讲解一下这段代码： def extract_img(location, img, contour=None): x, y, w, h = location # 只提取轮廓内的字符 if contour is None: extracted_img = img[y:y + h, x:x + w] else: mask = np.zeros(img.shape, np.uint8) cv2.drawContours(mask, [contour], -1, 255, cv2.FILLED) img_after_masked = cv2.bitwise_and(mask, img) extracted_img = img_after_masked[y:y + h, x:x + w] # 将提取出的img归一化成IMG_SIZE*IMG_SIZE大小的二值图 black = np.zeros((IMG_SIZE, IMG_SIZE), np.uint8) if (w > h): res = cv2.resize(extracted_img, (IMG_SIZE, (int)(h * IMG_SIZE / w)), interpolation=cv2.INTER_AREA) d = int(abs(res.shape[0] - res.shape[1]) / 2) black[d:res.shape[0] + d, 0:res.shape[1]] = res else: res = cv2.resize(extracted_img, ((int)(w * IMG_SIZE / h), IMG_SIZE), interpolation=cv2.INTER_AREA) d = int(abs(res.shape[0] - res.shape[1]) / 2) black[0:res.shape[0], d:res.shape[1] + d] = res extracted_img = skeletonize(black) extracted_img = np.logical_not(extracted_img) return extracted_img

三维立体图形生成工具：surf mesh contour演示

文件标题“m.rar_contour c_surf mesh”暗示了该资源主要涉及的是一系列用于生成和分析特定三维图形的技术和方法，特别是关于轮廓(contour)、曲面(surf)和网格(mesh)的创建与分析。在三维建模和可视化领域中，轮廓...

2D轮廓提取工具箱：Active Contour方法基础

资源摘要信息:"Active Contour Toolbox是一个专门为MATLAB环境设计的工具箱，专注于实现和研究2D活动轮廓模型（也称为蛇模型或SNAKES算法）。活动轮廓模型是一种用于图像分割的计算机视觉技术，尤其在生物医学图像...

这段代码是在做什么？ def binary_img_segment(binary_img, original_img=None): # binary_img = skeletonize(binary_img) # plot.imshow( binary_img,cmap = 'gray', interpolation = 'bicubic') # plot.show() # 寻找每一个字符的轮廓，使用cv2.RETR_EXTERNAL模式，表示只需要每一个字符最外面的轮廓 img, contours, hierarchy = cv2.findContours(binary_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # cv2.RETR_TREE # cv2.drawContours(img_original, contours, -1, (0, 255, 0), 2) if len(contours) > LARGEST_NUMBER_OF_SYMBOLS: raise ValueError('symtem cannot interpret this image!') symbol_segment_location = [] # 将每一个联通体，作为一个字符 symbol_segment_list = [] index = 1 for contour in contours: location = cv2.boundingRect(contour) x, y, w, h = location if (w * h < 100): continue symbol_segment_location.append(location) # 只提取轮廓内的字符 extracted_img = extract_img(location, img, contour) symbol_segment_list.append(extracted_img) if len(original_img): cv2.rectangle(original_img, (x, y), (x + w, y + h), (0, 0, 255), 3) symbols = [] for i in range(len(symbol_segment_location)): symbols.append({'location': symbol_segment_location[i], 'src_img': symbol_segment_list[i]}) # 对字符按字符横坐标排序 symbols.sort(key=lambda x: x['location'][0]) return symbols

这段代码是一个用于二值图像分割的函数。它的输入是一个二值图像(binary_img)和一个可选的原始图像(original_img)。函数的目标是找到图像中每个字符的轮廓，并将它们作为单独的字符进行分割。首先，使用cv2....

if name == "main": img_name = "img/cap.png" img = cv2.imread(img_name) gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转灰度图 bin_img = get_binary_img(gray_img) # 二值化 # print(bin_img[370][360]) #需要注意的是图像转为数组的时候 x，y是反过来的 # 边界追踪 flag1 = 0 find, start_i, start_j = get_left_up_start_pt(flag1, bin_img) # cv.circle(bin_img, (start_i, start_j), 3, (0, 0, 0), -1) # print("start_x, start_y = ", start_i, start_j) # cv.imshow("1", bin_img) contour_img = np.zeros(shape=bin_img.shape, dtype=np.uint8) contour_img += 255 flag2, contour_img = trace_contour(bin_img, find, start_i, start_j, contour_img) if flag2 == 1: find1, start_i1, start_j1 = get_left_up_start_pt(flag2, bin_img) f, contour_img = trace_contour(bin_img, find1 + 1, start_i1, start_j1, contour_img) contour_img[0] = 255 contour_img[len(contour_img) - 1] = 255 for w in range(len(contour_img)): contour_img[w][0] = 255 contour_img[w][len(contour_img[0]) - 1] = 255 cv2.imshow("img", contour_img) cv2.waitKey() cv2.destroyAllWindows()

