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