mirror of https://github.com/opencv/opencv.git
Vadim Pisarevsky
12 years ago
parent
2c2d6fa5fd
commit
2013118971
4 changed files with 574 additions and 368 deletions
@ -1,200 +1,212 @@ |
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import numpy as np |
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import cv2 |
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import os |
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from contextlib import contextmanager |
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import itertools as it |
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|
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image_extensions = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.pbm', '.pgm', '.ppm'] |
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|
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def splitfn(fn): |
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path, fn = os.path.split(fn) |
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name, ext = os.path.splitext(fn) |
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return path, name, ext |
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|
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def anorm2(a): |
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return (a*a).sum(-1) |
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def anorm(a): |
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return np.sqrt( anorm2(a) ) |
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|
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def homotrans(H, x, y): |
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xs = H[0, 0]*x + H[0, 1]*y + H[0, 2] |
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ys = H[1, 0]*x + H[1, 1]*y + H[1, 2] |
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s = H[2, 0]*x + H[2, 1]*y + H[2, 2] |
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return xs/s, ys/s |
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|
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def to_rect(a): |
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a = np.ravel(a) |
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if len(a) == 2: |
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a = (0, 0, a[0], a[1]) |
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return np.array(a, np.float64).reshape(2, 2) |
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|
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def rect2rect_mtx(src, dst): |
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src, dst = to_rect(src), to_rect(dst) |
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cx, cy = (dst[1] - dst[0]) / (src[1] - src[0]) |
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tx, ty = dst[0] - src[0] * (cx, cy) |
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M = np.float64([[ cx, 0, tx], |
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[ 0, cy, ty], |
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[ 0, 0, 1]]) |
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return M |
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|
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|
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def lookat(eye, target, up = (0, 0, 1)): |
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fwd = np.asarray(target, np.float64) - eye |
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fwd /= anorm(fwd) |
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right = np.cross(fwd, up) |
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right /= anorm(right) |
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down = np.cross(fwd, right) |
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R = np.float64([right, down, fwd]) |
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tvec = -np.dot(R, eye) |
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return R, tvec |
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|
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def mtx2rvec(R): |
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w, u, vt = cv2.SVDecomp(R - np.eye(3)) |
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p = vt[0] + u[:,0]*w[0] # same as np.dot(R, vt[0]) |
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c = np.dot(vt[0], p) |
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s = np.dot(vt[1], p) |
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axis = np.cross(vt[0], vt[1]) |
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return axis * np.arctan2(s, c) |
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|
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def draw_str(dst, (x, y), s): |
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cv2.putText(dst, s, (x+1, y+1), cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 0), thickness = 2, lineType=cv2.CV_AA) |
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cv2.putText(dst, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (255, 255, 255), lineType=cv2.CV_AA) |
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|
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class Sketcher: |
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def __init__(self, windowname, dests, colors_func): |
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self.prev_pt = None |
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self.windowname = windowname |
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self.dests = dests |
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self.colors_func = colors_func |
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self.dirty = False |
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self.show() |
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cv2.setMouseCallback(self.windowname, self.on_mouse) |
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|
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def show(self): |
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cv2.imshow(self.windowname, self.dests[0]) |
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|
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def on_mouse(self, event, x, y, flags, param): |