这段代码完成了对一张图像进行边界追踪并显示结果的过程。具体实现流程如下： 1. 设置图像文件名img_name，读取图像并将其转换为灰度图像gray_img。 2. 对灰度图像进行二值化处理，得到二值化图像bin_img。 3...

def compute_shape_features(images): contour_areas = [] contour_perimeters = [] aspect_ratios = [] for img in images: contours, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) contour = max(contours, key=cv2.contourArea) area = cv2.contourArea(contour) perimeter = cv2.arcLength(contour, True) x, y, w, h = cv2.boundingRect(contour) aspect_ratio = w / h if h != 0 else 0 contour_areas.append(area) contour_perimeters.append(perimeter) aspect_ratios.append(aspect_ratio) return np.array(contour_areas), np.array(contour_perimeters), np.array(aspect_ratios)的公牛

这段代码是用来计算一组图像的形状特征的。它通过使用OpenCV库的函数来找出每个图像的轮廓(contours)，并计算轮廓的面积(contour_area)、周长(contour_perimeter)和宽高比(aspect_ratio)等特征。最后将这些特征存储...

# min_x = # max_x = # min_y = # max_y =补全

在这段代码中，未完成的部分涉及到对活动物体位置的计算。min_x, max_x, min_y, 和 max_y 可能是用来确定活动物体的边界框尺寸。这部分代码应该在处理每个轮廓时更新这些变量，就像这样： python for ...

for c in contours: #找到最大的轮廓边界所包含的面积 area = cv2.contourArea(c) if area > max_area: max_contour = c max_area = area #矩形边框绘制

cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2) 这段代码的作用是在图像中找到最大的轮廓，并绘制一个矩形边框框住它。首先，我们遍历所有轮廓并计算它们的面积。如果某个轮廓的面积比当前最大面积还要大，我们...

coding=UTF-8 from flask import Flask, render_template, request, send_from_directory from werkzeug.utils import secure_filename from iconflow.model.colorizer import ReferenceBasedColorizer from skimage.feature import canny as get_canny_feature from torchvision import transforms from PIL import Image import os import datetime import torchvision import cv2 import numpy as np import torch import einops transform_Normalize = torchvision.transforms.Compose([ transforms.Normalize(0.5, 1.0)]) ALLOWED_EXTENSIONS = set([‘png’, ‘jpg’, ‘jpeg’]) app = Flask(name) train_model = ReferenceBasedColorizer() basepath = os.path.join( os.path.dirname(file), ‘images’) # 当前文件所在路径 def allowed_file(filename): return ‘.’ in filename and filename.rsplit(‘.’, 1)[1] in ALLOWED_EXTENSIONS def load_model(log_path=‘/mnt/4T/lzq/IconFlowPaper/checkpoints/normal_model.pt’): global train_model state = torch.load(log_path) train_model.load_state_dict(state[‘net’]) @app.route(“/”, methods=[“GET”, “POST”]) def hello(): if request.method == ‘GET’: return render_template(‘upload.html’) @app.route(‘/upload’, methods=[“GET”, “POST”]) def upload_lnk(): if request.method == ‘GET’: return render_template(‘upload.html’) if request.method == ‘POST’: try: file = request.files['uploadimg'] except Exception: return None if file and allowed_file(file.filename): format = "%Y-%m-%dT%H:%M:%S" now = datetime.datetime.utcnow().strftime(format) filename = now + '_' + file.filename filename = secure_filename(filename) basepath = os.path.join( os.path.dirname(file), ‘images’) # 当前文件所在路径 # upload_path = os.path.join(basepath,secure_filename(f.filename)) file.save(os.path.join(basepath, filename)) else: filename = None return filename @app.route(‘/download/string:filename’, methods=[‘GET’]) def download(filename): if request.method == “GET”: if os.path.isfile(os.path.join(basepath, filename)): return send_from_directory(basepath, filename, as_attachment=True) pass def get_contour(img): x = np.array(img) canny = 0 for layer in np.rollaxis(x, -1): canny |= get_canny_feature(layer, 0) canny = canny.astype(np.uint8) * 255 kernel = np.array([ [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], ], dtype=np.uint8) canny = cv2.dilate(canny, kernel) # canny = Image.fromarray(canny) return canny @app.route(‘/embedding//’, methods=[“GET”, “POST”]) def icontran(img, reference): global train_model if request.method == ‘POST’: imgPath = os.path.join(basepath, img) referencePath = os.path.join(basepath, reference) img = cv2.imread(imgPath) if img is None or img.size <= 0: return None contour = get_contour(img).astype(np.float32).copy() contour = 255 - contour reference = cv2.imread(referencePath).astype(np.float32) reference = cv2.cvtColor(reference, cv2.COLOR_BGR2RGB) reference = transform_Normalize(torch.from_numpy(reference).permute(2, 0, 1).unsqueeze(0).float()/ 255.0) contour = transform_Normalize(torch.from_numpy(contour).unsqueeze(0).unsqueeze(0).float()/ 255.0) train_model.eval() transfer = train_model(contour, reference) transfer = transfer.squeeze(0) transfer = (transfer + 0.5).clamp(0, 1).mul_(255).permute(1, 2, 0).type(torch.uint8).numpy() transfer = transfer.numpy() cv2.imwrite(imgPath, transfer) return basepath # success if name == “main”: load_model() app.run(host=‘10.21.16.144’, port=9999, debug=True) 用puthon写一个调用这个服务器的gui

