for (auto it : m_pts) { /*int rect_x = it.x() - RECT_SIZE/4 ; int rect_y = it.y() - RECT_SIZE /4; cv::Rect rect(rect_x, rect_y, RECT_SIZE, RECT_SIZE); rect = rect & cv::Rect(0, 0, ImgBin.cols, ImgBin.rows); for (int nRow = rect_y - rect.height; nRow < rect_y + rect.height; nRow++) { for (int nCol = rect_x - rect.width; nCol < rect_x + rect.width; nCol++) {*/ ImgBin.row(it.y()).data[it.x()] = 255; } /* }在画出图片边缘1到时候，会进入断点

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在qgraphicsview中用此函数遍历多个点集for (auto it : m_pts) { /int rect_x = it.x() - RECT_SIZE/4 ; int rect_y = it.y() - RECT_SIZE /4; cv::Rect rect(rect_x, rect_y, RECT_SIZE, RECT_SIZE); rect = rect & cv::Rect(0, 0, ImgBin.cols, ImgBin.rows); for (int nRow = rect_y - rect.height; nRow < rect_y + rect.height; nRow++) { for (int nCol = rect_x - rect.width; nCol < rect_x + rect.width; nCol++) {/ ImgBin.row(it.y()).data[it.x()] = 255; }在超出图片的地方会进入断点

for (auto it : m_pts) { int rect_x = it.x() - RECT_SIZE / 4; int rect_y = it.y() - RECT_SIZE / 4; cv::Rect rect(rect_x, rect_y, RECT_SIZE, RECT_SIZE); rect = rect & cv::Rect(0, 0, ImgBin.cols, ...

mid = RectWidth[sum / 2 - 1]; float meanangle = 0.0; for (int i = 0; i< sum; i++) { RotatedRect rect = rt[i]; /Point2f pts[4]; rect.points(pts);/ //过滤矩形宽度超过平均宽度20%的矩形 if (rect.size.width >(mid + 10) || rect.size.width < (mid - 10)) { continue; } RectCenterlist[index].x = rect.center.x; RectCenterlist[index].y = rect.center.y; RectWidth[index] = rect.size.width; RectHeight[index] = rect.size.height; s1 += rect.angle; index++; }此段代码有什么用

4. 对于每个旋转矩形 rect： - 检查矩形宽度是否超过平均宽度的上下限（mid + 10 和 mid - 10），如果超过则跳过继续下一个矩形。 - 将满足条件的矩形的中心坐标存储到 RectCenterlist 数组中的对应索引位置。 - ...

解释def four_point_transform(image, pts): # 透视变换得到车牌小图 # rect = order_points(pts) rect = pts.astype('float32') (tl, tr, br, bl) = rect widthA = np.sqrt(((br[0] - bl[0]) 2) + ((br[1] - bl[1]) 2)) widthB = np.sqrt(((tr[0] - tl[0]) 2) + ((tr[1] - tl[1]) 2)) maxWidth = max(int(widthA), int(widthB)) heightA = np.sqrt(((tr[0] - br[0]) 2) + ((tr[1] - br[1]) 2)) heightB = np.sqrt(((tl[0] - bl[0]) 2) + ((tl[1] - bl[1]) 2)) maxHeight = max(int(heightA), int(heightB)) dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype="float32") M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) return warped

- rect = pts.astype('float32')：将四个点的坐标转换为浮点数类型，方便后续计算。 - (tl, tr, br, bl) = rect：将转换后的四个点的坐标分别赋值给左上角、右上角、右下角、左下角，方便后续计算。 - widthA...

