for i, det in enumerate(pred): # detections per image if webcam: # batch_size >= 1 p, s, im0 = path[i], '%g: ' % i, im0s[i].copy() else: p, s, im0 = path, '', im0s save_path = str(Path(out) / Path(p).name) s += '%gx%g ' % img.shape[2:] # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh if det is not None and len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # Print results for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class s += '%g %ss, ' % (n, names[int(c)]) # add to string # Write results for xyxy, conf, cls in det: if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file: file.write(('%g ' 5 + '\n') % (cls, xywh)) # label format if save_img or view_img: # Add bbox to image label = '%s %.2f' % (names[int(cls)], conf) if label is not None: if (label.split())[0] == 'person': people_coords.append(xyxy) # plot_one_box(xyxy, im0, line_thickness=3) plot_dots_on_people(xyxy, im0) # Plot lines connecting people distancing(people_coords, im0, dist_thres_lim=(100, 150)) # Print time (inference + NMS) print('%sDone. (%.3fs)' % (s, t2 - t1)) # Stream results if 1: ui.showimg(im0) if cv2.waitKey(1) == ord('q'): # q to quit raise StopIteration # Save results (image with detections) if save_img: if dataset.mode == 'images': cv2.imwrite(save_path, im0) else: if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() # release previous video writer fps = vid_cap.get(cv2.CAP_PROP_FPS) w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(opt.fourcc), fps, (w, h)) vid_writer.write(im0)

2. 对每个类别的检测结果统计数量，并将数量和类别名称添加到输出字符串中。 3. 对每个检测到的目标绘制边界框，并在边界框上标注类别和置信度。 4. 如果检测到的目标是人，则将其坐标保存在列表中，并在图像上...

from sklearn import model_selection from sklearn import neural_network from sklearn import datasets from sklearn.model_selection import train_test_split import cv2 from fractions import Fraction import numpy import scipy from sklearn.neural_network import MLPClassifier from sklearn.neural_network import MLPRegressor from sklearn import preprocessing import imageio reg = MLPRegressor(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1) def image_to_data(image): im_resized = scipy.misc.imresize(image, (8, 8)) im_gray = cv2.cvtColor(imresized, cv2.COLOR_BGR2GRAY) im_hex = Fraction(16,255) * im_gray im_reverse = 16 - im_hex return imreverse.astype(numpy.int) def data_split(Data): x_train, x_test, y_train, y_test = train_test_split(Data.data, Data.target) return x_train, x_test, y_train, y_test def data_train(x_train, x_test, y_train, y_test): clf = neural_network.MLPClassifier() clf.fit(x_train, y_train) return clf def image_predict(image_path, clf): image = scipy.misc.imread(image_path) image_data = image_to_data(image) image_data_reshaped = image_data.reshape(1, 64) predict_result = clf.predict(image_data_reshaped) print("手写体数字识别结果为:",predict_result,'\n') if name=='main': print("若要退出，请按q退出!"'\n') str_get = input("请输入识别的手写数字序号:" +'\n') while str_get != 'q': print("识别第{}个手写数字：".format(str_get)+'\n') image_path = r"C: // Users // 33212 // Desktop // "+str_get+".png" Data = datasets.load_digits() x_train, x_test, y_train, y_test = data_split(Data) clf = data_train(x_train, x_test, y_train, y_test) image_predict(image_path, clf) str_get = input("请输入识别的手写数字序号:" +'\n')

这段代码似乎是用来进行手写数字识别的，其中使用了sklearn库中的MLPClassifier和MLPRegressor来进行分类和回归，同时使用了datasets和model_selection等模块来进行数据处理和模型选择，还有一些图像处理相关的函数...

基于天空分割得到天空区域与非天空区域的透射率，再叠加为一个总的透射率，再由四叉树算法估计大气光，从而通过暗通道先验算法去雾。请给出能够正常运行的python代码和中文注解

b, g, r = cv2.split(im) dc = cv2.min(cv2.min(r, g), b) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (sz,sz)) dark = cv2.erode(dc, kernel) return dark def atm_light(im, dark): """ 估算...

暗通道去雾算法python

b, g, r = cv2.split(img) dc = cv2.min(cv2.min(r, g), b) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (size, size)) dark = cv2.erode(dc, kernel) return dark def atm_light(img, dark): [h, w...

DeHaze的opencv算法实现

b, g, r = cv2.split(img) dc = cv2.min(cv2.min(r, g), b) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (size, size)) dark = cv2.erode(dc, kernel) return dark dark = dark_channel(img, 15) ...

a)请对Lena彩色图（lena_color_256.tif）转为YUV420格式，并存储为lena_256x256.yuv，获得其文件大小（bytes）。 b)请读入lena_256x256.yuv文件，并显示其原图、亮度图、色度图（Cb,Cr）于同一窗口。 c)请分别对b)中的结果进行DCT8x8变换,并使用上述QY和QC量化矩阵进行相应量化。量化完要取整 Qr=（16 11 10 16 24 40 51 61 12 12 14 19 26 58 60 55 14 13 16 24 40 57 69 56 18 22 37 56 68 109 103 77 24 35 55 64 81 104 113 92 49 64 78 87 103 121 120 101 72 92 95 98 112 100 103 99 ） Qc=(17 18 24 47 99 99 99 99 18 21 26 66 99 99 99 99 24 26 56 99 99 99 99 99 47 66 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99 99) d)请使用Zig-Zag扫描法对c)中的结果进行扫描，并使用游程编码(RLC)方法对其进行编码。 e)请使用哈夫曼编码方式对d)中的结果进行熵编码，并存为lena_256x256.cmp文件，获得其文件大小。 f)比较lena_256x256.yuv与lena_256x256.cmp文件大小，计算压缩率。

imf[i:i+8, j:j+8] = np.round(cv2.dct(im[i:i+8, j:j+8])/512) return imf # 量化操作 def quantize(im, qf): n, m = im.shape imq = np.zeros((n, m)) for i in range(0, n, 8): for j in range(0, m, 8): ...

