import numpy as np import cv2 class ColorMeter(object): color_hsv = { # HSV，H表示色调（度数表示0-180），S表示饱和度（取值0-255），V表示亮度（取值0-255） # "orange": [np.array([11, 115, 70]), np.array([25, 255, 245])], "yellow": [np.array([11, 115, 70]), np.array([34, 255, 245])], "green": [np.array([35, 115, 70]), np.array([77, 255, 245])], "lightblue": [np.array([78, 115, 70]), np.array([99, 255, 245])], "blue": [np.array([100, 115, 70]), np.array([124, 255, 245])], "purple": [np.array([125, 115, 70]), np.array([155, 255, 245])], "red": [np.array([156, 115, 70]), np.array([179, 255, 245])], } def __init__(self, is_show=False): self.is_show = is_show self.img_shape = None def detect_color(self, frame): self.img_shape = frame.shape res = {} # 将图像转化为HSV格式 hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) for text, range_ in self.color_hsv.items(): # 去除颜色范围外的其余颜色 mask = cv2.inRange(hsv, range_[0], range_[1]) erosion = cv2.erode(mask, np.ones((1, 1), np.uint8), iterations=2) dilation = cv2.dilate(erosion, np.ones((1, 1), np.uint8), iterations=2) target = cv2.bitwise_and(frame, frame, mask=dilation) # 将滤波后的图像变成二值图像放在binary中 ret, binary = cv2.threshold(dilation, 127, 255, cv2.THRESH_BINARY) # 在binary中发现轮廓，轮廓按照面积从小到大排列 contours, hierarchy = cv2.findContours( binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE ) if len(contours) > 0: # cv2.boundingRect()返回轮廓矩阵的坐标值，四个值为x, y, w, h，其中x, y为左上角坐标，w,h为矩阵的宽和高 boxes = [ box for box in [cv2.boundingRect(c) for c in contours] if min(frame.shape[0], frame.shape[1]) / 10 < min(box[2], box[3]) < min(frame.shape[0], frame.shape[1]) / 1 ] if boxes: res[text] = boxes if self.is_show: for box in boxes: x, y, w, h = box # 绘制矩形框对轮廓进行定位 cv2.rectangle( frame, (x, y), (x + w, y + h), (153, 153, 0), 2 ) # 将绘制的图像保存并展示 # cv2.imwrite(save_image, img) cv2.putText( frame, # image text, # text (x, y), # literal direction cv2.FONT_HERSHEY_SIMPLEX, # dot font 0.9, # scale (255, 255, 0), # color 2, # border ) if self.is_show: cv2.imshow("image", frame) cv2.waitKey(1) # cv2.destroyAllWindows() return res if __name__ == "__main__": cap = cv2.VideoCapture(0) m = ColorMeter(is_show=True) while True: success, frame = cap.read() res = m.detect_color(frame) print(res) if cv2.waitKey(1) & 0xFF == ord('q'): break

#!/usr/bin/env python # -- coding: utf-8 -- import cv2 import numpy as np import rospy from sensor_msgs.msg import Image from cv_bridge import CvBridge, CvBridgeError # 定义要识别的颜色范围 lower_color = np.array([19, 78, 44]) upper_color = np.array([74, 202, 129]) # 初始化cv_bridge bridge = CvBridge() # 定义回调函数，处理订阅到的图像 def image_callback(msg): # 将ROS图像格式转换为OpenCV图像格式 try: cv_image = bridge.imgmsg_to_cv2(msg, 'bgr8') except CvBridgeError as e: print(e) return # 转换颜色空间 hsv = cv2.cvtColor(cv_image, cv2.COLOR_BGR2HSV) # 根据颜色范围进行二值化 mask = cv2.inRange(hsv, lower_color, upper_color) # 寻找轮廓 _, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # 遍历轮廓 for contour in contours: # 计算轮廓面积 area = cv2.contourArea(contour) # 忽略面积较小的轮廓 if area < 100: continue # 计算轮廓的外接矩形 x, y, w, h = cv2.boundingRect(contour) # 在原图上绘制外接矩形 cv2.rectangle(cv_image, (x, y), (x + w, y + h), (0, 255, 0), 2) # 显示图像 cv2.imshow('frame', cv_image) cv2.waitKey(1) # 初始化节点 rospy.init_node('color_detection') # 订阅摄像头图像 image_sub = rospy.Subscriber('/usb_cam/image_raw', Image, image_callback) # 进入循环 rospy.spin() # 关闭窗口 cv2.destroyAllWindows() 帮我改成检测多种色值的

hsv = cv2.cvtColor(cv_image, cv2.COLOR_BGR2HSV) # 遍历所有颜色范围，进行二值化和轮廓检测 for color_name, color_range in colors.items(): lower_color = np.array(color_range[0]) upper_color = np....

