Experiment with the OpenCV digits.py example
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.gitignore
vendored
3
.gitignore
vendored
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@ -10,3 +10,6 @@ SVMTest.png
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DisplayImage
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SharpenImage
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*.pyc
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digits_svm.dat
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220
common.py
Executable file
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common.py
Executable file
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#!/usr/bin/env python
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'''
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This module contains some common routines used by other samples.
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'''
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import numpy as np
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import cv2
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# built-in modules
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import os
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import itertools as it
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from contextlib import contextmanager
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image_extensions = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.pbm', '.pgm', '.ppm']
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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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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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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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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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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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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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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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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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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.LINE_AA)
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cv2.putText(dst, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (255, 255, 255), lineType=cv2.LINE_AA)
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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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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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elif event == cv2.EVENT_LBUTTONUP:
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self.prev_pt = None
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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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# 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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cmap_data = { 'jet' : _jet_data }
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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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def nothing(*arg, **kw):
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pass
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def clock():
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return cv2.getTickCount() / cv2.getTickFrequency()
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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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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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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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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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def mosaic(w, imgs):
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'''Make a grid from images.
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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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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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BIN
digits.png
Normal file
BIN
digits.png
Normal file
Binary file not shown.
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After Width: | Height: | Size: 704 KiB |
195
digits.py
Executable file
195
digits.py
Executable file
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#!/usr/bin/env python
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'''
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SVM and KNearest digit recognition.
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Sample loads a dataset of handwritten digits from '../data/digits.png'.
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Then it trains a SVM and KNearest classifiers on it and evaluates
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their accuracy.
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Following preprocessing is applied to the dataset:
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- Moment-based image deskew (see deskew())
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- Digit images are split into 4 10x10 cells and 16-bin
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histogram of oriented gradients is computed for each
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cell
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- Transform histograms to space with Hellinger metric (see [1] (RootSIFT))
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[1] R. Arandjelovic, A. Zisserman
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"Three things everyone should know to improve object retrieval"
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http://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf
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Usage:
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digits.py
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'''
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# built-in modules
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from multiprocessing.pool import ThreadPool
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import cv2
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import numpy as np
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from numpy.linalg import norm
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# local modules
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from common import clock, mosaic
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SZ = 20 # size of each digit is SZ x SZ
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CLASS_N = 10
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DIGITS_FN = 'digits.png'
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def split2d(img, cell_size, flatten=True):
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h, w = img.shape[:2]
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sx, sy = cell_size
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cells = [np.hsplit(row, w//sx) for row in np.vsplit(img, h//sy)]
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cells = np.array(cells)
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if flatten:
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cells = cells.reshape(-1, sy, sx)
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return cells
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def load_digits(fn):
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print 'loading "%s" ...' % fn
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digits_img = cv2.imread(fn, 0)
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digits = split2d(digits_img, (SZ, SZ))
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labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
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return digits, labels
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def deskew(img):
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m = cv2.moments(img)
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if abs(m['mu02']) < 1e-2:
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return img.copy()
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skew = m['mu11']/m['mu02']
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M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
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img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
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return img
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class StatModel(object):
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def load(self, fn):
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self.model.load(fn)
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def save(self, fn):
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self.model.save(fn)
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class KNearest(StatModel):
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def __init__(self, k = 3):
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self.k = k
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self.model = cv2.KNearest()
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def train(self, samples, responses):
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self.model = cv2.KNearest()
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self.model.train(samples, responses)
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def predict(self, samples):
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retval, results, neigh_resp, dists = self.model.find_nearest(samples, self.k)
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return results.ravel()
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class SVM(StatModel):
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def __init__(self, kernel_type=cv2.SVM_RBF, C=1, gamma=0.5):
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self.params = dict( kernel_type = kernel_type,
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svm_type = cv2.SVM_C_SVC,
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C = C,
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gamma = gamma )
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self.model = cv2.SVM()
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def train(self, samples, responses):
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self.model = cv2.SVM()
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self.model.train(samples, responses, params = self.params)
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def train_auto(self, samples, responses):
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self.model = cv2.SVM()
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self.model.train_auto(samples, responses, None, None, params = self.params)
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def predict(self, samples):
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return self.model.predict_all(samples).ravel()
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def evaluate_model(model, digits, samples, labels):
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resp = model.predict(samples)
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err = (labels != resp).mean()
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print 'error: %.2f %%' % (err*100)
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confusion = np.zeros((10, 10), np.int32)
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for i, j in zip(labels, resp):
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confusion[i, j] += 1
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print 'confusion matrix:'
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print confusion
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print
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vis = []
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for img, flag in zip(digits, resp == labels):
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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if not flag:
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img[...,:2] = 0
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vis.append(img)
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return mosaic(25, vis)
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def preprocess_simple(digits):
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return np.float32(digits).reshape(-1, SZ*SZ) / 255.0
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def preprocess_hog(digits):
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samples = []
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for img in digits:
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gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
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gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
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mag, ang = cv2.cartToPolar(gx, gy)
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bin_n = 16
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bin = np.int32(bin_n*ang/(2*np.pi))
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bin_cells = bin[:10,:10], bin[10:,:10], bin[:10,10:], bin[10:,10:]
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mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]
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hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
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hist = np.hstack(hists)
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# transform to Hellinger kernel
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eps = 1e-7
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hist /= hist.sum() + eps
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hist = np.sqrt(hist)
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hist /= norm(hist) + eps
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samples.append(hist)
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return np.float32(samples)
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if __name__ == '__main__':
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print __doc__
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digits, labels = load_digits(DIGITS_FN)
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print 'preprocessing...'
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# shuffle digits
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rand = np.random.RandomState(321)
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shuffle = rand.permutation(len(digits))
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digits, labels = digits[shuffle], labels[shuffle]
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digits2 = map(deskew, digits)
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samples = preprocess_simple(digits2)
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#samples = preprocess_hog(digits2)
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train_n = int(0.9*len(samples))
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cv2.imshow('test set', mosaic(25, digits[train_n:]))
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digits_train, digits_test = np.split(digits2, [train_n])
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samples_train, samples_test = np.split(samples, [train_n])
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labels_train, labels_test = np.split(labels, [train_n])
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print 'training KNearest...'
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model = KNearest(k=4)
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model.train(samples_train, labels_train)
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vis = evaluate_model(model, digits_test, samples_test, labels_test)
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cv2.imshow('KNearest test', vis)
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print 'training SVM...'
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# HOG (original digits.py)
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#model = SVM(kernel_type=cv2.SVM_RBF, C=2.67, gamma=5.383)
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#model.train(samples_train, labels_train)
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# Simple (cross-validation)
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model = SVM(kernel_type=cv2.SVM_LINEAR, C=0.1)
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model.train(samples_train, labels_train)
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vis = evaluate_model(model, digits_test, samples_test, labels_test)
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cv2.imshow('SVM test', vis)
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print 'saving SVM as "digits_svm.dat"...'
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model.save('digits_svm.dat')
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cv2.waitKey(0)
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