Open Source Computer Vision Library https://opencv.org/
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#!/usr/bin/env python
'''
Planar augmented reality
==================
This sample shows an example of augmented reality overlay over a planar object
tracked by PlaneTracker from plane_tracker.py. solvePnP funciton is used to
estimate the tracked object location in 3d space.
video: http://www.youtube.com/watch?v=pzVbhxx6aog
Usage
-----
plane_ar.py [<video source>]
Keys:
SPACE - pause video
c - clear targets
Select a textured planar object to track by drawing a box with a mouse.
Use 'focal' slider to adjust to camera focal length for proper video augmentation.
'''
import numpy as np
import cv2
import video
import common
from plane_tracker import PlaneTracker
ar_verts = np.float32([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0],
[0, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0, 1],
[0, 0.5, 2], [1, 0.5, 2]])
ar_edges = [(0, 1), (1, 2), (2, 3), (3, 0),
(4, 5), (5, 6), (6, 7), (7, 4),
(0, 4), (1, 5), (2, 6), (3, 7),
(4, 8), (5, 8), (6, 9), (7, 9), (8, 9)]
class App:
def __init__(self, src):
self.cap = video.create_capture(src)
self.frame = None
self.paused = False
self.tracker = PlaneTracker()
cv2.namedWindow('plane')
cv2.createTrackbar('focal', 'plane', 25, 50, common.nothing)
self.rect_sel = common.RectSelector('plane', self.on_rect)
def on_rect(self, rect):
self.tracker.add_target(self.frame, rect)
def run(self):
while True:
playing = not self.paused and not self.rect_sel.dragging
if playing or self.frame is None:
ret, frame = self.cap.read()
if not ret:
break
self.frame = frame.copy()
vis = self.frame.copy()
if playing:
tracked = self.tracker.track(self.frame)
for tr in tracked:
cv2.polylines(vis, [np.int32(tr.quad)], True, (255, 255, 255), 2)
for (x, y) in np.int32(tr.p1):
cv2.circle(vis, (x, y), 2, (255, 255, 255))
self.draw_overlay(vis, tr)
self.rect_sel.draw(vis)
cv2.imshow('plane', vis)
ch = cv2.waitKey(1)
if ch == ord(' '):
self.paused = not self.paused
if ch == ord('c'):
self.tracker.clear()
if ch == 27:
break
def draw_overlay(self, vis, tracked):
x0, y0, x1, y1 = tracked.target.rect
quad_3d = np.float32([[x0, y0, 0], [x1, y0, 0], [x1, y1, 0], [x0, y1, 0]])
fx = 0.5 + cv2.getTrackbarPos('focal', 'plane') / 50.0
h, w = vis.shape[:2]
K = np.float64([[fx*w, 0, 0.5*(w-1)],
[0, fx*w, 0.5*(h-1)],
[0.0,0.0, 1.0]])
dist_coef = np.zeros(4)
ret, rvec, tvec = cv2.solvePnP(quad_3d, tracked.quad, K, dist_coef)
verts = ar_verts * [(x1-x0), (y1-y0), -(x1-x0)*0.3] + (x0, y0, 0)
verts = cv2.projectPoints(verts, rvec, tvec, K, dist_coef)[0].reshape(-1, 2)
for i, j in ar_edges:
(x0, y0), (x1, y1) = verts[i], verts[j]
cv2.line(vis, (int(x0), int(y0)), (int(x1), int(y1)), (255, 255, 0), 2)
if __name__ == '__main__':
print __doc__
import sys
try: video_src = sys.argv[1]
except: video_src = 0
App(video_src).run()