解释def adaPoint(box, pro): box_pro = box if pro != 1.0: box_pro = box / pro box_pro = np.trunc(box_pro) return box_pro # 四边形顶点排序，[top-left, top-right, bottom-right, bottom-left] def orderPoints(pts): rect = np.zeros((4, 2), dtype="float32") s = pts.sum(axis=1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] diff = np.diff(pts, axis=1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] return rect # 计算长宽 def pointDistance(a, b): return int(np.sqrt(np.sum(np.square(a - b)))) # 透视变换 def warpImage(image, box): w, h = pointDistance(box[0], box[1]), pointDistance(box[1], box[2]) dst_rect = np.array([[0, 0], [w - 1, 0], [w - 1, h - 1], [0, h - 1]], dtype='float32') M = cv2.getPerspectiveTransform(box, dst_rect) warped = cv2.warpPerspective(image, M, (w, h)) return warped

(b) 使用cv2.getPerspectiveTransform函数计算从原始四边形box到目标矩形dst_rect的透视变换矩阵M； (c) 使用cv2.warpPerspective函数对原始图像image进行透视变换，得到目标矩形内的图像warped。透视变换常...

import cv2 import numpy as np # 读取图像 img = cv2.imread('123.jpg') new_size=(600,700) img=cv2.resize(img,new_size) # 灰度化处理 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 高斯模糊 Gaussian = cv2.GaussianBlur(gray,(9,9),2) cv2.imwrite('gaosi.jpg',Gaussian) # 边缘检测 edges = cv2.Canny(Gaussian, 100, 200) cv2.imwrite('bianyuan.jpg',edges) # 轮廓检测 contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # 找到最大轮廓 max_area = 0 max_contour = None for contour in contours: area = cv2.contourArea(contour) if area > max_area: max_area = area max_contour = contour # 计算矩形边界框 rect = cv2.minAreaRect(max_contour) box = cv2.boxPoints(rect) box = np.int0(box) # 计算变换矩阵 width = int(rect[1][0]) height = int(rect[1][1]) src_pts = box.astype("float32") dst_pts = np.array([[0, height-1], [0, 0], [width-1, 0], [width-1, height-1]], dtype="float32") M = cv2.getPerspectiveTransform(src_pts, dst_pts) # 进行透视变换 result = cv2.warpPerspective(img, M, (width, height)) # 显示结果 cv2.imshow('input', img) cv2.imshow('output', result) cv2.imwrite('result.jpg',result) cv2.waitKey(0)用到的函数解释

9. cv2.getPerspectiveTransform(src_pts, dst_pts)：根据输入的源点和目标点计算出变换矩阵。 10. cv2.warpPerspective(img, M, (width, height))：对原图像进行透视变换，得到变换后的图像。 11. cv2....

def four_point_transform(image, pts): # obtain a consistent order of the points and unpack them # individually rect = order_points(pts) (tl, tr, br, bl) = rect # compute the width of the new image, which will be the # maximum distance between bottom-right and bottom-left # x-coordiates or the top-right and top-left x-coordinates widthA = np.sqrt(((br[0] - bl[0]) 2) + ((br[1] - bl[1]) 2)) widthB = np.sqrt(((tr[0] - tl[0]) 2) + ((tr[1] - tl[1]) 2)) maxWidth = max(int(widthA), int(widthB)) # compute the height of the new image, which will be the # maximum distance between the top-right and bottom-right # y-coordinates or the top-left and bottom-left y-coordinates heightA = np.sqrt(((tr[0] - br[0]) 2) + ((tr[1] - br[1]) 2)) heightB = np.sqrt(((tl[0] - bl[0]) 2) + ((tl[1] - bl[1]) 2)) maxHeight = max(int(heightA), int(heightB)) # now that we have the dimensions of the new image, construct # the set of destination points to obtain a "birds eye view", # (i.e. top-down view) of the image, again specifying points # in the top-left, top-right, bottom-right, and bottom-left # order dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype = "float32") # compute the perspective transform matrix and then apply it M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) # return the warped image return warped

这是一个使用透视变换的函数，它可以将一个四边形区域从原...4. 计算透视变换矩阵，并将其应用到原图像上，得到变换后的矩形图像； 5. 返回变换后的矩形图像。这个函数可以用于许多应用，例如车牌识别、文档扫描等。

width = int(rect[1][0]) height = int(rect[1][1]) src_pts = box.astype("float32") dst_pts = np.array([[0, height-1], [0, 0], [width-1, 0], [width-1, height-1]], dtype="float32") M = cv2.getPerspectiveTransform(src_pts, dst_pts)代码解释

