Calibration and homography tools
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Ruben van de Ven
commit
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94
01-calibrate.py
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94
01-calibrate.py
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'''
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Find camera intrinsicts:
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camera matrix and distortion coefficients
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Largely a copy from https://longervision.github.io/2017/03/16/ComputerVision/OpenCV/opencv-internal-calibration-chessboard/
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Usage:
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1. Set dataset variable to point to a directory containing chessboard.mp4
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2. make sure CHECKERBOARD has the nr of corners in the printed board used. Use (6,9) for https://github.com/opencv/opencv/blob/4.x/doc/pattern.png
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3. Scripts creates a `calibration.json` in the dataset folder
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'''
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import numpy as np
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import cv2
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import json
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import tqdm
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import math
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dataset = 'hof2'
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# set needed detections. Use math.inf to scan the whole video
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needed_detections = math.inf # 20
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# Defining the dimensions of checkerboard
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CHECKERBOARD = (6,9)
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# termination criteria
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criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
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# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
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objp = np.zeros((CHECKERBOARD[0] * CHECKERBOARD[1],3), np.float32)
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objp[:,:2] = np.mgrid[0:CHECKERBOARD[0],0:CHECKERBOARD[1]].T.reshape(-1,2)
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# Arrays to store object points and image points from all the images.
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objpoints = [] # 3d point in real world space
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imgpoints = [] # 2d points in image plane.
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cap = cv2.VideoCapture(dataset / "chessboard.mp4")
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frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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dim = {
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'width': frame_width,
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'height': frame_height,
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}
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found = 0
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p = tqdm.tqdm()
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p2 = tqdm.tqdm(total=needed_detections)
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while ((found < needed_detections) if math.isfinite(needed_detections) else True):
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ret, img = cap.read() # Capture frame-by-frame
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if not ret:
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break
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p.update()
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# Find the chess board corners
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ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD,None)
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# If found, add object points, image points (after refining them)
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if ret == True:
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objpoints.append(objp) # Certainly, every loop objp is the same, in 3D.
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corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
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imgpoints.append(corners2)
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p2.update()
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p2.n
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# Draw and display the corners
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img = cv2.drawChessboardCorners(img, CHECKERBOARD, corners2, ret)
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found += 1
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cv2.imshow('img', img)
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cv2.waitKey(1)
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# When everything done, release the capture
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cap.release()
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cv2.destroyAllWindows()
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print(f"Calculating matrixes with {found} detections")
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ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
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# It's very important to transform the matrix to list.
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data = {'dim': dim, 'camera_matrix': np.asarray(mtx).tolist(), 'dist_coeff': np.asarray(dist).tolist()}
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fn = dataset + "/calibration.json"
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print(f"write to {fn}")
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with open(fn, "w") as f:
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json.dump(data, f)
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116
02-testcalibration-and-draw-points.py
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02-testcalibration-and-draw-points.py
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"""
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After obtaining the calibration.json (camera P and distortion matrixes)
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using 01-calibrate.py, this script previews the calibration. Using the cursor
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on the original (not-yet-undistorted) image you can add points, which can be
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used for the homography later.
