热红外相机图片与可见光图片配准教程_业界新闻

发布时间:2024-08-07 06:42

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一、前言

图像配准是一种图像处理技术，用于将多个场景对齐到单个集成图像中。在这篇文章中，我将讨论如何在可见光及其相应的热图像上应用图像配准。在继续该过程之前，让我们看看什么是热图像及其属性。

二、热红外数据介绍

热图像本质上通常是灰度图像：黑色物体是冷的，白色物体是热的，灰色的深度表示两者之间的差异。然而，一些热像仪会为图像添加颜色，以帮助用户识别不同温度下的物体。

图1 左图为可见光；有图为热红外图像

上面两个图像是可见的，它是对应的热图像，你可以看到热图像有点被裁剪掉了。这是因为在热图像中并没有捕获整个场景，而是将额外的细节作为元数据存储在热图像中。

因此，为了执行配准，我们要做的是找出可见图像的哪一部分出现在热图像中，然后对图像的该部分应用配准。

图2 .与热图像匹配后裁剪的可见图像

为了执行上述操作，基本上包含两张图像，一张参考图像和另一张要匹配的图像。因此，下面的算法会找出参考图像的哪一部分出现在第二张图像中，并为您提供匹配图像部分的位置。

现在我们知道热图像中存在可见图像的哪一部分，我们可以裁剪可见图像，然后对生成的图像进行配准。

三、配准过程

为了执行配准，我们要做的是找出将像素从可见图像映射到热图像的特征点，这在本文中进行了解释，一旦我们获得了一定数量的像素，我们就会停止并开始映射这些像素，从而完成配准过程完成了。

