i:\18Breakageratecalculation\mask-slic use\maskSLIC-master\maskslic\slic_superpixels.py:362: UserWarning: The following kwargs were not used by contour: 'contours' plt.contour(msk, contours=1, colors='yellow', linewidths=1) Time: 5.17 s Traceback (most recent call last): File "i:\18Breakageratecalculation\mask-slic use\maskSLIC-master\run_self", line 43, in <module> plt.imshow(mark_boundaries(img, segments)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\win10\AppData\Local\Programs\Python\Python311\Lib\site-packages\skimage\segmentation\boundaries.py", line 238, in mark_boundaries marked[boundaries] = color ~~~~~~^^^^^^^^^^^^ ValueError: shape mismatch: value array of shape (3,) could not be broadcast to indexing result of shape (5046,4)

class_labels = { 0: r'I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\result6\slic_pellucid\non-broken roofing', 1: r'I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\result6\slic_pellucid\broken roofing', 2: r'I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\result6\slic_pellucid\courtyard' }计算类被为1的像素点/（类别为+类别为的像素点）

根据您提供的代码，我可以看出这是一个Python字典类别标签和它们对应的路径。要计算类别为1的像素点占总像素点的比例，需要知道类别为1的像素点数和所有类别为1或类别为2的像素点数。代码如下： ...

i:\18Breakageratecalculation\mask-slic use\maskSLIC-master\maskslic\slic_superpixels.py:362: UserWarning: The following kwargs were not used by contour: 'contours' plt.contour(msk, contours=1, colors='yellow', linewidths=1) Time: 5.60 s Traceback (most recent call last): File "i:\18Breakageratecalculation\mask-slic use\maskSLIC-master\run_self", line 43, in <module> plt.imshow(mark_boundaries(img, segments)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\win10\AppData\Local\Programs\Python\Python311\Lib\site-packages\skimage\segmentation\boundaries.py", line 238, in mark_boundaries marked[boundaries] = color ~~~~~~^^^^^^^^^^^^ ValueError: shape mismatch: value array of shape (3,) could not be broadcast to indexing result of shape (5046,4)

这也是一个Python的错误信息，它告诉我们在slic_superpixels.py文件的第362行出现了一个用户警告。具体地说，我们提供了一个参数'contours'给plt.contour函数，但是该参数没有被使用。此外，还发生了一个值形状不...

t1 = time.time() # Note that compactness is defined differently because a grid is not used. Lower compactness for maskSLIC is equivalent segments = seg.slic(img, compactness=10, seed_type='nplace', mask=roi, n_segments=100, recompute_seeds=True, plot_examples=True, enforce_connectivity=True) print("Time: {:.2f} s".format(time.time() - t1)) for segment_id in np.unique(segments): mask = segments == segment_id masked_image = img.copy() masked_image[~mask] = 0 cv2.imwrite(r'I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\result6\segment_{}.png'.format(segment_id), masked_image) plt.figure() plt.imshow(mark_boundaries(img, segments)) plt.contour(roi, contours=1, colors='red', linewidths=0.5) plt.axis('off') plt.savefig(r'I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\result6\maskslic.png') #保存图片怎么提取超像素块的位置和大小

mask_i = segments == i x, y, w, h = cv2.boundingRect(mask_i.astype(np.uint8)) superpixel_blocks.append((x, y, w, h)) # 输出结果 print(superpixel_blocks) 请注意，以上代码仅供参考，实际使用时...

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()

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) hsv_test_image = cv2....

import os import shutil folder_path = r"I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\result6\slic_cut" # 文件夹路径 count = 1 # 文件名计数器 # 遍历文件夹中的所有文件 for filename in os.listdir(folder_path): # 获取文件扩展名 extension = os.path.splitext(filename)[1] # 构造新的文件名 new_filename = str(count).zfill(3) + extension # 拼接文件路径 src = os.path.join(folder_path, filename) dst = os.path.join(folder_path, new_filename) # 重命名文件 shutil.move(src, dst) # 更新计数器 count += 1改为从682开始命名

folder_path = r"I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\result6\slic_cut" # 文件夹路径 count = 682 # 文件名计数器，从 682 开始 # 遍历文件夹中的所有文件 for filename in os....

t1 = time.time() # Note that compactness is defined differently because a grid is not used. Lower compactness for maskSLIC is equivalent segments = seg.slic(img, compactness=10, seed_type='nplace', mask=roi, n_segments=120, recompute_seeds=True, plot_examples=True, enforce_connectivity=True) print("Time: {:.2f} s".format(time.time() - t1)) plt.figure() plt.imshow(mark_boundaries(img, segments)) plt.contour(roi, contours=1, colors='red', linewidths=0.5) plt.axis('off') plt.savefig(r'I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\split\result3\maskslic.png') 怎么保存每一块超像素图像完整代码

plt.savefig(r'I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\split\result3\maskslic.png') 请注意，此代码使用了一些未定义的变量，例如 seg。在使用此代码之前，请确保已经正确...