这是一段 Python 代码，它使用 Flask 框架来创建一个网站应用。它还导入了一些需要使用的库，如 werkzeug、numpy、torch 等。该代码可以对图片进行一些处理，如调整亮度、色彩等。其中包括一个可以将黑白图片变成...

FMesh:注意：使用Fltk_Contour插入此代码！-开源

注意：使用Fltk_Contour插入此代码！ FMesh是基于OpenGL的简单科学数据可视化工具。 FMesh可以为任何（x，y，z）分散的数据集生成轮廓图和颜色图的2D和3D图形，其中包括许多其他选项。

# Iterate through each contour and extract the rectangle bounding the digit for contour in contours: (x, y, w, h) = cv2.boundingRect(contour) roi = thresholded[y:y + h, x:x + w] roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)这段代码什么意思

接着，使用cv2.boundingRect函数计算数字轮廓的最小矩形边界框的左上角坐标(x, y)和宽度(w)、高度(h)。然后，使用这些坐标和尺寸来提取数字区域(roi)。最后，将数字区域调整为28x28大小，并使用cv2.INTER_AREA插值...

补全代码 sigma = 1; x = -2:0.01:2; y = -2:0.01:1.5; [X, Y] = meshgrid(x, y); %X = X; %Y = Y; P_E = X + sqrt(3) * Y + sigma/2 * (X.^2+Y.^2-1).^2; figure subplot(121) contour(X, Y, P_E, 80) sigma = 8; x = -2:0.01:2; y = -2:0.01:1.5; [X, Y] = meshgrid(x

这段代码是用MATLAB实现的，其中包含两个子图，分别对应着不同的惩罚系数sigma。首先，我们在一个二维网格上计算出函数P_E的值，然后通过contour函数绘制出等高线图。其中，sigma=1和sigma=8分别对应着左右两张子图...

for contour in contours: area = cv2.contourArea(contour) if(area>10): print("area:",area) x,y,w,h = cv2.boundingRect(contour) rect = (x,y,w,h) train_chars = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2) roi = img[y:y + h, x:x + w] plt.imshow(train_chars) plt.imshow(roi) resize_roi = cv2.resize(roi,(20,30)) roi_float = resize_roi.astype(np.float32) #plt.show(resize_roi) train_data = np.append(train_data, roi_float.reshape(1, -1), axis=0) print("reshape roi",roi_float.reshape(1, -1)) train_data怎么初始化

在代码中没有看到 train_data 的初始化过程，但可以根据代码推断出其应该是一个 numpy 数组，用于存储处理后的字符图像数据。因此，可以在代码开始处添加如下初始化语句： train_data = np.empty((0, 20*30),...

import numpy as np import matplotlib.pyplot as plt # 二元二次函数 def f(x,y): return x**2+10*y**2import numpy as np import matplotlib.pyplot as plt # 梯度函数 def grad_f(x,y): return np.array([2x,20y]) x0 = np.array([10,1]) alphs_k = 0.085 iter_num = 15 x_list = [x0] for i in range(iter_num): x_k = x_list[-1] x_next = x_k - alphs_k*grad_f(x_k[0],x_k[1]) x_list.append(x_next) # 画图 delta = 0.05 x = np.arange(-15.0,15.0,delta) y = np.arange(-5.0,5.0,delta) X,Y = meshgrid(x,y) Z = f(X,Y) fig,ax = plt.subplots() cs = ax.contour(X,Y,Z) ax.calbel(cs,inline=1,fontsize=10) x_list = np.array(x_list) ax.plot(x_list[:,0],x_list[:,1],'-0') plt.show()

