Experiment with the OpenCV digits.py example

master
neingeist 9 years ago
parent 16b142d94c
commit 47ba4b1b8a

3
.gitignore vendored

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DisplayImage DisplayImage
SharpenImage SharpenImage
*.pyc
digits_svm.dat

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#!/usr/bin/env python
'''
This module contains some common routines used by other samples.
'''
import numpy as np
import cv2
# built-in modules
import os
import itertools as it
from contextlib import contextmanager
image_extensions = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.pbm', '.pgm', '.ppm']
class Bunch(object):
def __init__(self, **kw):
self.__dict__.update(kw)
def __str__(self):
return str(self.__dict__)
def splitfn(fn):
path, fn = os.path.split(fn)
name, ext = os.path.splitext(fn)
return path, name, ext
def anorm2(a):
return (a*a).sum(-1)
def anorm(a):
return np.sqrt( anorm2(a) )
def homotrans(H, x, y):
xs = H[0, 0]*x + H[0, 1]*y + H[0, 2]
ys = H[1, 0]*x + H[1, 1]*y + H[1, 2]
s = H[2, 0]*x + H[2, 1]*y + H[2, 2]
return xs/s, ys/s
def to_rect(a):
a = np.ravel(a)
if len(a) == 2:
a = (0, 0, a[0], a[1])
return np.array(a, np.float64).reshape(2, 2)
def rect2rect_mtx(src, dst):
src, dst = to_rect(src), to_rect(dst)
cx, cy = (dst[1] - dst[0]) / (src[1] - src[0])
tx, ty = dst[0] - src[0] * (cx, cy)
M = np.float64([[ cx, 0, tx],
[ 0, cy, ty],
[ 0, 0, 1]])
return M
def lookat(eye, target, up = (0, 0, 1)):
fwd = np.asarray(target, np.float64) - eye
fwd /= anorm(fwd)
right = np.cross(fwd, up)
right /= anorm(right)
down = np.cross(fwd, right)
R = np.float64([right, down, fwd])
tvec = -np.dot(R, eye)
return R, tvec
def mtx2rvec(R):
w, u, vt = cv2.SVDecomp(R - np.eye(3))
p = vt[0] + u[:,0]*w[0] # same as np.dot(R, vt[0])
c = np.dot(vt[0], p)
s = np.dot(vt[1], p)
axis = np.cross(vt[0], vt[1])
return axis * np.arctan2(s, c)
def draw_str(dst, (x, y), s):
cv2.putText(dst, s, (x+1, y+1), cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 0), thickness = 2, lineType=cv2.LINE_AA)
cv2.putText(dst, s, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (255, 255, 255), lineType=cv2.LINE_AA)
class Sketcher:
def __init__(self, windowname, dests, colors_func):
self.prev_pt = None
self.windowname = windowname
self.dests = dests
self.colors_func = colors_func
self.dirty = False
self.show()
cv2.setMouseCallback(self.windowname, self.on_mouse)
def show(self):
cv2.imshow(self.windowname, self.dests[0])
def on_mouse(self, event, x, y, flags, param):
pt = (x, y)
if event == cv2.EVENT_LBUTTONDOWN:
self.prev_pt = pt
elif event == cv2.EVENT_LBUTTONUP:
self.prev_pt = None
if self.prev_pt and flags & cv2.EVENT_FLAG_LBUTTON:
for dst, color in zip(self.dests, self.colors_func()):
cv2.line(dst, self.prev_pt, pt, color, 5)
