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mouse problem in python 2 examples Ubuntu 12.04

asked 2013-08-01 06:46:44 -0500

Rupert gravatar image

Hi,

I'm experiencing a problem with any of the python2 examples that use the mouse under Ubuntu 12.04 64-bit (camshift.py, plane_tracker, watershed...) As written, the examples don't respond to the mouse - it seems because lines of this type

if flags & cv2.EVENT_FLAG_LBUTTON

aren't doing what they are supposed to.

Is anyone else experiencing this problem? I have not been able to test on other platforms yet. The problem occurs with 2.4.4 and 2.4.6.1 for me.

A workaround is to restructure the python code to check for button click events. I'd be happy to submit that patch. Alternately, there may be something fishy in the way these flags are being handled on Ubuntu.

best, Rupert

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Comments

I'm having the same problem. How did you fix the code?

camolin3 gravatar imagecamolin3 ( 2013-11-10 22:12:53 -0500 )edit
1

Placing a return statement after setting the self.drag_start to (x, y) fix the problem. It seems that 'flags' doesn't notify that the left click was pressed on the first time.

camolin3 gravatar imagecamolin3 ( 2013-11-10 22:33:30 -0500 )edit

Thank you, that is correct ;)

dvsaraiva gravatar imagedvsaraiva ( 2015-07-28 08:04:52 -0500 )edit

hey rupert , could u submit that workaround of yours as i am stuck in the same problem

vasu12360 gravatar imagevasu12360 ( 2016-08-12 06:26:39 -0500 )edit

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answered 2015-12-11 02:38:00 -0500

aman gravatar image

!/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 ...
(more)
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Asked: 2013-08-01 06:46:44 -0500

Seen: 892 times

Last updated: Aug 01 '13