图3 热成像到可见光图像配准

一旦我们执行了配准，如果匹配正确，我们将获得具有配准图像的输出，如下图所示。

图4 最终输出结果

我对 400 张图像的数据集执行了此操作，获得的结果非常好。错误数量很少，请参考下面的代码，看看一切是如何完成的。

 from __future__ import print_function import numpy as np import argparse import glob import cv2 import os  MAX_FEATURES = 500 GOOD_MATCH_PERCENT = 0.15  #function to align the thermal and visible image, it returns the homography matrix  def alignImages(im1, im2,filename):     # Convert images to grayscale   im1Gray = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)   im2Gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)       # Detect ORB features and compute descriptors.   orb = cv2.ORB_create(MAX_FEATURES)   keypoints1, descriptors1 = orb.detectAndCompute(im1Gray, None)   keypoints2, descriptors2 = orb.detectAndCompute(im2Gray, None)       # Match features.   matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)   matches = matcher.match(descriptors1, descriptors2, None)       # Sort matches by score   matches.sort(key=lambda x: x.distance, reverse=False)     # Remove not so good matches   numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)   matches = matches[:numGoodMatches]     # Draw top matches   imMatches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None)   if os.path.exists(os.path.join(args["output"],"registration")):     pass   else:    os.mkdir(os.path.join(args["output"],"registration"))   cv2.imwrite(os.path.join(args["output"],"registration",filename), imMatches)       # Extract location of good matches   points1 = np.zeros((len(matches), 2), dtype=np.float32)   points2 = np.zeros((len(matches), 2), dtype=np.float32)     for i, match in enumerate(matches):     points1[i, :] = keypoints1[match.queryIdx].pt     points2[i, :] = keypoints2[match.trainIdx].pt       # Find homography   h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)     # Use homography   height, width, channels = im2.shape   im1Reg = cv2.warpPerspective(im1, h, (width, height))       return im1Reg, h  # construct the argument parser and parse the arguments # run the file with python registration.py --image filename ap = argparse.ArgumentParser() # ap.add_argument("-t", "--template", required=True, help="Path to template image") ap.add_argument("-i", "--image", required=False,default=r"热红外图像的路径",     help="Path to images where thermal template will be matched") ap.add_argument("-v", "--visualize",required=False,default=r"真彩色影像的路径") ap.add_argument("-o", "--output",required=False,default=r"保存路径") args = vars(ap.parse_args())  # put the thermal image in a folder named thermal and the visible image in a folder named visible with the same name # load the image image, convert it to grayscale, and detect edges template = cv2.imread(args["image"]) template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY) template = cv2.Canny(template, 50, 200) (tH, tW) = template.shape[:2] cv2.imshow("Template", template) #cv2.waitKey(0)  # loop over the images to find the template in  # load the image, convert it to grayscale, and initialize the # bookkeeping variable to keep track of the matched region image = cv2.imread(args["visualize"]) gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) found = None  # loop over the scales of the image for scale in np.linspace(0.2, 1.0, 20)[::-1]:     # resize the image according to the scale, and keep track     # of the ratio of the resizing     resized = cv2.resize(gray, (int(gray.shape[1] * scale),int(gray.shape[0] * scale)))     r = gray.shape[1] / float(resized.shape[1])      # if the resized image is smaller than the template, then break     # from the loop     if resized.shape[0] < tH or resized.shape[1] < tW:         break      # detect edges in the resized, grayscale image and apply template     # matching to find the template in the image     edged = cv2.Canny(resized, 50, 200)     result = cv2.matchTemplate(edged, template, cv2.TM_CCOEFF)     (_, maxVal, _, maxLoc) = cv2.minMaxLoc(result)      # check to see if the iteration should be visualized     if True:         # draw a bounding box around the detected region         clone = np.dstack([edged, edged, edged])         cv2.rectangle(clone, (maxLoc[0], maxLoc[1]),             (maxLoc[0] + tW, maxLoc[1] + tH), (0, 0, 255), 2)         cv2.imshow("Visualize", clone)         #cv2.waitKey(0)      # if we have found a new maximum correlation value, then update     # the bookkeeping variable     if found is None or maxVal > found[0]:         found = (maxVal, maxLoc, r)  # unpack the bookkeeping variable and compute the (x, y) coordinates # of the bounding box based on the resized ratio (_, maxLoc, r) = found (startX, startY) = (int(maxLoc[0] * r), int(maxLoc[1] * r)) (endX, endY) = (int((maxLoc[0] + tW) * r), int((maxLoc[1] + tH) * r))  # draw a bounding box around the detected result and display the image cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2) crop_img = image[startY:endY, startX:endX] #cv2.imshow("Image", image) cv2.imshow("Crop Image", crop_img) #cv2.waitKey(0)  #name = r"E:\temp\data5/thermal/"+args["image"]+'.JPG' thermal_image = cv2.imread(args["image"], cv2.IMREAD_COLOR)  #cropping out the matched part of the thermal image crop_img = cv2.resize(crop_img, (thermal_image.shape[1], thermal_image.shape[0]))  #cropped image will be saved in a folder named output if os.path.exists(os.path.join(args["output"],"process")):    pass else:    os.mkdir(os.path.join(args["output"],"process")) cv2.imwrite(os.path.join(args["output"],"process", os.path.basename(args["visualize"])),crop_img)  #both images are concatenated and saved in a folder named results final = np.concatenate((crop_img, thermal_image), axis = 1) if os.path.exists(os.path.join(args["output"],"results")):    pass else:    os.mkdir(os.path.join(args["output"],"results")) cv2.imwrite(os.path.join(args["output"],"results", os.path.basename(args["visualize"])),final)  #cv2.waitKey(0) # Registration # Read reference image refFilename =  args["image"] print("Reading reference image : ", refFilename) imReference = cv2.imread(refFilename, cv2.IMREAD_COLOR)  # Read image to be aligned imFilename = os.path.join(args["output"],"process", os.path.basename(args["visualize"])) print("Reading image to align : ", imFilename);   im = cv2.imread(imFilename, cv2.IMREAD_COLOR) file_name=os.path.basename(args["image"])+'_registration.JPG' imReg, h = alignImages(im,imReference,file_name) cv2.imwrite(os.path.join(args["output"],"results", os.path.basename(args["image"])+'_result.JPG'),imReg) print("Estimated homography : \n",  h)

我们已经成功地进行了热到可见图像配准。你可以用你的数据集来尝试一下，然后看看结果。