!/usr/bin/env python

'''

Multitarget planar tracking

Example of using features2d framework for interactive video homography matching. ORB features and FLANN matcher are used. This sample provides PlaneTracker class and an example of its usage.

video: http://www.youtube.com/watch?v=pzVbhx...

Usage

plane_tracker.py [<video source="">]

Keys: SPACE - pause video c - clear targets

Select a textured planar object to track by drawing a box with a mouse. '''

import numpy as np import cv2

built-in modules

from collections import namedtuple

local modules

import video import common

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

''' image - image to track rect - tracked rectangle (x1, y1, x2, y2) keypoints - keypoints detected inside rect descrs - their descriptors data - some user-provided data ''' PlanarTarget = namedtuple('PlaneTarget', 'image, rect, keypoints, descrs, data')

''' target - reference to PlanarTarget p0 - matched points coords in target image p1 - matched points coords in input frame H - homography matrix from p0 to p1 quad - target bounary quad in input frame ''' TrackedTarget = namedtuple('TrackedTarget', 'target, p0, p1, H, quad')

class PlaneTracker: def __init__(self): self.detector = cv2.ORB_create( nfeatures = 1000 ) self.matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329) self.targets = []

def add_target(self, image, rect, data=None):
    '''Add a new tracking target.'''
    x0, y0, x1, y1 = rect
    raw_points, raw_descrs = self.detect_features(image)
    points, descs = [], []
    for kp, desc in zip(raw_points, raw_descrs):
        x, y = kp.pt
        if x0 <= x <= x1 and y0 <= y <= y1:
            points.append(kp)
            descs.append(desc)
    descs = np.uint8(descs)
    self.matcher.add([descs])
    target = PlanarTarget(image = image, rect=rect, keypoints = points, descrs=descs, data=data)
    self.targets.append(target)

def clear(self):
    '''Remove all targets'''
    self.targets = []
    self.matcher.clear()

def track(self, frame):
    '''Returns a list of detected TrackedTarget objects'''
    frame_points, frame_descrs = self.detect_features(frame)
    if len(frame_points) < MIN_MATCH_COUNT:
        return []
    matches = self.matcher.knnMatch(frame_descrs, k = 2)
    matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * 0.75]
    if len(matches) < MIN_MATCH_COUNT:
        return []
    matches_by_id = [[] for _ in xrange(len(self.targets))]
    for m in matches:
        matches_by_id[m.imgIdx].append(m)
    tracked = []
    for imgIdx, matches in enumerate(matches_by_id):
        if len(matches) < MIN_MATCH_COUNT:
            continue
        target = self.targets[imgIdx]
        p0 = [target.keypoints[m.trainIdx].pt for m in matches]
        p1 = [frame_points[m.queryIdx].pt for m in matches]
        p0, p1 = np.float32((p0, p1))
        H, status = cv2.findHomography(p0, p1, cv2.RANSAC, 3.0)
        status = status.ravel() != 0
        if status.sum() < MIN_MATCH_COUNT:
            continue
        p0, p1 = p0[status], p1[status]

        x0, y0, x1, y1 = target.rect
        quad = np.float32([[x0, y0], [x1, y0], [x1, y1], [x0, y1]])
        quad = cv2.perspectiveTransform(quad.reshape(1, -1, 2), H).reshape(-1, 2)

        track = TrackedTarget(target=target, p0=p0, p1=p1, H=H, quad=quad)
        tracked.append(track)
    tracked.sort(key = lambda t: len(t.p0), reverse=True)
    return tracked

def detect_features(self, frame):
    '''detect_features(self, frame) -> keypoints, descrs'''
    keypoints, descrs = self.detector.detectAndCompute(frame, None)
    if descrs is None:  # detectAndCompute returns ...