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...

# 遍历每个类别 for class_label in class_labels: # 获取该类别对应的文件夹名称 folder_name = class_labels[class_label] folder_path = os.path.join(r'I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\result6\slic_pellucid', folder_name) # 初始化非透明像素点数量为 0 total_non_transparent_pixels = 0 # 遍历文件夹中的所有文件 for filename in os.listdir(folder_path): # 获取文件路径 filepath = os.path.join(folder_path, filename) # 判断是否为图像文件 if filepath.lower().endswith(('.png', '.jpg', '.jpeg', '.gif')): # 计算非透明像素点数量 non_transparent_pixels = count_non_transparent_pixels(filepath) # 累加到总数中 total_non_transparent_pixels += non_transparent_pixels # 输出结果 print(f'类别 {class_label} 的非透明像素点总数：{total_non_transparent_pixels}')然后计算类别为1的像素点/（类别为1+类别为0的像素点）

folder_path = os.path.join(r'I:\18Breakageratecalculation\mask-slic use\maskSLIC-master\result\result6\slic_pellucid', folder_name) # 初始化非透明像素点数量为 0 total_non_transparent_pixels = 0 ...

Traceback (most recent call last): File "i:\18Breakageratecalculation\mask-slic use\maskSLIC-master\test.py", line 43, in <module> plt.imshow(mark_boundaries(img, segments)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\win10\AppData\Local\Programs\Python\Python311\Lib\site-packages\skimage\segmentation\boundaries.py", line 238, in mark_boundaries marked[boundaries] = color ~~~~~~^^^^^^^^^^^^ ValueError: shape mismatch: value array of shape (3,) could not be broadcast to indexing result of shape (5732,4)

这个错误是由于您的代码中传递给 mark_boundaries 函数的 color 参数的形状与 boundaries 参数不一致导致的。您可以尝试检查 color 参数的形状是否正确，并确保其与 boundaries 参数具有相同的形状。...

ModuleNotFoundError: No module named 'fast_slic'

根据提供的引用内容，出现了一个ModuleNotFoundError错误，错误信息显示找不到名为fast_slic的模块。解决这个问题的方法是手动安装该模块。可以使用以下命令来安装该模块： shell pip install fast_slic ...

img = imread(r'I:\\18Breakageratecalculation\\mask-slic use\\maskSLIC-master\\1\\056.jpg') # The ROI is also stored as an image for viewing convenience # But the roi input input maskSLIC should be a binary image with the same spatial # Dimensions as the image (in this case 300x451) roi = imread(r'I:\\18Breakageratecalculation\\mask-slic use\\maskSLIC-master\\1\\0562.png') # The alpha channel is used to store the ROI in this case and is converted into a logical array of 0s and 1s roi = roi[:, :, 3] > 0 # Alternatively a mask could be created manually with for example a disk: # roi = np.zeros((img.shape[0], img.shape[1])) # a, b = 150, 150 # r = 100 # y,x = np.ogrid[-a:img.shape[0]-a, -b:img.shape[1]-b] # mask = xx + yy <= r*r # roi[mask] = 1 # ~~~~~~ Example 1: maskSLIC ~ t1 = time.time() # Note that compactness is defined differently because a grid is not used. Lower compactness for maskSLIC is equivalent segments = seg.slic(img, compactness=10, seed_type='nplace', mask=roi, n_segments=120, recompute_seeds=True, plot_examples=True, enforce_connectivity=True) print("Time: {:.2f} s".format(time.time() - t1)) plt.figure() plt.imshow(mark_boundaries(img, segments)) plt.contour(roi, contours=1, colors='red', linewidths=0.5) plt.axis('off') # ~ Example 2: SLIC ~~~~~~~~~~~~~ t1 = time.time() segments = seg.slic(img, compactness=10, seed_type='grid', n_segments=100, plot_examples=False, enforce_connectivity=True) # segments[roi==0] = -1 print("Time: {:.2f} s".format(time.time() - t1)) plt.figure() plt.imshow(mark_boundaries(img, segments)) plt.contour(roi, contours=1, colors='red', linewidths=1) plt.axis('off') plt.show() plt.show()怎么保存结果

如果想要保存分割结果，可以使用matplotlib库中的imsave函数来保存图像。例如，可以在代码中添加以下语句来保存Example 1中的分割结果： plt.imshow(mark_boundaries(img, segments)) plt.contour(roi, ...

