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yRBW2 — Postimages[^]

hi, at the first, please open link and see (image) that is on top of question

i have only one simple question

as you see in image , there are few lines in image, my code find intersection points of lines

i want to know how we can select and drag these points (intersection points) by mouse or by arrow keys (change position of each point manually)?

you can download my project (lower than 1mb) at:Upload files for free - file1.zip - ufile.io[^]

**What I have tried:**

hi, at the first, please open link and see (image) that is on top of question

i have only one simple question

as you see in image , there are few lines in image, my code find intersection points of lines

i want to know how we can select and drag these points (intersection points) by mouse or by arrow keys (change position of each point manually)?

you can download my project (lower than 1mb) at:Upload files for free - file1.zip - ufile.io[^]

Python

<pre>import cv2 import math import utils import numpy as np from itertools import permutations def findBestHomography(): # Loop through all the possible permutations of 4 points in the image all_img_pts = [np.array(i, dtype=float) for i in intersections] for img_pts in permutations(intersections, 4): # Optimization: check if polygon is convex and clockwise if not utils.isConvex(img_pts) or not utils.isClockwise(img_pts): continue # Loop through all the possible permutations of 4 points in the reference for ref_points in permutations(utils.reference_points_right, 4): # Optimization: check if polygon is convex and clockwise if not utils.isConvex(img_pts) or not utils.isClockwise(img_pts): continue # Convert to np.ndarray of np.ndarray of float64 img_pts = [np.array(i, dtype=float) for i in img_pts] img_pts = np.array(img_pts) ref_points = np.array(ref_points) # Calculate homography h, status = cv2.findHomography(img_pts, ref_points) h_inv = None if h is None: continue try: h_inv = np.linalg.inv(h) except: continue # Check if at least one other image point matches one other reference point for test_img_pt in all_img_pts: if not test_img_pt in img_pts: for test_ref_pt in utils.reference_points_right: if not list(test_ref_pt) in ref_points.tolist(): # Apply homography to reference test point test_img_in_ref = cv2.perspectiveTransform(test_img_pt.reshape(1, 1, -1), h)[0][0] # Check if it matches the image test point with an error of at most 2m distance = np.linalg.norm(test_img_in_ref - test_ref_pt) if distance < 2: print(test_ref_pt) print(ref_points) print(test_img_pt) print(img_pts) print(test_img_in_ref) print(distance) print('-------------------------------------') applyHomographyLine(img, h, h_inv) return def applyHomographyLine(img, h, h_inv): # Get offside player point (field image) print('Click on the offside player and then press [ENTER]') player_im = utils.get_points(img, 1)[0] player_imm = utils.get_points(img, 1)[0] #print(player_im) # Get corresponding offside player point in real world player_rw = cv2.perspectiveTransform(player_im.reshape(1, 1, -1), h)[0][0] player_rww = cv2.perspectiveTransform(player_imm.reshape(1, 1, -1), h)[0][0] #print(player_rw) # Get the two line points in the real world line (same x, y is the field bounds) line_point_1_rw = player_rw.copy() line_point_1_rw[1] = 0 line_point_2_rw = player_rw.copy() line_point_2_rw[1] = 67 line_point_3_rw = player_rww.copy() line_point_3_rw[1] = 0 line_point_4_rw = player_rww.copy() line_point_4_rw[1] = 67 #print(line_point_1_rw) #print(line_point_2_rw) # Get corresponding second point in the image line_point_1_im = cv2.perspectiveTransform(line_point_1_rw.reshape(1, 1, -1), h_inv)[0][0] line_point_2_im = cv2.perspectiveTransform(line_point_2_rw.reshape(1, 1, -1), h_inv)[0][0] line_point_3_im = cv2.perspectiveTransform(line_point_3_rw.reshape(1, 1, -1), h_inv)[0][0] line_point_4_im = cv2.perspectiveTransform(line_point_4_rw.reshape(1, 1, -1), h_inv)[0][0] #print(line_point_1_im) #print(line_point_2_im) # Draw line height, width, channels = img.shape blank_image = np.zeros((height,width,3), np.uint8) cv2.line(blank_image, tuple(line_point_1_im.astype(int)), tuple(line_point_2_im.astype(int)), (0,0,255), 1, cv2.LINE_AA) cv2.line(blank_image, tuple(line_point_3_im.astype(int)), tuple(line_point_4_im.astype(int)), (255,255,0), 1, cv2.LINE_AA) img = utils.blend_overlay_with_field(img,blank_image,0.5) cv2.imshow("Image", img) cv2.waitKey(0) if __name__ == '__main__': debug = True img = cv2.imread('./football1.jpg') field = utils.GetFieldLayer(img) # cv2.imshow("Field Layer", field) field = cv2.GaussianBlur(field, (3, 3), cv2.BORDER_DEFAULT) edges = cv2.Canny(field, 100, 300) lines = cv2.HoughLines(edges, 1, math.radians(1.7), 150, None, 0, 0) lns = [] if lines is not None: for i in range(0, len(lines)): rho = lines[i][0][0] theta = lines[i][0][1] a = math.cos(theta) b = math.sin(theta) x0 = a * rho y0 = b * rho pt1 = (int(x0 + 1000 * (-b)), int(y0 + 1000 * (a))) pt2 = (int(x0 - 1000 * (-b)), int(y0 - 1000 * (a))) lns.append(utils.Line(pt1, pt2)) # Calculate intersection points intersections = [] for i in range(0, len(lns)): for j in range(i + 1, len(lns)): point = lns[i].intersection(lns[j]) if not point is None: intersections.append(point) if debug: cv2.line(img, lns[i].pt1, lns[i].pt2, (0, 0, 255), 1) # Cleanup similar points and points outside of the image i = 0 while i < len(intersections): # Check if outside of image xi,yi = intersections[i] sy,sx,_ = img.shape if xi < 0 or yi < 0 or xi > sx or yi > sy: del intersections[i] continue # Check if similar j = i+1 while j < len(intersections): distance = np.linalg.norm(list(x-y for x,y in zip(intersections[i],intersections[j]))) if distance < 20: del intersections[j] continue j += 1 i += 1 # Draw points if debug: for point in intersections: cv2.circle(img, point, 10, (0, 255, 0)) # Homography if len(intersections) < 4: raise Exception('Not enough points were detected for a homography') findBestHomography()

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