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pt = (x, y) |
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if event == cv2.EVENT_LBUTTONDOWN: |
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self.prev_pt = pt |
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if self.prev_pt and flags & cv2.EVENT_FLAG_LBUTTON: |
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for dst, color in zip(self.dests, self.colors_func()): |
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cv2.line(dst, self.prev_pt, pt, color, 5) |
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self.dirty = True |
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self.prev_pt = pt |
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self.show() |
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else: |
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self.prev_pt = None |
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|
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|
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# palette data from matplotlib/_cm.py |
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_jet_data = {'red': ((0., 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89,1, 1), |
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(1, 0.5, 0.5)), |
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'green': ((0., 0, 0), (0.125,0, 0), (0.375,1, 1), (0.64,1, 1), |
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(0.91,0,0), (1, 0, 0)), |
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'blue': ((0., 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65,0, 0), |
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(1, 0, 0))} |
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|
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cmap_data = { 'jet' : _jet_data } |
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|
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def make_cmap(name, n=256): |
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data = cmap_data[name] |
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xs = np.linspace(0.0, 1.0, n) |
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channels = [] |
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eps = 1e-6 |
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for ch_name in ['blue', 'green', 'red']: |
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ch_data = data[ch_name] |
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xp, yp = [], [] |
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for x, y1, y2 in ch_data: |
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xp += [x, x+eps] |
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yp += [y1, y2] |
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ch = np.interp(xs, xp, yp) |
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channels.append(ch) |
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return np.uint8(np.array(channels).T*255) |
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|
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def nothing(*arg, **kw): |
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pass |
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|
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def clock(): |
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return cv2.getTickCount() / cv2.getTickFrequency() |
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|
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@contextmanager |
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def Timer(msg): |
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print msg, '...', |
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start = clock() |
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try: |
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yield |
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finally: |
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print "%.2f ms" % ((clock()-start)*1000) |
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|
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class StatValue: |
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def __init__(self, smooth_coef = 0.5): |
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self.value = None |
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self.smooth_coef = smooth_coef |
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def update(self, v): |
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if self.value is None: |
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self.value = v |
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else: |
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c = self.smooth_coef |
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self.value = c * self.value + (1.0-c) * v |
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|
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class RectSelector: |
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def __init__(self, win, callback): |
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self.win = win |
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self.callback = callback |
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cv2.setMouseCallback(win, self.onmouse) |
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self.drag_start = None |
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self.drag_rect = None |
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def onmouse(self, event, x, y, flags, param): |
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x, y = np.int16([x, y]) # BUG |
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if event == cv2.EVENT_LBUTTONDOWN: |
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self.drag_start = (x, y) |
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if self.drag_start: |
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if flags & cv2.EVENT_FLAG_LBUTTON: |
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xo, yo = self.drag_start |
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x0, y0 = np.minimum([xo, yo], [x, y]) |
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x1, y1 = np.maximum([xo, yo], [x, y]) |