self.dirty = True
self.prev_pt = pt
self.show()
# palette data from matplotlib/_cm.py
_jet_data = {'red': ((0., 0, 0), (0.35, 0, 0), (0.66, 1, 1), (0.89,1, 1),
(1, 0.5, 0.5)),
'green': ((0., 0, 0), (0.125,0, 0), (0.375,1, 1), (0.64,1, 1),
(0.91,0,0), (1, 0, 0)),
'blue': ((0., 0.5, 0.5), (0.11, 1, 1), (0.34, 1, 1), (0.65,0, 0),
(1, 0, 0))}
cmap_data = { 'jet' : _jet_data }
def make_cmap(name, n=256):
data = cmap_data[name]
xs = np.linspace(0.0, 1.0, n)
channels = []
eps = 1e-6
for ch_name in ['blue', 'green', 'red']:
ch_data = data[ch_name]
xp, yp = [], []
for x, y1, y2 in ch_data:
xp += [x, x+eps]
yp += [y1, y2]
ch = np.interp(xs, xp, yp)
channels.append(ch)
return np.uint8(np.array(channels).T*255)
def nothing(*arg, **kw):
pass
def clock():
return cv2.getTickCount() / cv2.getTickFrequency()
@contextmanager
def Timer(msg):
print msg, '...',
start = clock()
try:
yield
finally:
print "%.2f ms" % ((clock()-start)*1000)
class StatValue:
def __init__(self, smooth_coef = 0.5):
self.value = None
self.smooth_coef = smooth_coef
def update(self, v):
if self.value is None:
self.value = v
else:
c = self.smooth_coef
self.value = c * self.value + (1.0-c) * v
class RectSelector:
def __init__(self, win, callback):
self.win = win
self.callback = callback
cv2.setMouseCallback(win, self.onmouse)
self.drag_start = None
self.drag_rect = None
def onmouse(self, event, x, y, flags, param):
x, y = np.int16([x, y]) # BUG
if event == cv2.EVENT_LBUTTONDOWN:
self.drag_start = (x, y)
if self.drag_start:
if flags & cv2.EVENT_FLAG_LBUTTON:
xo, yo = self.drag_start
x0, y0 = np.minimum([xo, yo], [x, y])
x1, y1 = np.maximum([xo, yo], [x, y])
self.drag_rect = None
if x1-x0 > 0 and y1-y0 > 0:
self.drag_rect = (x0, y0, x1, y1)
else:
rect = self.drag_rect
self.drag_start = None
self.drag_rect = None
if rect:
self.callback(rect)
def draw(self, vis):
if not self.drag_rect:
return False
x0, y0, x1, y1 = self.drag_rect
cv2.rectangle(vis, (x0, y0), (x1, y1), (0, 255, 0), 2)
return True
@property
def dragging(self):
return self.drag_rect is not None
def grouper(n, iterable, fillvalue=None):
'''grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx'''
args = [iter(iterable)] * n
return it.izip_longest(fillvalue=fillvalue, *args)
def mosaic(w, imgs):
'''Make a grid from images.
w -- number of grid columns
imgs -- images (must have same size and format)
'''
imgs = iter(imgs)
img0 = imgs.next()
pad = np.zeros_like(img0)
imgs = it.chain([img0], imgs)
rows = grouper(w, imgs, pad)
return np.vstack(map(np.hstack, rows))
def getsize(img):
h, w = img.shape[:2]
return w, h
def mdot(*args):
return reduce(np.dot, args)
def draw_keypoints(vis, keypoints, color = (0, 255, 255)):
for kp in keypoints:
x, y = kp.pt
cv2.circle(vis, (int(x), int(y)), 2, color)