使用pytharm完成无人机航拍图像去雾选择什么算法以及使用该算法完成去雾的相关程序

b, g, r = cv2.split(img) dc = cv2.min(cv2.min(r, g), b) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (size, size)) dark = cv2.erode(dc, kernel) return dark def atm_light(img, dark): [h, w...

def add_mask2(image, mask, color_id): # 单通道 int_mas

gray_bgr = cv2.cvtColor(gray_image, cv2.COLOR_GRAY2BGR) # 应用掩码 result_with_mask = cv2.addWeighted(gray_bgr, 1, color_mask, 0.7, 0) 上述代码片段展示了如何创建一个全绿的掩码并将之应用于原始...

yolov8分割数据增强

hue, sat, val = cv2.split(cv2.cvtColor(bgr_img, cv2.COLOR_BGR2HSV)) x = np.arange(0, 256, dtype=np.int16) lut_hue = ((x * r[0]) % 180).astype('uint8') lut_sat = np.clip(x * r[1], 0, 255).astype('...

计算机视觉傅里叶变换

### 计算机视觉中傅里叶变换的使用方法在计算机视觉领域，傅里叶变换用于将空间域上的图像转换到频域表示。这种转换能够帮助分析和处理图像的不同频率成分。 #### Numpy 实现傅里叶变换通过Numpy库可以方便地...

基于暗通道先验去雨Python算法

cov_IP=cv2.boxFilter(I*p,cv2.CV_64F,ksize=(r,r))-mean_I*cv2.boxFilter(p,cv2.CV_64F,ksize=(r,r)) a=cov_IP/(var_I+eps) b=cv2.boxFilter(p,cv2.CV_64F,ksize=(r,r))-a*mean_I mean_a=cv2.boxFilter(a,cv2...

M = np.array([[1,0,0], [0,2,0], [0,0,2]]) iM = inv(M) xy = np.zeros((640,640,2),dtype=np.float32) for py in range(640): for px in range(640): xy[py,px] = np.array([px,py],dtype=np.float32) ixy=cv2.perspectiveTransform(xy,iM) mpx,mpy = cv2.split(ixy) mapx,mapy=cv2.convertMaps(mpx,mpy,cv2.CV_16SC2)

相关推荐

B,G,R=cv2.split(im)

M = np.array([[1,0,0], [0,2,0], [0,0,2]]) iM = inv(M) xy = np.zeros((640,640,2),dtype=np.float32) for py in range(640): for px in range(640): xy[py,px] = np.array([px,py],dtype=np.float32) ixy=cv2.perspectiveTransform(xy,iM) mpx,mpy = cv2.split(ixy) mapx,mapy=cv2.convertMaps(mpx,mpy,cv2.CV_16SC2)

相关推荐

Python OpenCV之图片缩放的实现（cv2.resize）

python中plt.imshow与cv2.imshow显示颜色问题

解决python cv2.imread 读取中文路径的图片返回为None的问题

基于天空分割得到天空区域与非天空区域的透射率，再叠加为一个总的透射率，再由四叉树算法估计大气光，从而通过暗通道先验算法去雾。请给出能够正常运行的python代码和中文注解

暗通道去雾算法python

DeHaze的opencv算法实现

使用pytharm完成无人机航拍图像去雾选择什么算法以及使用该算法完成去雾的相关程序

def add_mask2(image, mask, color_id): # 单通道 int_mas

yolov8分割数据增强

计算机视觉傅里叶变换

基于暗通道先验去雨Python算法

大家在看

zlg的Python应用

UART.rar_2407 串口_F2407_TMS320LF2407_uart c语言

cam350导出smt坐标

TA-Lib的whl文件

本科-OOAD-原婷婷-2015212109-188013989281

最新推荐

基于CNN-GRU-Attention混合神经网络的负荷预测方法 附Python代码.rar

,,Induction-Motor-VF-Control：基于MATLAB Simulink的利用V F控制的感应电机调速仿真模型 仿真条件：MATLAB Simulink R2015b ,核心关键

Windows下操作Linux图形界面的VNC工具

【SketchUp Ruby API：从入门到精通】

VMware虚拟机打开虚拟网络编辑器出现由于找不到vnetlib.dll,无法继续执行代码。重新安装程序可能会解决问题

基于Preact的高性能PWA实现定期天气信息更新

从停机到上线，EMC VNX5100控制器SP更换的实战演练

ubuntu labelme中文版安装

全新免费HTML5商业网站模板发布

EMC VNX5100控制器SP更换全流程指南：新手到高手的必备技能

基于CNN-GRU-Attention混合神经网络的负荷预测方法附Python代码.rar

,,Induction-Motor-VF-Control：基于MATLAB Simulink的利用V F控制的感应电机调速仿真模型仿真条件：MATLAB Simulink R2015b ,核心关键