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self.drag_rect = None |
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if x1-x0 > 0 and y1-y0 > 0: |
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self.drag_rect = (x0, y0, x1, y1) |
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else: |
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rect = self.drag_rect |
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self.drag_start = None |
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self.drag_rect = None |
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if rect: |
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self.callback(rect) |
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def draw(self, vis): |
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if not self.drag_rect: |
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return False |
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x0, y0, x1, y1 = self.drag_rect |
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cv2.rectangle(vis, (x0, y0), (x1, y1), (0, 255, 0), 2) |
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return True |
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@property |
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def dragging(self): |
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return self.drag_rect is not None |
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|
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|
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def grouper(n, iterable, fillvalue=None): |
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'''grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx''' |
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args = [iter(iterable)] * n |
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return it.izip_longest(fillvalue=fillvalue, *args) |
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|
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def mosaic(w, imgs): |
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'''Make a grid from images. |
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|
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w -- number of grid columns |
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imgs -- images (must have same size and format) |
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''' |
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imgs = iter(imgs) |
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img0 = imgs.next() |
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pad = np.zeros_like(img0) |
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imgs = it.chain([img0], imgs) |
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rows = grouper(w, imgs, pad) |
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return np.vstack(map(np.hstack, rows)) |
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def getsize(img): |
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h, w = img.shape[:2] |
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return w, h |
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|
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def mdot(*args): |
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return reduce(np.dot, args) |
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import numpy as np |
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import cv2 |
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import os |
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from contextlib import contextmanager |
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import itertools as it |
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image_extensions = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.pbm', '.pgm', '.ppm'] |
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|
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class Bunch(object): |
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def __init__(self, **kw): |
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self.__dict__.update(kw) |
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def __str__(self): |
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return str(self.__dict__) |
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|
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def splitfn(fn): |
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path, fn = os.path.split(fn) |
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name, ext = os.path.splitext(fn) |
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return path, name, ext |
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|
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def anorm2(a): |
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return (a*a).sum(-1) |
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def anorm(a): |
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return np.sqrt( anorm2(a) ) |
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|
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def homotrans(H, x, y): |
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xs = H[0, 0]*x + H[0, 1]*y + H[0, 2] |
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ys = H[1, 0]*x + H[1, 1]*y + H[1, 2] |
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s = H[2, 0]*x + H[2, 1]*y + H[2, 2] |
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return xs/s, ys/s |
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|
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def to_rect(a): |
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a = np.ravel(a) |
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if len(a) == 2: |
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a = (0, 0, a[0], a[1]) |
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return np.array(a, np.float64).reshape(2, 2) |
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|
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def rect2rect_mtx(src, dst): |
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src, dst = to_rect(src), to_rect(dst) |
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cx, cy = (dst[1] - dst[0]) / (src[1] - src[0]) |