import cv2 import numpy as np def retinex(img, sigma_list): retinex = np.zeros_like(img) for sigma in sigma_list: img_blur = cv2.GaussianBlur(img, (0, 0), sigma) img_blur = np.where(img_blur == 0, 0.1, img_blur) retinex += np.log10(img) - np.log10(img_blur) retinex = retinex / len(sigma_list) retinex = np.where(retinex < 0, 0, retinex) retinex = np.where(retinex > 1, 1, retinex) return retinex def enhance_image(img): hsi = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) h, s, i = cv2.split(hsi) i_enhanced = retinex(i.astype(np.float64), [15, 80, 250]) i_enhanced = (i_enhanced * 255).astype(np.uint8) s_enhanced = cv2.equalizeHist(s) hsi_enhanced = cv2.merge([h, s_enhanced, i_enhanced]) enhanced_img = cv2.cvtColor(hsi_enhanced, cv2.COLOR_HSV2BGR) return enhanced_img img = cv2.imread('low_illumination_1.png') enhanced_img = enhance_image(img) cv2.imshow('Enhanced Image', enhanced_img) cv2.waitKey(0) cv2.destroyAllWindows()

这是一段基于Retinex算法对图像进行增强的Python代码。Retinex算法是一种用于图像增强的经典算法，它可以增强图像的对比度和颜色鲜艳度，同时可以保留图像的细节信息。该算法的基本思想是将图像分解为多个尺度的高斯...

解释一下这段代码import cv2 import numpy as np cap = cv2.VideoCapture(0) # 初始化 ROI ret, frame = cap.read() roi = cv2.selectROI(frame, False) # 初始化 CamShift hsv_roi = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv_roi, np.array((0., 60., 32.)), np.array((180., 255., 255.))) roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180]) cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX) # 开始跟踪 term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1) while True: ret, frame = cap.read() if ret == True: hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1) ret, track_window = cv2.CamShift(dst, track_window, term_crit) pts = cv2.boxPoints(ret) pts = np.int0(pts) img = cv2.polylines(frame, [pts], True, 255, 2) cv2.imshow('CamShift', img) k = cv2.waitKey(60) & 0xff if k == 27: break else: break cv2.destroyAllWindows() cap.release()

2. 创建视频捕获对象：cv2.VideoCapture(0)表示从摄像头获取视频。 3. 初始化ROI（感兴趣区域）：通过调用cv2.selectROI函数，可以在视频帧中手动选择一个感兴趣的区域。 4. 初始化CamShift：将ROI转换为HSV...

import numpy as np import cv2 Key_Esc = 27 cap = cv2.VideoCapture(0) lower_blue = np.array([110, 50, 50]) upper_blue = np.array([130, 255, 255]) while True: ret, frame = cap.read() # hsv变换 hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) mask = cv2.inRange(hsv, lower_blue, upper_blue) res = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) if len(res) == 2: # python3 写法 contours, hierarchy = res elif len(res) == 3: # python2 写法 _, contours, hierarchy = res # 用黄色绘制出轮廓（BGR 0，255，255） cv2.drawContours(frame, contours, -1, (0, 255, 255), thickness=2) # 显示处理结果 cv2.imshow('detected', np.rot90(cv2.resize(frame, (320, 240)))) # cv2.imshow('mask', np.rot90(cv2.resize(mask,(320,240))) ) # 按q键退出，不加这一句可能无法正常显示图像 if cv2.waitKey(1) == Key_Esc: break cap.release() cv2.destroyAllWindows()

这段代码是使用Python语言编写的，主要是导入了numpy和cv2两个库。定义了一个常量Key_Esc等于27，表示按下Esc键退出程序。通过cv2.VideoCapture(0)打开电脑的摄像头，lower_blue和upper_blue是蓝色区间的下限和上限...

import cv2 import numpy as np # 读取图像 img = cv2.imread('work8.tif', -1) #转为彩色图 img1 = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) # 转换为HSV hsv = cv2.cvtColor(img1, cv2.COLOR_BGR2HSV) # 设置颜色范围 lower_gray = np.array([0, 0, 100]) upper_gray = np.array([180, 50, 255]) # 创建掩膜 mask = cv2.inRange(hsv, lower_gray, upper_gray) # 去噪 kernel = np.ones((5, 5), np.uint8) opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel) # 获取花 flower = cv2.bitwise_and(img, img, mask=opening) cv2.imshow("test", flower) # 着色 color = np.zeros_like(img) color[..., 0] = 255 color[..., 1] = 255 color[..., 2] = 0 # 将原始图像中需要着色的部分变为蓝色 result = img.copy() result[opening > 0] = color[opening > 0] # 显示结果 cv2.imshow('result', result) cv2.waitKey(0)

1. 使用cv2.imread函数读取输入图像work8.tif，并使用参数-1表示读取的是原始图像，包括alpha通道。 2. 将原始图像转换为彩色图像，因为后面需要用到颜色信息。 3. 将彩色图像转换为HSV颜色空间，方便对颜色进行...

