174 lines
6.0 KiB
Python
174 lines
6.0 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 collections import namedtuple
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from common import getsize, Bunch
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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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ar_verts = np.float32([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0],
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[0, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0, 1],
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[0, 0.5, 2], [1, 0.5, 2]])
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ar_edges = [(0, 1), (1, 2), (2, 3), (3, 0),
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(4, 5), (5, 6), (6, 7), (7, 4),
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(0, 4), (1, 5), (2, 6), (3, 7),
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(4, 8), (5, 8), (6, 9), (7, 9), (8, 9)]
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def draw_keypoints(vis, keypoints, color = (0, 255, 255)):
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for kp in keypoints:
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x, y = kp.pt
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cv2.circle(vis, (int(x), int(y)), 2, color)
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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.frame = None
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self.paused = False
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self.ref_frames = []
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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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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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matches = self.matcher.knnMatch(self.frame_desc, k = 2)
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matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * 0.75]
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if len(matches) < MIN_MATCH_COUNT:
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return
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img_ids = [m.imgIdx for m in matches]
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match_counts = np.bincount(img_ids, minlength=len(self.ref_frames))
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bast_id = match_counts.argmax()
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if match_counts[bast_id] < MIN_MATCH_COUNT:
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return
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ref_frame = self.ref_frames[bast_id]
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matches = [m for m in matches if m.imgIdx == bast_id]
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p0 = [ref_frame.points[m.trainIdx].pt for m in matches]
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p1 = [self.frame_points[m.queryIdx].pt for m in matches]
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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 ref_frame, p0, p1, H
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def on_frame(self, vis):
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match = self.match_frames()
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if match is None:
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return
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w, h = getsize(self.frame)
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ref_frame, p0, p1, H = match
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vis[:h,w:] = ref_frame.frame
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draw_keypoints(vis[:,w:], ref_frame.points)
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x0, y0, x1, y1 = ref_frame.rect
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cv2.rectangle(vis, (x0+w, y0), (x1+w, y1), (0, 255, 0), 2)
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corners0 = np.float32([[x0, y0], [x1, y0], [x1, y1], [x0, y1]])
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img_corners = cv2.perspectiveTransform(corners0.reshape(1, -1, 2), H)
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cv2.polylines(vis, [np.int32(img_corners)], True, (255, 255, 255), 2)
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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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'''
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corners3d = np.hstack([corners0, np.zeros((4, 1), np.float32)])
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fx = 0.9
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K = np.float64([[fx*w, 0, 0.5*(w-1)],
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[0, fx*w, 0.5*(h-1)],
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[0.0,0.0, 1.0]])
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dist_coef = np.zeros(4)
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ret, rvec, tvec = cv2.solvePnP(corners3d, img_corners, K, dist_coef)
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verts = ar_verts * [(x1-x0), (y1-y0), -(x1-x0)*0.3] + (x0, y0, 0)
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verts = cv2.projectPoints(verts, rvec, tvec, K, dist_coef)[0].reshape(-1, 2)
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for i, j in ar_edges:
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(x0, y0), (x1, y1) = verts[i], verts[j]
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cv2.line(vis, (int(x0), int(y0)), (int(x1), int(y1)), (255, 255, 0), 2)
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'''
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def on_rect(self, rect):
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x0, y0, x1, y1 = 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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descs = np.uint8(descs)
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frame_data = Bunch(frame = self.frame, rect=rect, points = points, descs=descs)
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self.ref_frames.append(frame_data)
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self.matcher.add([descs])
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def run(self):
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while True:
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playing = not self.paused and not self.rect_sel.dragging
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if playing or self.frame is None:
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ret, frame = self.cap.read()
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if not ret:
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break
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self.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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w, h = getsize(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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draw_keypoints(vis, self.frame_points)
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if playing:
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self.on_frame(vis)
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self.rect_sel.draw(vis)
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cv2.imshow('plane', vis)
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ch = cv2.waitKey(1)
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if ch == ord(' '):
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self.paused = not self.paused
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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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