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tx, ty = dst[0] - src[0] * (cx, cy) |
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M = np.float64([[ cx, 0, tx], |
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[ 0, cy, ty], |
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[ 0, 0, 1]]) |
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return M |
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|
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|
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def lookat(eye, target, up = (0, 0, 1)): |
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fwd = np.asarray(target, np.float64) - eye |
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fwd /= anorm(fwd) |
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right = np.cross(fwd, up) |
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right /= anorm(right) |
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down = np.cross(fwd, right) |
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R = np.float64([right, down, fwd]) |
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tvec = -np.dot(R, eye) |
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return R, tvec |
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|
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def mtx2rvec(R): |
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w, u, vt = cv2.SVDecomp(R - np.eye(3)) |
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p = vt[0] + u[:,0]*w[0] # same as np.dot(R, vt[0]) |
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c = np.dot(vt[0], p) |
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s = np.dot(vt[1], p) |
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axis = np.cross(vt[0], vt[1]) |
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return axis * np.arctan2(s, c) |
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|
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def draw_str(dst, (x, y), s): |
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cv2.putText(dst, s, (x+1, y+1), cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 0), thickness = 2, lineType=cv2.CV_AA) |
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cv2.putText(dst, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (255, 255, 255), lineType=cv2.CV_AA) |
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|
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class Sketcher: |
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def __init__(self, windowname, dests, colors_func): |
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self.prev_pt = None |
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self.windowname = windowname |
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self.dests = dests |
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self.colors_func = colors_func |
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self.dirty = False |
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self.show() |
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cv2.setMouseCallback(self.windowname, self.on_mouse) |
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def show(self): |
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cv2.imshow(self.windowname, self.dests[0]) |
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|
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def on_mouse(self, event, x, y, flags, param): |
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pt = (x, y) |
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if event == cv2.EVENT_LBUTTONDOWN: |
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self.prev_pt = pt |
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if self.prev_pt and flags & cv2.EVENT_FLAG_LBUTTON: |
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for dst, color in zip(self.dests, self.colors_func()): |
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cv2.line(dst, self.prev_pt, pt, color, 5) |
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self.dirty = True |
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self.prev_pt = pt |
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self.show() |
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else: |
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self.prev_pt = None |
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|
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|
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# palette data from matplotlib/_cm.py |
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_jet_data = {'red': ((0., 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89,1, 1), |
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(1, 0.5, 0.5)), |
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'green': ((0., 0, 0), (0.125,0, 0), (0.375,1, 1), (0.64,1, 1), |
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(0.91,0,0), (1, 0, 0)), |
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'blue': ((0., 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65,0, 0), |
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(1, 0, 0))} |
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|
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cmap_data = { 'jet' : _jet_data } |
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|
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def make_cmap(name, n=256): |
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data = cmap_data[name] |
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xs = np.linspace(0.0, 1.0, n) |
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channels = [] |
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eps = 1e-6 |
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for ch_name in ['blue', 'green', 'red']: |
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ch_data = data[ch_name] |
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xp, yp = [], [] |
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for x, y1, y2 in ch_data: |
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xp += [x, x+eps] |
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yp += [y1, y2] |
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ch = np.interp(xs, xp, yp) |