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()

color_hist = cv2.calcHist([hsv_image], [0, 1], None, [180, 256], [0, 180, 0, 256]) color_features = cv2.normalize(color_hist, color_hist).flatten() features = np.concatenate((hog_features, color_...

import cv2 import numpy as np # 读取相机拍摄的图像 image = cv2.imread('3.jpg') # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 定义红色瓶盖的HSV颜色范围 lower_red = np.array([0, 50, 50]) upper_red = np.array([10, 255, 255]) # 根据颜色范围提取红色瓶盖的区域 mask = cv2.inRange(hsv, lower_red, upper_red)完善上述代码，显示结果

import numpy as np # 读取相机拍摄的图像 image = cv2.imread('3.jpg') # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 定义红色瓶盖的HSV颜色范围 lower_red = np.array([0, 50, ...

import numpy as np import tensorflow as tf import tflearn GAMMA = 0.99 A_DIM = 3 ENTROPY_WEIGHT = 4 ENTROPY_EPS = 1e-6 S_INFO = 4这段代码是什么意思

- import numpy as np: 导入NumPy库，并将其别名设置为np，以便在代码中更方便地使用NumPy中的函数和类。 - import tensorflow as tf: 导入TensorFlow库，以便在代码中使用TensorFlow中的函数和类。 - import...

import cv2 import numpy as np cap = cv2.VideoCapture(0) while True: ret, frame = cap.read() hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) lower_red = np.array([78,43,46]) upper_red = np.array([99,255,255]) mask = cv2.inRange(hsv, lower_red, upper_red) res = cv2.bitwise_and(frame,frame, mask= mask) cv2.imshow('frame',frame) cv2.imshow('mask',mask) cv2.imshow('res',res) if cv2.waitKey(5) & 0xFF == ord('q'): break cv2.destroyAllWindows() cap.release()

它打开摄像头（设备号为0），并在一个循环中读取帧。然后，它将每一帧转换为HSV颜色空间，并使用预定义的红色范围创建掩码。掩码将与原始帧进行逻辑与运算，以提取出红色区域。最后，它将原始帧、掩码和提取出的红色...

hsv = cv2.cvtColor(src, cv2.COLOR_BGR2HSV) low_hsv = np.array([0,43,46]) high_hsv = np.array([10,255,255])

2. 定义了一个名为low_hsv的numpy数组，其中包含HSV颜色空间中的最低取值，即[0, 43, 46]。 3. 定义了一个名为high_hsv的numpy数组，其中包含HSV颜色空间中的最高取值，即[10, 255, 255]。这个HSV范围的目的是用于...

# -- coding: utf-8 -- import cv2 import numpy as np # 定义要识别的颜色范围 lower_color = np.array([85, 90, 28]) upper_color = np.array([121, 226, 76]) # 初始化摄像头 cap = cv2.VideoCapture(0) while True: # 获取当前帧 ret, frame = cap.read() # 转换颜色空间 hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # 根据颜色范围进行二值化 mask = cv2.inRange(hsv, lower_color, upper_color) # 寻找轮廓 _,contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # 遍历轮廓 for contour in contours: # 计算轮廓面积 area = cv2.contourArea(contour) # 忽略面积较小的轮廓 if area < 100: continue # 计算轮廓的外接矩形 x, y, w, h = cv2.boundingRect(contour) # 在原图上绘制外接矩形 cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) # 显示图像 cv2.imshow('frame', frame) # 按下 q 键退出程序 if cv2.waitKey(1) & 0xFF == ord('q'): break # 释放摄像头并关闭窗口 cap.release() cv2.destroyAllWindows() 这段代码帮我改成ROS订阅摄像头

hsv = cv2.cvtColor(cv_image, cv2.COLOR_BGR2HSV) # 根据颜色范围进行二值化 mask = cv2.inRange(hsv, lower_color, upper_color) # 寻找轮廓 _, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2....