这段代码是用于计算透视变换矩阵M的。首先，根据矩形的边界框rect获取矩形的宽度和高度，将其转换为整型数值。然后，将四个角点的坐标box转换为浮点型数值，并赋值给src_pts。接着，定义目标图像的四个角点...

contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) img_bgr = cv2.cvtColor(imm, cv2.COLOR_GRAY2BGR) # 绘制边缘 # imm = cv2.drawContours(img_bgr, contours, -1, (0, 0, 255), 1) # 遍历轮廓 rects = [] for contour in contours: rect = cv2.minAreaRect(contour) rects.append(rect) box = cv2.boxPoints(rect) box = np.int0(box) cv2.drawContours(img_bgr, [box], 0, (0, 0, 255), 2) for i, rect in enumerate(sorted(rects, key=lambda x: x[1][0] * x[1][1], reverse=True)): print(f'Rank {i + 1}: Size={rect[1][0] * rect[1][1]:.0f}, ({int(rect[0][0])}, {int(rect[0][1])}), Width={rect[1][0]:.0f}, Height={rect[1][1]:.0f}, Angle={rect[2]:.0f}')打印的具体是什么帮我再加上矩形轮廓的四个角的坐标

除了之前提到的矩形的信息，... print(f'Rank {i + 1}: Size={rect[1][0] * rect[1][1]:.0f}, ({int(rect[0][0])}, {int(rect[0][1])}), Width={rect[1][0]:.0f}, Height={rect[1][1]:.0f}, Angle={rect[2]:.0f}')

基于以下代码import sys import dlib import cv2 predictor_path="shape_predictor_194_face_landmarks.dat" detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(predictor_path) cam = cv2.VideoCapture(0) cam.set(3, 1280) cam.set(4, 720) color_white = (255, 255, 255) line_width = 3 while True: ret_val, img = cam.read() rgb_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) dets = detector(rgb_image) for det in dets: cv2.rectangle(img, (det.left(), det.top()), (det.right(), det.bottom()), color_white, line_width) shape = predictor(img, det) for p in shape.parts(): cv2.circle(img, (p.x, p.y), 2, (0, 255, 0), -1) cv2.imshow('my webcam', img) if cv2.waitKey(1) == 27: break cv2.destroyAllWindows() 加入dilb和OpenCV的目标跟踪算法，于持续视频播放中，每秒中的第一帧图像执行dilb人脸特征点检测，后续图像跟踪这些特征点，并使用dlib.correlation_tracker()函数、cv2.calcOpticalFlowFarneback()函数，争取实现194个人脸特征点的跟踪

rect = dlib.rectangle(int(pos.left()), int(pos.top()), int(pos.right()), int(pos.bottom())) shape = predictor(rgb_image, rect) tracking_pts = [(p.x, p.y) for p in shape.parts()] for pt in ...

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box = np.int0(cv2.boxPoints(rect)) # 利用顶点坐标进行仿射变换 orig_pts = np.float32(box) dst_pts = np.float32([[0, 0], [500, 0], [0, 500], [500, 500]]) matrix = cv2.getPerspectiveTransform(orig_pts, ...

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if len(approx) == 4: rect.append(approx) # 显示筛选后的轮廓 cv2.drawContours(img, rect, -1, (0, 255, 0), 2) cv2.imshow('contours', img) cv2.waitKey(0) cv2.destroyAllWindows() 最后，我们可以...

Python1.二维码几何矫正

M = cv2.getPerspectiveTransform(src_pts, dst_pts) cropped = cv2.warpPerspective(rotated, M, (width, height)) # 解码裁剪后的二维码图像 gray_cropped = cv2.cvtColor(cropped, cv2.COLOR_BGR2GRAY) ...

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M = cv2.getPerspectiveTransform(src_pts, dst_pts) # 进行变换 result = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0])) # 显示结果 cv2.imshow('input', img) cv2.imshow('output', result) cv2....

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if len(four_lines) == 4: pts = [] for line in four_lines: x1, y1, x2, y2 = line[0] pts.append((x1, y1)) pts.append((x2, y2)) rect = cv2.minAreaRect(np.array(pts)) box = cv2.boxPoints(rect) box...

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