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channels.append(ch) |
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return np.uint8(np.array(channels).T*255) |
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|
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def nothing(*arg, **kw): |
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pass |
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|
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def clock(): |
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return cv2.getTickCount() / cv2.getTickFrequency() |
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|
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@contextmanager |
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def Timer(msg): |
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print msg, '...', |
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start = clock() |
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try: |
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yield |
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finally: |
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print "%.2f ms" % ((clock()-start)*1000) |
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|
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class StatValue: |
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def __init__(self, smooth_coef = 0.5): |
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self.value = None |
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self.smooth_coef = smooth_coef |
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def update(self, v): |
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if self.value is None: |
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self.value = v |
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else: |
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c = self.smooth_coef |
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self.value = c * self.value + (1.0-c) * v |
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|
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class RectSelector: |
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def __init__(self, win, callback): |
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self.win = win |
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self.callback = callback |
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cv2.setMouseCallback(win, self.onmouse) |
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self.drag_start = None |
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self.drag_rect = None |
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def onmouse(self, event, x, y, flags, param): |
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x, y = np.int16([x, y]) # BUG |
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if event == cv2.EVENT_LBUTTONDOWN: |
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self.drag_start = (x, y) |
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if self.drag_start: |
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if flags & cv2.EVENT_FLAG_LBUTTON: |
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xo, yo = self.drag_start |
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x0, y0 = np.minimum([xo, yo], [x, y]) |
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x1, y1 = np.maximum([xo, yo], [x, y]) |
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self.drag_rect = None |
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if x1-x0 > 0 and y1-y0 > 0: |
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self.drag_rect = (x0, y0, x1, y1) |
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else: |
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rect = self.drag_rect |
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self.drag_start = None |
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self.drag_rect = None |
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if rect: |
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self.callback(rect) |
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def draw(self, vis): |
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if not self.drag_rect: |
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return False |
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x0, y0, x1, y1 = self.drag_rect |
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cv2.rectangle(vis, (x0, y0), (x1, y1), (0, 255, 0), 2) |
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return True |
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@property |
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def dragging(self): |
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return self.drag_rect is not None |
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|
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|
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def grouper(n, iterable, fillvalue=None): |
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'''grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx''' |
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args = [iter(iterable)] * n |
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return it.izip_longest(fillvalue=fillvalue, *args) |
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|
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def mosaic(w, imgs): |
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'''Make a grid from images. |
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|
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w -- number of grid columns |
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imgs -- images (must have same size and format) |
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''' |
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imgs = iter(imgs) |
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img0 = imgs.next() |
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pad = np.zeros_like(img0) |
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imgs = it.chain([img0], imgs) |
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rows = grouper(w, imgs, pad) |
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return np.vstack(map(np.hstack, rows)) |