这段代码实现了梯度下降法求解二元二次函数的最小值，并将迭代过程可视化。具体来说，代码中定义了二元二次函数 $f(x,y) = x^2 + 10y^2$ 和梯度函数 $\nabla f(x,y) = [2x, 20y]$。然后从初始点 $[10,1]$ 开始进行...

def get_postion(image): img = cv2.imread(image) blurred = cv2.GaussianBlur(image,(5,5),0,0) canny = cv2.Canny(blurred,0,100) contours,hierarchy=cv2.findContours(canny,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) print(len(contours)) for contour in contours: x,y,w,h = cv2.boundingRect(contour) area = cv2.contourArea(contour) loop = cv2.arcLength(contour,True) if 152<loop<162 and 1444<area<1849: x,y,w,h = cv2.boundingRect(contour) cv2.rectangle(image,(x,y),(x+w,y+h),(0,0,255),2) cv2.imwrite("111.jpg",image) return x return 0帮我优化一下代码

x,y,w,h = cv2.boundingRect(contour) cv2.rectangle(image, (x,y), (x+w,y+h), (0,0,255), 2) cv2.imwrite("111.jpg", image) return x return 0 You can adjust the values of min_loop, max_loop, ...

def get_postion(image): img = cv2.imread(image) blurred = cv2.GaussianBlur(image,(5,5),0,0) canny = cv2.Canny(blurred,0,100) contours,hierarchy=cv2.findContours(canny,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) print(len(contours)) for contour in contours: x,y,w,h = cv2.boundingRect(contour) area = cv2.contourArea(contour) loop = cv2.arcLength(contour,True) if 152<loop<162 and 1444<area<1849: x,y,w,h = cv2.boundingRect(contour) cv2.rectangle(image,(x,y),(x+w,y+h),(0,0,255),2) cv2.imwrite("111.jpg",image) return x return 0帮我优化一下代码用中文

x,y,w,h = cv2.boundingRect(contour) area = cv2.contourArea(contour) loop = cv2.arcLength(contour,True) if 152<loop<162 and 1444<area<1849: x,y,w,h = cv2.boundingRect(contour) cv2.rectangle(image...

def get_postion(image): img = cv2.imread(image) blurred = cv2.GaussianBlur(image,(5,5),0,0) canny = cv2.Canny(blurred,0,100) contours,hierarchy=cv2.findContours(canny,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) print(len(contours)) for contour in contours: x,y,w,h = cv2.boundingRect(contour) area = cv2.contourArea(contour) loop = cv2.arcLength(contour,True) if 152<loop<162 and 1444<area<1849: x,y,w,h = cv2.boundingRect(contour) cv2.rectangle(image,(x,y),(x+w,y+h),(0,0,255),2) cv2.imwrite("111.jpg",image) return x return 0识别滑动验证码的验证区域帮我优化一下源代码

x, y, w, h = cv2.boundingRect(contour) # 使用滑动窗口搜索验证码的位置 for i in range(x, x + w): for j in range(y, y + h): if thresh[j, i] == 255: return i return 0 您可以根据您的实际需求和...

import cv2 import glob import numpy as np imgs = glob.glob("maze.png") res, L, N = [], 256, 5 for i in imgs: img = cv2.imread(i) img = cv2.resize(img, (512, 512)) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) _, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV) # contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) max_contour = max(contours, key=cv2.contourArea) epsilon = 0.1 * cv2.arcLength(max_contour, True) approx = cv2.approxPolyDP(max_contour, epsilon, True) circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param1=50, param2=15, minRadius=5, maxRadius=15) if circles is not None: circles = np.round(circles[0, :]).astype("int") for (x, y, r) in circles: cv2.circle(img, (x, y), r, (0, 0, 255), 2) # edges = cv2.Canny(gray, 100, 200) contours, hierarchy = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for contour in contours: center, (width, height), angle = cv2.minAreaRect(contour) if -5 <= (width - height) <= 5 and 30 <= width <= 50: cv2.drawContours(img, [contour], -1, (0, 0, 255), 3) res.append(cv2.resize(img, (L, L))) resImg = np.zeros((L * N, L * N, 3), dtype=np.uint8) for i, img in enumerate(res): row, col = i // N, i % N x, y = col * L, row * L resImg[y:y + L, x:x + L] = img cv2.imshow("", resImg) cv2.waitKey(0) cv2.destroyAllWindows()