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1. Set dataset variable to point to the folder with calibration.json and a preview image
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2. Set a snapshot image. Note that this image _can_ be higher resolution than the video that is used
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this allows for more precise point placement, but might need some conversion in the next step
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3. Points are read and saved from points.json in the dataset folder
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"""
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import cv2
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import json
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import os
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import numpy as np
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dataset = 'hof2'
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snapshot_img = 'snapshot3.png'
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with open(dataset + '/calibration.json', 'r') as fp:
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calibdata = json.load(fp)
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mtx = np.array(calibdata['camera_matrix'])
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dist = np.array(calibdata['dist_coeff'])
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w, h = calibdata['dim']['width'], calibdata['dim']['height']
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# # Refining the camera matrix using parameters obtained by calibration
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# if we don't set this, the new image will be cropped to the minimum size
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# this way, no cropping occurs
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# w, h = 2560, 1440
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newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
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if os.path.exists(dataset + '/points.json'):
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with open(dataset + '/points.json', 'r') as fp:
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points = json.load(fp)
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else:
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points = [[500,500],[1000,1000]]
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def add_point(event,x,y,flags,param):
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global points
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if event == cv2.EVENT_LBUTTONUP:
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selected = None
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for i, p in enumerate(points):
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d = (p[0]-x)**2 + (p[1]-y)**2
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if d < 14:
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selected = i
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break
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print('click', selected)
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if selected is None:
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points.append([x,y])
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else:
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points.pop(selected)
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# cv2.circle(img,(x,y),100,(255,0,0),-1)
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# mouseX,mouseY = x,y
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cv2.namedWindow('original image')
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cv2.setMouseCallback('original image',add_point)
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# cap = cv2.VideoCapture("./hof2-hikvision.mp4")
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cap = cv2.VideoCapture(dataset + "/" + snapshot_img)
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img_w = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
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img_h = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
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# scale saved points to snapshot
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points = [
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[p[0]*(img_w/w), p[1]*(img_h/h)] for p in points
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]
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imgOld = None
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while True: # (found < needed): # Here, 10 can be changed to whatever number you like to choose
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ret, img = cap.read() # Capture frame-by-frame
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if not ret:
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img = imgOld
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else:
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imgOld = img
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# Method 1 to undistort the image
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dst = cv2.undistort(img, mtx, dist, None, newcameramtx)
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dstPoints = cv2.undistortPoints(np.array(points).astype('float32'), mtx, dist, None, P=newcameramtx)
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# dst = cv2.undistort(img, mtx, dist, None)
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# # Method 2 to undistort the image
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# mapx,mapy=cv2.initUndistortRectifyMap(mtx,dist,None,newcameramtx,(w,h),5)
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# dst = cv2.remap(img,mapx,mapy,cv2.INTER_LINEAR)
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# Displaying the undistorted image
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drawImg = img.copy()
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drawDst = dst.copy()
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for p in dstPoints:
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x = int(p[0][0])
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y= int(p[0][1])
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cv2.circle(drawDst, (x,y), radius=2, color=(0, 0, 255), thickness=-1)
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for i, p in enumerate(points):
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x = int(p[0])
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y= int(p[1])
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cv2.circle(drawImg, (x,y), radius=2, color=(0, 0, 255), thickness=-1)
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cv2.putText(drawImg, f"{i}", (x,y-5), cv2.FONT_HERSHEY_COMPLEX, 4, (0,0,255))
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cv2.imshow("undistorted image",drawDst)
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cv2.imshow("original image",drawImg)
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if cv2.waitKey(5) == ord('q'):
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break
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print("write points.json")
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with open(dataset + '/points.json', 'w') as fp:
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points = [
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[p[0]*(w/img_w), p[1]*(h/img_h)] for p in points
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]
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json.dump(points, fp)
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03-homography.py
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03-homography.py
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"""
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After calibrating the camera, this scripts helps with setting
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the homography to map all points to a top-down space (so that
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distances remain equal.)
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1. Set dataset variable and snapshot img as in `02-....py`.
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2. Make sure to have a irl_points.json file which translates all
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points in img_points.json to their respective real world coordinates.
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A useful way to obtain these: draw them (e.g. with chalk marker) on the
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ground, measure distances, place distances in SolveSpace, export SolveSpace
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to SVG, get the point coordinates from that file (in cm).
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3. Run the script to save the homography.json file.
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"""
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import cv2
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import json
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import numpy as np
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dataset = 'hof2'
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dataset_sample_img = "snapshot3.png"
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with open(dataset + '/img_points.json', 'r') as fp:
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img_points = np.array(json.load(fp))
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# to place points accurate I used a 2160p image, but during calibration and
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# prediction I use(d) a 1440p image, so convert points to different space:
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img_points = np.array(img_points)
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with open(dataset + '/irl_points.json', 'r') as fp:
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irl_points = json.load(fp)
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# irl_points = np.array([[p[0]/10+100, p[1]/10+100] for p in irl_points])
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irl_points = np.array(irl_points)
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# I measured IRL points in cm. Scale to meters
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irl_points /= 100
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def points_on_img(in_img, points) -> cv2.Mat:
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img = in_img.copy()
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if points.shape[1:] == (1,2):
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points = np.reshape(points, (points.shape[0], 2))
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for i, p in enumerate(points):
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x = int(p[0])
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y= int(p[1])
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cv2.circle(img, (x,y), radius=2, color=(0, 0, 255), thickness=-1)
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cv2.putText(img, f"{i}", (x,y-5), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255))
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return img
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with open(dataset + '/calibration.json', 'r') as fp:
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calibdata = json.load(fp)
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mtx = np.array(calibdata['camera_matrix'])
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dist = np.array(calibdata['dist_coeff'])
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w, h = calibdata['dim']['width'], calibdata['dim']['height']
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img = cv2.resize(cv2.imread(dataset + "/" + dataset_sample_img), (w, h))
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newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w,h), 1, (w,h))
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# first undistort the points so that lines are actually straight
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undistorted_img_points = cv2.undistortPoints(np.array([img_points]).astype('float32'), mtx, dist, None, newcameramtx)
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undistorted_img = cv2.undistort(img, mtx, dist, None, newcameramtx)
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cv2.imshow('original', points_on_img(img, img_points))
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cv2.imshow('undistorted', points_on_img(undistorted_img, undistorted_img_points))
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H, status = cv2.findHomography(undistorted_img_points,irl_points)
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# Homography converts to meters, this make the picture miniscule.