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|
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def getsize(img): |
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h, w = img.shape[:2] |
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return w, h |
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def mdot(*args): |
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return reduce(np.dot, args) |
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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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@ -1,168 +1,88 @@ |
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''' |
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Feature homography |
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================== |
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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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|
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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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|
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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 |
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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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[0.5, 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, 8), (7, 8)] |
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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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|
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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_frame = None |
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|
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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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|
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cv2.namedWindow('plane') |
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self.rect_sel = common.RectSelector('plane', self.on_rect) |
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|
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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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|
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raw_matches = self.matcher.knnMatch(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.trainIdx].pt ) # queryIdx |
||||
p1.append( self.frame_points[m.queryIdx].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, vis): |
||||
match = self.match_frames() |
||||
if match is None: |
||||
return |
||||
w, h = getsize(self.frame) |
||||
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 |
||||
corners0 = np.float32([[x0, y0], [x1, y0], [x1, y1], [x0, y1]]) |
||||
img_corners = cv2.perspectiveTransform(corners0.reshape(1, -1, 2), H) |
||||
cv2.polylines(vis, [np.int32(img_corners)], True, (255, 255, 255), 2) |
||||
|
||||
corners3d = np.hstack([corners0, np.zeros((4, 1), np.float32)]) |
||||
fx = 0.9 |
||||
K = np.float64([[fx*w, 0, 0.5*(w-1)], |
||||
[0, fx*w, 0.5*(h-1)], |
||||
[0.0,0.0, 1.0]]) |
||||
dist_coef = np.zeros(4) |
||||
ret, rvec, tvec = cv2.solvePnP(corners3d, img_corners, K, dist_coef) |
||||
verts = ar_verts * [(x1-x0), (y1-y0), -(x1-x0)*0.3] + (x0, y0, 0) |
||||
verts = cv2.projectPoints(verts, rvec, tvec, K, dist_coef)[0].reshape(-1, 2) |
||||
for i, j in ar_edges: |
||||
(x0, y0), (x1, y1) = verts[i], verts[j] |
||||
cv2.line(vis, (int(x0), int(y0)), (int(x1), int(y1)), (255, 255, 0), 2) |
||||
|
||||
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) |
||||
|
||||
self.matcher.clear() |
||||
self.matcher.add([self.ref_descs]) |
||||
|
||||
def run(self): |
||||
while True: |
||||
playing = not self.paused and not self.rect_sel.dragging |
||||
if playing or self.frame is None: |
||||
ret, frame = self.cap.read() |
||||
if not ret: |
||||
break |
||||
self.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 = [] |
||||
|
||||
w, h = getsize(self.frame) |
||||
vis = np.zeros((h, w*2, 3), np.uint8) |
||||
vis[:h,:w] = self.frame |
||||
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) |
||||
draw_keypoints(vis[:,w:], self.ref_points) |
||||
draw_keypoints(vis, self.frame_points) |
||||
|
||||
if playing and self.ref_frame is not None: |
||||
self.on_frame(vis) |
||||
|
||||
self.rect_sel.draw(vis) |
||||
cv2.imshow('plane', vis) |
||||
ch = cv2.waitKey(1) |
||||
if ch == ord(' '): |
||||
self.paused = not self.paused |
||||
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() |
||||
''' |
||||
Feature homography |
||||
================== |
||||
|
||||
Example of using features2d framework for interactive video homography matching. |
||||
ORB features and FLANN matcher are used. The actual tracking is implemented by |
||||
PlaneTracker class in plane_tracker.py |
||||
|
||||
Inspired by http://www.youtube.com/watch?v=-ZNYoL8rzPY |
||||
|
||||
video: http://www.youtube.com/watch?v=FirtmYcC0Vc |
||||
|
||||
Usage |
||||
----- |
||||
feature_homography.py [<video source>] |
||||
|
||||
Keys: |
||||
SPACE - pause video |
||||
|
||||
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 common import getsize, draw_keypoints |
||||
from plane_tracker import PlaneTracker |
||||
|
||||
|
||||
class App: |
||||
def __init__(self, src): |
||||
self.cap = video.create_capture(src) |
||||
self.frame = None |
||||
self.paused = False |
||||
self.tracker = PlaneTracker() |
||||
|
||||
cv2.namedWindow('plane') |
||||
self.rect_sel = common.RectSelector('plane', self.on_rect) |
||||
|
||||
def on_rect(self, rect): |
||||
self.tracker.clear() |
||||
self.tracker.add_target(self.frame, rect) |
||||
|
||||
def run(self): |
||||
while True: |
||||
playing = not self.paused and not self.rect_sel.dragging |
||||
if playing or self.frame is None: |
||||
ret, frame = self.cap.read() |
||||
if not ret: |
||||
break |
||||
self.frame = np.frame.copy() |
||||
|
||||
w, h = getsize(self.frame) |
||||
vis = np.zeros((h, w*2, 3), np.uint8) |
||||
vis[:h,:w] = self.frame |
||||
if len(self.tracker.targets) > 0: |
||||
target = self.tracker.targets[0] |
||||
vis[:,w:] = target.image |