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#!/usr/bin/env python
'''
SVM and KNearest digit recognition.
Sample loads a dataset of handwritten digits from '../data/digits.png'.
Then it trains a SVM and KNearest classifiers on it and evaluates
their accuracy.
Following preprocessing is applied to the dataset:
- Moment-based image deskew (see deskew())
- Digit images are split into 4 10x10 cells and 16-bin
histogram of oriented gradients is computed for each
cell
- Transform histograms to space with Hellinger metric (see [1] (RootSIFT))
[1] R. Arandjelovic, A. Zisserman
"Three things everyone should know to improve object retrieval"
http://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf
Usage:
digits.py
'''
# built-in modules
from multiprocessing.pool import ThreadPool
import cv2
import numpy as np
from numpy.linalg import norm
# local modules
from common import clock, mosaic
SZ = 20 # size of each digit is SZ x SZ
CLASS_N = 10
DIGITS_FN = 'digits.png'
def split2d(img, cell_size, flatten=True):
h, w = img.shape[:2]
sx, sy = cell_size
cells = [np.hsplit(row, w//sx) for row in np.vsplit(img, h//sy)]
cells = np.array(cells)
if flatten:
cells = cells.reshape(-1, sy, sx)
return cells
def load_digits(fn):
print 'loading "%s" ...' % fn
digits_img = cv2.imread(fn, 0)
digits = split2d(digits_img, (SZ, SZ))
labels = np.repeat(np.arange(CLASS_N), len(digits)/CLASS_N)
return digits, labels
def deskew(img):
m = cv2.moments(img)
if abs(m['mu02']) < 1e-2:
return img.copy()
skew = m['mu11']/m['mu02']
M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
return img
class StatModel(object):
def load(self, fn):
self.model.load(fn)
def save(self, fn):
self.model.save(fn)
class KNearest(StatModel):
def __init__(self, k = 3):
self.k = k
self.model = cv2.KNearest()
def train(self, samples, responses):
self.model = cv2.KNearest()
self.model.train(samples, responses)
def predict(self, samples):
retval, results, neigh_resp, dists = self.model.find_nearest(samples, self.k)
return results.ravel()
class SVM(StatModel):
def __init__(self, kernel_type=cv2.SVM_RBF, C=1, gamma=0.5):
self.params = dict( kernel_type = kernel_type,
svm_type = cv2.SVM_C_SVC,
C = C,
gamma = gamma )
self.model = cv2.SVM()
def train(self, samples, responses):
self.model = cv2.SVM()
self.model.train(samples, responses, params = self.params)
def train_auto(self, samples, responses):
self.model = cv2.SVM()
self.model.train_auto(samples, responses, None, None, params = self.params)
def predict(self, samples):
return self.model.predict_all(samples).ravel()
def evaluate_model(model, digits, samples, labels):
resp = model.predict(samples)
err = (labels != resp).mean()
print 'error: %.2f %%' % (err*100)
confusion = np.zeros((10, 10), np.int32)
for i, j in zip(labels, resp):
confusion[i, j] += 1
print 'confusion matrix:'
print confusion
print
vis = []
for img, flag in zip(digits, resp == labels):
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if not flag:
img[...,:2] = 0
vis.append(img)
return mosaic(25, vis)
def preprocess_simple(digits):
return np.float32(digits).reshape(-1, SZ*SZ) / 255.0
def preprocess_hog(digits):
samples = []
for img in digits:
gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)
gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
mag, ang = cv2.cartToPolar(gx, gy)
bin_n = 16
bin = np.int32(bin_n*ang/(2*np.pi))
bin_cells = bin[:10,:10], bin[10:,:10], bin[:10,10:], bin[10:,10:]
mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]
hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
hist = np.hstack(hists)
# transform to Hellinger kernel
eps = 1e-7
hist /= hist.sum() + eps
hist = np.sqrt(hist)
hist /= norm(hist) + eps
samples.append(hist)
return np.float32(samples)
if __name__ == '__main__':
print __doc__
digits, labels = load_digits(DIGITS_FN)
print 'preprocessing...'
# shuffle digits
rand = np.random.RandomState(321)
shuffle = rand.permutation(len(digits))
digits, labels = digits[shuffle], labels[shuffle]
digits2 = map(deskew, digits)
samples = preprocess_simple(digits2)
#samples = preprocess_hog(digits2)
train_n = int(0.9*len(samples))
cv2.imshow('test set', mosaic(25, digits[train_n:]))
digits_train, digits_test = np.split(digits2, [train_n])
samples_train, samples_test = np.split(samples, [train_n])
labels_train, labels_test = np.split(labels, [train_n])
print 'training KNearest...'
model = KNearest(k=4)
model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv2.imshow('KNearest test', vis)
print 'training SVM...'
# HOG (original digits.py)
#model = SVM(kernel_type=cv2.SVM_RBF, C=2.67, gamma=5.383)
#model.train(samples_train, labels_train)
# Simple (cross-validation)
model = SVM(kernel_type=cv2.SVM_LINEAR, C=0.1)
model.train(samples_train, labels_train)
vis = evaluate_model(model, digits_test, samples_test, labels_test)
cv2.imshow('SVM test', vis)
print 'saving SVM as "digits_svm.dat"...'
model.save('digits_svm.dat')
cv2.waitKey(0)
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