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# Scale up for preview
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view_H = H
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view_H[:2] = H[:2]*100
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dst_img = cv2.warpPerspective(undistorted_img,view_H,(w,h))
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dst_img_points = cv2.perspectiveTransform(np.array(undistorted_img_points), view_H)
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# when converting from mapped space back to image space
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# inv_H = np.linalg.pinv(H)
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print(dst_img_points)
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dst = points_on_img(dst_img, dst_img_points)
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# print(dst.shape)
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cv2.imshow('sampl', dst)
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for a,b, c, d in zip(img_points, undistorted_img_points, irl_points, dst_img_points):
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print(f"{a} -> {b} -> {c} -> {d}")
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# H[:2] = H[:2]/ 100
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# print(H)
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with open(dataset + "/homography.json", 'w') as fp:
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json.dump(H.tolist(), fp)
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while True:
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if cv2.waitKey(33) == ord('q'):
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break
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23
README.md
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README.md
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# Some tools to facilitate trajectory prediction
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_See also [trap](https://git.rubenvandeven.com/security_vision/trap)_
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## 1. Camera calibration
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Find the camera intrinsics and lens distortion matrixes. This helps to remove curvature from the image, and points map to a linear space.
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## 02. Test Calibration and draw points
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Apply the now obtained camera matrix to undistort a snapshot. Check if it looks good.
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Now we can obtain coordinates to map for the homography. Draw points on the floor (I used chalk) and measure their distances. I then used SolveSpace to go from their distances to positions in a plane.
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Then with a camera snapshot of these points, click with the cursor in the source image to draw mark these points in the image.
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This is saved to `points.json`. If this is right, rename it to `img_points.json` for the homography.
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## 2. Homography
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Having the camera intrinsics, the perspective of the camera can be undone by mapping points to a 'top down' space. This way, the distances between points is in accordance to their distance IRL.
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This file reads camera intrinsics & distortion matrixes, `img_points.json` (obtained step 2) and the corresponding `irl_points.json`. Which I created based on coordinates obtained with SolveSpace.
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118
poetry.lock
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poetry.lock
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# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
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[[package]]
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name = "colorama"
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version = "0.4.6"
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description = "Cross-platform colored terminal text."
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optional = false
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python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
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files = [
