mirror of
https://github.com/opencv/opencv.git
synced 2024-11-29 05:29:54 +08:00
138 lines
4.3 KiB
Python
138 lines
4.3 KiB
Python
'''
|
|
Feature homography
|
|
==================
|
|
|
|
Example of using features2d framework for interactive video homography matching.
|
|
ORB features and FLANN matcher are used.
|
|
|
|
Inspired by http://www.youtube.com/watch?v=-ZNYoL8rzPY
|
|
|
|
Usage
|
|
-----
|
|
feature_homography.py [<video source>]
|
|
|
|
Select a textured planar object to track by drawing a box with a mouse.
|
|
|
|
'''
|
|
|
|
import numpy as np
|
|
import cv2
|
|
import video
|
|
import common
|
|
from operator import attrgetter
|
|
|
|
def get_size(a):
|
|
h, w = a.shape[:2]
|
|
return w, h
|
|
|
|
|
|
FLANN_INDEX_KDTREE = 1
|
|
FLANN_INDEX_LSH = 6
|
|
flann_params= dict(algorithm = FLANN_INDEX_LSH,
|
|
table_number = 6, # 12
|
|
key_size = 12, # 20
|
|
multi_probe_level = 1) #2
|
|
|
|
|
|
MIN_MATCH_COUNT = 10
|
|
|
|
class App:
|
|
def __init__(self, src):
|
|
self.cap = video.create_capture(src)
|
|
self.ref_frame = None
|
|
|
|
self.detector = cv2.ORB( nfeatures = 1000 )
|
|
self.matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
|
|
|
|
cv2.namedWindow('plane')
|
|
self.rect_sel = common.RectSelector('plane', self.on_rect)
|
|
|
|
self.frame = None
|
|
|
|
def match_frames(self):
|
|
if len(self.frame_desc) < MIN_MATCH_COUNT or len(self.frame_desc) < MIN_MATCH_COUNT:
|
|
return
|
|
|
|
raw_matches = self.matcher.knnMatch(self.ref_descs, trainDescriptors = self.frame_desc, k = 2)
|
|
p0, p1 = [], []
|
|
for m in raw_matches:
|
|
if len(m) == 2 and m[0].distance < m[1].distance * 0.75:
|
|
m = m[0]
|
|
p0.append( self.ref_points[m.queryIdx].pt )
|
|
p1.append( self.frame_points[m.trainIdx].pt )
|
|
p0, p1 = np.float32((p0, p1))
|
|
if len(p0) < MIN_MATCH_COUNT:
|
|
return
|
|
|
|
H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 4.0)
|
|
status = status.ravel() != 0
|
|
if status.sum() < MIN_MATCH_COUNT:
|
|
return
|
|
p0, p1 = p0[status], p1[status]
|
|
return p0, p1, H
|
|
|
|
|
|
def on_frame(self, frame):
|
|
if self.frame is None or not self.rect_sel.dragging:
|
|
self.frame = frame = np.fliplr(frame).copy()
|
|
self.frame_points, self.frame_desc = self.detector.detectAndCompute(self.frame, None)
|
|
if self.frame_desc is None: # detectAndCompute returns descs=None if not keypoints found
|
|
self.frame_desc = []
|
|
else:
|
|
self.ref_frame = None
|
|
|
|
w, h = get_size(self.frame)
|
|
vis = np.zeros((h, w*2, 3), np.uint8)
|
|
vis[:h,:w] = self.frame
|
|
self.rect_sel.draw(vis)
|
|
|
|
if self.ref_frame is not None:
|
|
vis[:h,w:] = self.ref_frame
|
|
x0, y0, x1, y1 = self.ref_rect
|
|
cv2.rectangle(vis, (x0+w, y0), (x1+w, y1), (0, 255, 0), 2)
|
|
|
|
for kp in self.ref_points:
|
|
x, y = kp.pt
|
|
cv2.circle(vis, (int(x+w), int(y)), 2, (0, 255, 255))
|
|
|
|
|
|
match = self.match_frames()
|
|
if match is not None:
|
|
p0, p1, H = match
|
|
for (x0, y0), (x1, y1) in zip(np.int32(p0), np.int32(p1)):
|
|
cv2.line(vis, (x0+w, y0), (x1, y1), (0, 255, 0))
|
|
x0, y0, x1, y1 = self.ref_rect
|
|
corners = np.float32([[x0, y0], [x1, y0], [x1, y1], [x0, y1]])
|
|
corners = np.int32( cv2.perspectiveTransform(corners.reshape(1, -1, 2), H) )
|
|
cv2.polylines(vis, [corners], True, (255, 255, 255), 2)
|
|
|
|
cv2.imshow('plane', vis)
|
|
|
|
def on_rect(self, rect):
|
|
x0, y0, x1, y1 = rect
|
|
self.ref_frame = self.frame.copy()
|
|
self.ref_rect = rect
|
|
points, descs = [], []
|
|
for kp, desc in zip(self.frame_points, self.frame_desc):
|
|
x, y = kp.pt
|
|
if x0 <= x <= x1 and y0 <= y <= y1:
|
|
points.append(kp)
|
|
descs.append(desc)
|
|
self.ref_points, self.ref_descs = points, np.uint8(descs)
|
|
|
|
def run(self):
|
|
while True:
|
|
ret, frame = self.cap.read()
|
|
self.on_frame(frame)
|
|
ch = cv2.waitKey(1)
|
|
if ch == 27:
|
|
break
|
|
|
|
if __name__ == '__main__':
|
|
print __doc__
|
|
|
|
import sys
|
|
try: video_src = sys.argv[1]
|
|
except: video_src = '0'
|
|
App(video_src).run()
|