Traceback (most recent call last): File "i:\18Breakageratecalculation\SVM run\hog-color-svm", line 11, in <module> for i in range(num_samples = 681): ^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: range() takes no keyword arguments

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_...

from skimage.segmentation import slic from skimage.segmentation import mark_boundaries from skimage.util import img_as_float import matplotlib.pyplot as plt import numpy as np import cv2 import os # args args = {"image": 'I:\\18Breakageratecalculation\\SVM run\\images\\030.jpg'} # load the image and apply SLIC and extract (approximately) # the supplied number of segments image = cv2.imread(args["image"]) segments = slic(img_as_float(image), n_segments=100, sigma=3) # show the output of SLIC fig = plt.figure('Superpixels') ax = fig.add_subplot(1, 1, 1) ax.imshow(mark_boundaries(img_as_float(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)), segments)) plt.axis("off") plt.show() print("segments:\n", segments) print("np.unique(segments):", np.unique(segments)) # loop over the unique segment values for (i, segVal) in enumerate(np.unique(segments)): # construct a mask for the segment print("[x] inspecting segment {}, for {}".format(i, segVal)) mask = np.zeros(image.shape[:2], dtype="uint8") mask[segments == segVal] = 255 # apply the mask to the image masked_image = np.multiply(image, cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) > 0) # save the masked image as a file filename = os.path.join(r"I:\18Breakageratecalculation\SVM run\run-images2\030", "segment_%d.png" % i) cv2.imwrite(filename, masked_image) # show the masked region cv2.imshow("Mask", mask) cv2.imshow("Applied", np.multiply(image, cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) > 0)) cv2.waitKey(0)保存超像素识别结果图像

# apply the mask to the image masked_image = np.multiply(image, cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) > 0) # save the masked image as a file filename = os.path.join("segmented_images", "segment_...

import torch import torch.nn as nn import torch.nn.functional as F import torchvision.transforms as transforms import skimage.segmentation as seg import numpy as np # 超参数 from PIL import Image num_superpixels = 1000 compactness = 10 sigma = 1 # 定义模型 class SuperpixelSegmentation(nn.Module): def init(self): super(SuperpixelSegmentation, self).init() self.convs = nn.Sequential( nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(64, num_superpixels, kernel_size=1, stride=1) ) def forward(self, x): x = self.convs(x) return x # 加载图像 imgA = Image.open('1.png').convert('RGB') imgB = Image.open('2.jpg').convert('RGB') # 超像素分割 imgA_np = np.array(imgA) segments = seg.slic(imgA_np, n_segments=num_superpixels, compactness=compactness, sigma=sigma) segments = torch.from_numpy(segments).unsqueeze(0).unsqueeze(0).float() segments = F.interpolate(segments, size=(imgA.height, imgA.width), mode='nearest').long() # 应用超像素块范围到图像B imgB_np = np.array(imgB) for i in range(num_superpixels): mask = (segments == i) imgB_np[mask.expand(3, -1, -1)] = np.mean(imgB_np[mask.expand(3, -1, -1)], axis=1, keepdims=True) # 显示超像素分割图像 imgA_segments = np.zeros_like(imgA_np) for i in range(num_superpixels): mask = (segments == i) imgA_segments[mask.expand(3, -1, -1)] = np.random.randint(0, 255, size=(3,)) imgA_segments = Image.fromarray(imgA_segments.astype(np.uint8)) imgB_segments = Image.fromarray(imgB_np) # 显示图像 transforms.ToPILImage()(imgA).show() transforms.ToPILImage()(imgB).show() imgA_segments.show() imgB_segments.show()上述代码出现错误：RuntimeError: expand(CPUBoolType{[1, 1, 512, 512]}, size=[3, -1, -1]): the number of sizes provided (3) must be greater or equal to the number of dimensions in the tensor (4)

for i in range(num_superpixels): mask = (segments == i) imgB_np[:, mask] = np.mean(imgB_np[:, mask], axis=1, keepdims=True) imgB_np = np.transpose(imgB_np, (1, 2, 0)) 这里我们先对imgB_np进行...

卡通风格化魔法术技能粒子特效：Toon Projectiles 2 1.0

这款卡通射击特效资源包提供了 15 种独特的射击物、命中效果和闪光效果，风格统一且易于与您的项目集成。它默认支持 Unity 的内置渲染器，并且兼容 HDRP 和 URP 渲染管线。如果您拥有 Hovl Studio 的其他资源，该包将免费提供。所有效果均在各平台兼容，并且可以通过标准尺寸值轻松调整命中效果的大小。需要注意的是，调整射击物大小时，可能需要修改轨迹长度和按距离生成的速率。该资源还包含了一个演示场景射击脚本，方便用户快速了解如何使用这些特效。该资源包还与 InfinityPBR 的 Projectile Factory 插件兼容，可以进一步增强您的射击游戏效果。需要注意的是，推广媒体中使用的后处理效果 "Bloom" 并非资源包自带，建议用户在下载资源包之前，先行从 Unity 包管理器下载 "Post Processing Stack"。HDRP 和 URP 渲染管线的用户可以直接利用内置的 "Volume" 组件中的 "Bloom" 效果。

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ModuleNotFoundError: No module named 'fast_slic'

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