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{file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
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]
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[[package]]
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name = "numpy"
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version = "2.1.2"
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description = "Fundamental package for array computing in Python"
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optional = false
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python-versions = ">=3.10"
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|
||||
{file = "numpy-2.1.2-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:13602b3174432a35b16c4cfb5de9a12d229727c3dd47a6ce35111f2ebdf66ff4"},
|
||||
{file = "numpy-2.1.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1ebec5fd716c5a5b3d8dfcc439be82a8407b7b24b230d0ad28a81b61c2f4659a"},
|
||||
{file = "numpy-2.1.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e2b49c3c0804e8ecb05d59af8386ec2f74877f7ca8fd9c1e00be2672e4d399b1"},
|
||||
{file = "numpy-2.1.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:2cbba4b30bf31ddbe97f1c7205ef976909a93a66bb1583e983adbd155ba72ac2"},
|
||||
{file = "numpy-2.1.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8e00ea6fc82e8a804433d3e9cedaa1051a1422cb6e443011590c14d2dea59146"},
|
||||
{file = "numpy-2.1.2-cp311-cp311-win32.whl", hash = "sha256:5006b13a06e0b38d561fab5ccc37581f23c9511879be7693bd33c7cd15ca227c"},
|
||||
{file = "numpy-2.1.2-cp311-cp311-win_amd64.whl", hash = "sha256:f1eb068ead09f4994dec71c24b2844f1e4e4e013b9629f812f292f04bd1510d9"},
|
||||
{file = "numpy-2.1.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d7bf0a4f9f15b32b5ba53147369e94296f5fffb783db5aacc1be15b4bf72f43b"},
|
||||
{file = "numpy-2.1.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b1d0fcae4f0949f215d4632be684a539859b295e2d0cb14f78ec231915d644db"},
|
||||
{file = "numpy-2.1.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:f751ed0a2f250541e19dfca9f1eafa31a392c71c832b6bb9e113b10d050cb0f1"},
|
||||
{file = "numpy-2.1.2-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:bd33f82e95ba7ad632bc57837ee99dba3d7e006536200c4e9124089e1bf42426"},
|
||||
{file = "numpy-2.1.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1b8cde4f11f0a975d1fd59373b32e2f5a562ade7cde4f85b7137f3de8fbb29a0"},
|
||||
{file = "numpy-2.1.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d95f286b8244b3649b477ac066c6906fbb2905f8ac19b170e2175d3d799f4df"},
|
||||
{file = "numpy-2.1.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:ab4754d432e3ac42d33a269c8567413bdb541689b02d93788af4131018cbf366"},
|
||||
{file = "numpy-2.1.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e585c8ae871fd38ac50598f4763d73ec5497b0de9a0ab4ef5b69f01c6a046142"},
|
||||
{file = "numpy-2.1.2-cp312-cp312-win32.whl", hash = "sha256:9c6c754df29ce6a89ed23afb25550d1c2d5fdb9901d9c67a16e0b16eaf7e2550"},
|
||||
{file = "numpy-2.1.2-cp312-cp312-win_amd64.whl", hash = "sha256:456e3b11cb79ac9946c822a56346ec80275eaf2950314b249b512896c0d2505e"},
|
||||
{file = "numpy-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:a84498e0d0a1174f2b3ed769b67b656aa5460c92c9554039e11f20a05650f00d"},
|
||||
{file = "numpy-2.1.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:4d6ec0d4222e8ffdab1744da2560f07856421b367928026fb540e1945f2eeeaf"},
|
||||
{file = "numpy-2.1.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:259ec80d54999cc34cd1eb8ded513cb053c3bf4829152a2e00de2371bd406f5e"},
|
||||
{file = "numpy-2.1.2-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:675c741d4739af2dc20cd6c6a5c4b7355c728167845e3c6b0e824e4e5d36a6c3"},
|
||||
{file = "numpy-2.1.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:05b2d4e667895cc55e3ff2b56077e4c8a5604361fc21a042845ea3ad67465aa8"},
|
||||
{file = "numpy-2.1.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:43cca367bf94a14aca50b89e9bc2061683116cfe864e56740e083392f533ce7a"},
|
||||
{file = "numpy-2.1.2-cp313-cp313-musllinux_1_1_x86_64.whl", hash = "sha256:76322dcdb16fccf2ac56f99048af32259dcc488d9b7e25b51e5eca5147a3fb98"},
|
||||
{file = "numpy-2.1.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:32e16a03138cabe0cb28e1007ee82264296ac0983714094380b408097a418cfe"},
|
||||
{file = "numpy-2.1.2-cp313-cp313-win32.whl", hash = "sha256:242b39d00e4944431a3cd2db2f5377e15b5785920421993770cddb89992c3f3a"},
|
||||
{file = "numpy-2.1.2-cp313-cp313-win_amd64.whl", hash = "sha256:f2ded8d9b6f68cc26f8425eda5d3877b47343e68ca23d0d0846f4d312ecaa445"},
|
||||
