116 lines
3.6 KiB
Python
116 lines
3.6 KiB
Python
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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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