||||
draw_keypoints(vis[:,w:], target.keypoints) |
||||
x0, y0, x1, y1 = target.rect |
||||
cv2.rectangle(vis, (x0+w, y0), (x1+w, y1), (0, 255, 0), 2) |
||||
|
||||
if playing: |
||||
tracked = self.tracker.track(self.frame) |
||||
if len(tracked) > 0: |
||||
tracked = tracked[0] |
||||
cv2.polylines(vis, [np.int32(tracked.quad)], True, (255, 255, 255), 2) |
||||
for (x0, y0), (x1, y1) in zip(np.int32(tracked.p0), np.int32(tracked.p1)): |
||||
cv2.line(vis, (x0+w, y0), (x1, y1), (0, 255, 0)) |
||||
draw_keypoints(vis, self.tracker.frame_points) |
||||
|
||||
self.rect_sel.draw(vis) |
||||
cv2.imshow('plane', vis) |
||||
ch = cv2.waitKey(1) |
||||
if ch == ord(' '): |
||||
self.paused = not self.paused |
||||
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() |
||||
|
||||
@ -0,0 +1,103 @@ |
||||
''' |
||||
Planar augmented reality |
||||
================== |
||||
|
||||
This sample shows an example of augmented reality overlay over a planar object |
||||
tracked by PlaneTracker from plane_tracker.py. solvePnP funciton is used to |
||||
estimate the tracked object location in 3d space. |
||||
|
||||
video: http://www.youtube.com/watch?v=pzVbhxx6aog |
||||
|
||||
Usage |
||||
----- |
||||
plane_ar.py [<video source>] |
||||
|
||||
Keys: |
||||
SPACE - pause video |
||||
c - clear targets |
||||
|
||||
Select a textured planar object to track by drawing a box with a mouse. |
||||
Use 'focal' slider to adjust to camera focal length for proper video augmentation. |
||||
''' |
||||
|
||||
import numpy as np |
||||
import cv2 |
||||
import video |
||||
import common |
||||
from plane_tracker import PlaneTracker |
||||
|
||||
|
||||
ar_verts = np.float32([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0], |
||||
[0, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0, 1], |
||||
[0, 0.5, 2], [1, 0.5, 2]]) |
||||
ar_edges = [(0, 1), (1, 2), (2, 3), (3, 0), |
||||
(4, 5), (5, 6), (6, 7), (7, 4), |
||||
(0, 4), (1, 5), (2, 6), (3, 7), |
||||
(4, 8), (5, 8), (6, 9), (7, 9), (8, 9)] |
||||
|
||||
class App: |
||||
def __init__(self, src): |
||||
self.cap = video.create_capture(src) |
||||
self.frame = None |
||||
self.paused = False |
||||
self.tracker = PlaneTracker() |
||||
|
||||
cv2.namedWindow('plane') |
||||
cv2.createTrackbar('focal', 'plane', 25, 50, common.nothing) |
||||
self.rect_sel = common.RectSelector('plane', self.on_rect) |
||||
|
||||
def on_rect(self, rect): |
||||
self.tracker.add_target(self.frame, rect) |
||||
|
||||
def run(self): |
||||
while True: |
||||
playing = not self.paused and not self.rect_sel.dragging |
||||
if playing or self.frame is None: |
||||
ret, frame = self.cap.read() |
||||
if not ret: |
||||
break |
||||
self.frame = frame.copy() |
||||
|
||||
vis = self.frame.copy() |
||||
if playing: |
||||
tracked = self.tracker.track(self.frame) |
||||
for tr in tracked: |
||||
cv2.polylines(vis, [np.int32(tr.quad)], True, (255, 255, 255), 2) |
||||
for (x, y) in np.int32(tr.p1): |
||||
cv2.circle(vis, (x, y), 2, (255, 255, 255)) |
||||
self.draw_overlay(vis, tr) |
||||
|
||||
self.rect_sel.draw(vis) |
||||
cv2.imshow('plane', vis) |
||||
ch = cv2.waitKey(1) |
||||
if ch == ord(' '): |
||||
self.paused = not self.paused |
||||
if ch == ord('c'): |
||||
self.tracker.clear() |
||||
if ch == 27: |
||||
break |
||||
|
||||
def draw_overlay(self, vis, tracked): |
||||
x0, y0, x1, y1 = tracked.target.rect |
||||
quad_3d = np.float32([[x0, y0, 0], [x1, y0, 0], [x1, y1, 0], [x0, y1, 0]]) |
||||
fx = 0.5 + cv2.getTrackbarPos('focal', 'plane') / 50.0 |
||||
h, w = vis.shape[:2] |
||||
K = np.float64([[fx*w, 0, 0.5*(w-1)], |
||||
[0, fx*w, 0.5*(h-1)], |
||||
[0.0,0.0, 1.0]]) |
||||
dist_coef = np.zeros(4) |
||||
ret, rvec, tvec = cv2.solvePnP(quad_3d, tracked.quad, K, dist_coef) |
||||
verts = ar_verts * [(x1-x0), (y1-y0), -(x1-x0)*0.3] + (x0, y0, 0) |
||||
verts = cv2.projectPoints(verts, rvec, tvec, K, dist_coef)[0].reshape(-1, 2) |
||||
for i, j in ar_edges: |
||||
(x0, y0), (x1, y1) = verts[i], verts[j] |
||||
cv2.line(vis, (int(x0), int(y0)), (int(x1), int(y1)), (255, 255, 0), 2) |
||||
|
||||
|
||||
if __name__ == '__main__': |
||||
print __doc__ |
||||
|
||||
import sys |
||||
try: video_src = sys.argv[1] |
||||
except: video_src = 0 |
||||
App(video_src).run() |
||||
@ -0,0 +1,171 @@ |
||||
''' |
||||
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=pzVbhxx6aog |
||||
|
||||
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 |
||||
from collections import namedtuple |
||||
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( 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=None) |
||||
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''' |
||||
self.frame_points, self.frame_descrs = self.detect_features(frame) |
||||
if len(self.frame_points) < MIN_MATCH_COUNT: |
||||
return [] |
||||
matches = self.matcher.knnMatch(self.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 = [self.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 descs=None if not keypoints found |
||||
descrs = [] |
||||
return keypoints, descrs |
||||
|
||||
|
||||
class App: |
||||
def __init__(self, src): |
||||
self.cap = video.create_capture(src) |
||||
self.frame = None |
||||
self.paused = False |
||||
self.tracker = PlaneTracker() |
||||
|
||||
cv2.namedWindow('plane') |
||||
self.rect_sel = common.RectSelector('plane', self.on_rect) |
||||
|
||||
def on_rect(self, rect): |
||||
self.tracker.add_target(self.frame, rect) |
||||
|
||||
def run(self): |
||||
while True: |
||||
playing = not self.paused and not self.rect_sel.dragging |
||||
if playing or self.frame is None: |
||||
ret, frame = self.cap.read() |
||||
if not ret: |
||||
break |
||||
self.frame = frame.copy() |
||||
|
||||
vis = self.frame.copy() |
||||
if playing: |
||||
tracked = self.tracker.track(self.frame) |
||||
for tr in tracked: |
||||
cv2.polylines(vis, [np.int32(tr.quad)], True, (255, 255, 255), 2) |
||||
for (x, y) in np.int32(tr.p1): |
||||
cv2.circle(vis, (x, y), 2, (255, 255, 255)) |
||||
|
||||
self.rect_sel.draw(vis) |
||||
cv2.imshow('plane', vis) |
||||
ch = cv2.waitKey(1) |
||||
if ch == ord(' '): |
||||
self.paused = not self.paused |
||||
if ch == ord('c'): |
||||
self.tracker.clear() |
||||
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() |
||||
Loading…
Reference in new issue