{file = "numpy-2.1.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:2ffef621c14ebb0188a8633348504a35c13680d6da93ab5cb86f4e54b7e922b5"},
|
||||
{file = "numpy-2.1.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:ad369ed238b1959dfbade9018a740fb9392c5ac4f9b5173f420bd4f37ba1f7a0"},
|
||||
{file = "numpy-2.1.2-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:d82075752f40c0ddf57e6e02673a17f6cb0f8eb3f587f63ca1eaab5594da5b17"},
|
||||
{file = "numpy-2.1.2-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:1600068c262af1ca9580a527d43dc9d959b0b1d8e56f8a05d830eea39b7c8af6"},
|
||||
{file = "numpy-2.1.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a26ae94658d3ba3781d5e103ac07a876b3e9b29db53f68ed7df432fd033358a8"},
|
||||
{file = "numpy-2.1.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13311c2db4c5f7609b462bc0f43d3c465424d25c626d95040f073e30f7570e35"},
|
||||
{file = "numpy-2.1.2-cp313-cp313t-musllinux_1_1_x86_64.whl", hash = "sha256:2abbf905a0b568706391ec6fa15161fad0fb5d8b68d73c461b3c1bab6064dd62"},
|
||||
{file = "numpy-2.1.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:ef444c57d664d35cac4e18c298c47d7b504c66b17c2ea91312e979fcfbdfb08a"},
|
||||
{file = "numpy-2.1.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:bdd407c40483463898b84490770199d5714dcc9dd9b792f6c6caccc523c00952"},
|
||||
{file = "numpy-2.1.2-pp310-pypy310_pp73-macosx_14_0_x86_64.whl", hash = "sha256:da65fb46d4cbb75cb417cddf6ba5e7582eb7bb0b47db4b99c9fe5787ce5d91f5"},
|
||||
{file = "numpy-2.1.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1c193d0b0238638e6fc5f10f1b074a6993cb13b0b431f64079a509d63d3aa8b7"},
|
||||
{file = "numpy-2.1.2-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a7d80b2e904faa63068ead63107189164ca443b42dd1930299e0d1cb041cec2e"},
|
||||
{file = "numpy-2.1.2.tar.gz", hash = "sha256:13532a088217fa624c99b843eeb54640de23b3414b14aa66d023805eb731066c"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "opencv-python"
|
||||
version = "4.10.0.84"
|
||||
description = "Wrapper package for OpenCV python bindings."
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "opencv-python-4.10.0.84.tar.gz", hash = "sha256:72d234e4582e9658ffea8e9cae5b63d488ad06994ef12d81dc303b17472f3526"},
|
||||
{file = "opencv_python-4.10.0.84-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:fc182f8f4cda51b45f01c64e4cbedfc2f00aff799debebc305d8d0210c43f251"},
|
||||
{file = "opencv_python-4.10.0.84-cp37-abi3-macosx_12_0_x86_64.whl", hash = "sha256:71e575744f1d23f79741450254660442785f45a0797212852ee5199ef12eed98"},
|
||||
{file = "opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:09a332b50488e2dda866a6c5573ee192fe3583239fb26ff2f7f9ceb0bc119ea6"},
|
||||
{file = "opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ace140fc6d647fbe1c692bcb2abce768973491222c067c131d80957c595b71f"},
|
||||
{file = "opencv_python-4.10.0.84-cp37-abi3-win32.whl", hash = "sha256:2db02bb7e50b703f0a2d50c50ced72e95c574e1e5a0bb35a8a86d0b35c98c236"},
|
||||
{file = "opencv_python-4.10.0.84-cp37-abi3-win_amd64.whl", hash = "sha256:32dbbd94c26f611dc5cc6979e6b7aa1f55a64d6b463cc1dcd3c95505a63e48fe"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = {version = ">=1.26.0", markers = "python_version >= \"3.12\""}
|
||||
|
||||
[[package]]
|
||||
name = "tqdm"
|
||||
version = "4.66.6"
|
||||
description = "Fast, Extensible Progress Meter"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tqdm-4.66.6-py3-none-any.whl", hash = "sha256:223e8b5359c2efc4b30555531f09e9f2f3589bcd7fdd389271191031b49b7a63"},
|
||||
{file = "tqdm-4.66.6.tar.gz", hash = "sha256:4bdd694238bef1485ce839d67967ab50af8f9272aab687c0d7702a01da0be090"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
colorama = {version = "*", markers = "platform_system == \"Windows\""}
|
||||
|
||||
[package.extras]
|
||||
dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
|
||||
notebook = ["ipywidgets (>=6)"]
|
||||
slack = ["slack-sdk"]
|
||||
telegram = ["requests"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.12"
|
||||
content-hash = "f93605d68b70d310dc7383c5e54252f753003732137baad292ec11a523042d66"
|
||||
16
pyproject.toml
Normal file
16
pyproject.toml
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
[tool.poetry]
|
||||
name = "traptools"
|
||||
version = "0.1.0"
|
||||
description = ""
|
||||
authors = ["Ruben van de Ven <git@rubenvandeven.com>"]
|
||||
readme = "README.md"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = "^3.12"
|
||||
opencv-python = "^4.10.0.84"
|
||||
tqdm = "^4.66.6"
|
||||
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
Loading…
Reference in a new issue