这是一段Python代码，主要的作用是对给定的maze.png图片进行处理并展示处理后的结果。具体包括以下步骤： 1. 导入cv2、glob和numpy模块。 2. 使用glob模块获取maze.png图片的路径，将其读取并调整大小为512x512。 3...

def Process(img): # 高斯平滑 gaussian = cv2.GaussianBlur(img, (3, 3), 0, 0, cv2.BORDER_DEFAULT) # 中值滤波 median = cv2.medianBlur(gaussian, 5) # Sobel算子 # 梯度方向: x sobel = cv2.Sobel(median, cv2.CV_8U, 1, 0, ksize=3) # 二值化 ret, binary = cv2.threshold(sobel, 170, 255, cv2.THRESH_BINARY) # 核函数 element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 1)) element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 7)) # 膨胀 dilation = cv2.dilate(binary, element2, iterations=1) # 腐蚀 erosion = cv2.erode(dilation, element1, iterations=1) # 膨胀 dilation2 = cv2.dilate(erosion, element2, iterations=3) return dilation2 def GetRegion(img): regions = [] # 查找轮廓 contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) for contour in contours: area = cv2.contourArea(contour) if (area < 7500): continue eps = 1e-3 * cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, eps, True) rect = cv2.minAreaRect(contour) box = cv2.boxPoints(rect) box = np.int0(box) height = abs(box[0][1] - box[2][1]) width = abs(box[0][0] - box[2][0]) ratio =float(width) / float(height) if (ratio < 6 and ratio > 1.8): regions.append(box) return regions def detect(img): # 灰度化 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) prc = Process(gray) regions = GetRegion(prc) print('[INFO]:Detect %d license plates' % len(regions)) for box in regions: cv2.drawContours(img, [box], 0, (0, 0,255), 2) cv2.imwrite(r'C:\Users\gzy\Pictures\Saved Pictures\xiaoguotu.png', img) cv2.waitKey(0) cv2.destroyAllWindows()请简单描述一下该代码是如何实现车牌检测功能的

这段代码使用了一系列图像处理技术实现了车牌检测功能。主要流程如下： 1. 对输入的彩色图像进行灰度化处理。 2. 对灰度图像进行高斯平滑和中值滤波处理，以降低噪声干扰。 3. 使用 Sobel 算子提取图像的边缘信息。...

import cv2 import numpy as np import tensorflow as tf # 加载之前训练好的模型 model = tf.keras.models.load_model('mnist_cnn_model') for img in images_data: # 将RGB格式转换为BGR格式 img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) # 转换为灰度图像 gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY) # 二值化处理 _, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) # 找到轮廓 contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 初始化计数器 count = 0 # 遍历所有轮廓 for contour in contours: # 计算轮廓面积 area = cv2.contourArea(contour) if area < 200 or area > 2000: # 如果轮廓面积小于10个像素，则忽略该轮廓 continue # 获取轮廓的外接矩形 x, y, w, h = cv2.boundingRect(contour) # 在原始图像上标记出抠出来的数字部分，并将BGR格式转换为RGB格式 cv2.rectangle(img_bgr, (x, y), (x+w, y+h), (0, 255, 0), 2) digit = cv2.cvtColor(img_bgr[y:y+h, x:x+w], cv2.COLOR_BGR2RGB) # 对数字图像进行预处理，使其与训练数据具有相同的格式 digit_resized = cv2.resize(digit, (28, 28)) digit_gray = cv2.cvtColor(digit_resized, cv2.COLOR_RGB2GRAY) digit_normalized = digit_gray / 255.0 digit_reshaped = np.reshape(digit_normalized, (1, 28, 28)) # 进行预测并输出最大概率对应的数字 prediction = model.predict(digit_reshaped) digit_class = np.argmax(prediction) print("抠出来的数字是:", digit_class) # 增加计数器 count += 1 # 在原始图像上显示标记过的抠出来的数字部分 #plt.figure(figsize=(20, 20)) #plt.imshow(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)) #plt.show() # 显示抠出来的数字个数 print("抠出来了{}个数字".format(count)) 如何解决error Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_12928/408549256.py in <module> 8 for img in images_data: 9 # 将RGB格式转换为BGR格式 ---> 10 img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) 11 12 # 转换为灰度图像 error: OpenCV(4.7.0) :-1: error: (-5:Bad argument) in function 'cvtColor' > Overload resolution failed: > - src is not a numpy array, neither a scalar > - Expected Ptrcv::UMat for argument 'src'这个错误

检查一下images_data中的图片格式是否正确。images_data中的图片应该是numpy数组格式，如果不是，你需要将其转换为numpy数组格式才能进行后续的操作。你可以使用np.array()函数将图片数据转换为numpy数组格式...

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