import cv2 import numpy as np # 读取图像 img = cv2.imread('image.jpg') # 将图像从RGB颜色空间转换为HSV颜色空间 hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # 定义红色的HSV范围 lower_red = np.array([, 50, 50]) upper_red = np.array([10, 255, 255]) mask1 = cv2.inRange(hsv, lower_red, upper_red) lower_red = np.array([170, 50, 50]) upper_red = np.array([180, 255, 255]) mask2 = cv2.inRange(hsv, lower_red, upper_red) # 将两个掩膜相加 mask = mask1 + mask2 # 对原始图像和掩膜进行位运算 res = cv2.bitwise_and(img, img, mask=mask) # 显示结果 cv2.imshow('image', img) cv2.imshow('mask', mask) cv2.imshow('res', res) cv2.waitKey() cv2.destroyAllWindows()改写为c++代码

cv::cvtColor(img, hsv, cv::COLOR_BGR2HSV); // 定义红色的HSV范围 cv::Scalar lower_red1(0, 50, 50); cv::Scalar upper_red1(10, 255, 255); cv::Mat mask1; cv::inRange(hsv, lower_red1, upper_red1, ...

import cv2 import numpy as np def extract_features(image): # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 计算颜色直方图 hist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256]) hist = cv2.normalize(hist, hist) # 展开直方图为一维数组 features = hist.flatten() return features from sklearn.model_selection import train_test_split from sklearn.svm import SVC # 加载数据集和标签 data = [] labels = [] for i in range(10): for j in range(100): image = cv2.imread(f"data/{i}_{j}.jpg") feature = extract_features(image) data.append(feature)w labels.append(i) # 划分数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2) # 训练SVM分类器 model = SVC(kernel="linear", C=1.0) model.fit(X_train, y_train) # 在测试集上进行测试 accuracy = model.score(X_test, y_test) pr# 加载新的测试图像 test_image = cv2.imread("test.jpg") test_feature = extract_features(test_image) # 对图像进行分类 prediction = model.predict([test_feature]) print(f"Prediction: {prediction}") print(f"Accuracy: {accuracy}")

hist = cv2.calcHist([hsv], [0, 1], None, [180, 256], [0, 180, 0, 256]) hist = cv2.normalize(hist, hist) # 展开直方图为一维数组 features = hist.flatten() return features from sklearn.model_...

import cv2 import numpy as np # 读取相机拍摄的图像 image = cv2.imread('1.jpg') # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 定义红色瓶盖的HSV颜色范围 lower_red = np.array([0, 50, 50]) upper_red = np.array([10, 255, 255]) # 根据颜色范围提取红色瓶盖的区域 mask = cv2.inRange(hsv, lower_red, upper_red) #cv2.imshow('Mask', mask) # 进行形态学操作，去除噪声 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel) # 检测红色瓶盖的轮廓 contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # 对轮廓进行筛选和排序，选择两个最大的轮廓 contours = sorted(contours, key=cv2.contourArea, reverse=True)[:2]请完善上述代码。显示结果

hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) # 定义红色瓶盖的HSV颜色范围 lower_red = np.array([0, 50, 50]) upper_red = np.array([10, 255, 255]) # 根据颜色范围提取红色瓶盖的区域 mask = cv2.inRange...

import cv2 import numpy as np img_path = 'C:\\Users\\11021\\Desktop\\火1二辉橄榄岩1-2.jpg' img = cv2.imread(img_path) # 定义每种矿物质的HSV范围 mineral_ranges = { 'biotite': [(0, 0, 50), (10, 255, 255)], # 黑云母 'hornblende': [(30, 50, 50), (50, 255, 255)], # 角闪石 'olivine': [(50, 50, 50), (70, 255, 255)], # 橄榄石 'plagioclase': [(100, 50, 50), (120, 255, 255)], # 斜长石 'pyroxene': [(70, 50, 50), (90, 255, 255)], # 辉石 'quartz': [(20, 50, 50), (30, 255, 255)] # 石英 } # 将图像转换为HSV颜色空间 hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # 创建掩膜和标签数组 masks = [] labels = np.zeros_like(hsv[:, :, 0]) # 遍历每种矿物质的范围，并创建掩膜 for i, (mineral, (lower, upper)) in enumerate(mineral_ranges.items()): mask = cv2.inRange(hsv, np.array(lower), np.array(upper)) masks.append(mask) labels[mask == 255] = i # 保存标签数据到文件 np.savetxt('labels.txt', labels)

这段代码的作用是读取一张图像，并将其转换为HSV颜色空间。然后根据预定义的每种...需要注意的是，代码中使用了numpy库中的np.zeros_like()函数创建了和hsv[:,:,0]相同大小的全零数组，并将其作为labels数组的初始值。

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import numpy as np import tensorflow as tf import tflearn GAMMA = 0.99 A_DIM = 3 ENTROPY_WEIGHT = 4 ENTROPY_EPS = 1e-6 S_INFO = 4这段代码是什么意思

hsv = cv2.cvtColor(src, cv2.COLOR_BGR2HSV) low_hsv = np.array([0,43,46]) high_hsv = np.array([10,255,255])

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