def rotate_bdd(X_train, deg):
HOG_train = np.zeros((len(X_train), 324))
for i in range(len(X_train)):
image = X_train.ix[i].values.reshape(3, 32, 32).transpose(1, 2, 0)
im = rotate(image, deg)
HOG_train[i, :] = hog(im)
return HOG_train
def flip_bdd(X_train):
HOG_train = np.zeros((len(X_train), 324))
for i in range(len(X_train)):
image = X_train.ix[i].values.reshape(3, 32, 32).transpose(1, 2, 0)
im = flip(image, 1)
HOG_train[i, :] = hog(im)
return HOG_train
def fit(self, X, y, pix=False):
'''
Fit model to the observation (X,y) of detection
'''
y = np.array(y)
X_feat = []
AR = [[] for i in range(self.K)]
for i, im in enumerate(X):
ai = im.size[0] * 1. / im.size[1]
if y[i] < self.K:
AR[y[i]].append(ai)
im_r = im.resize((22, 20))
if not pix:
X_feat.append(hog(im_r, 4).reshape((-1, )))
else:
im_r = im_r.convert('L')
im_a = np.array(im_r.getdata()).reshape((-1, ))
X_feat.append(im_a)
for i in range(self.K):
y_feat = (y == i)
self.model[i].fit(X_feat, y_feat)
self.AR = np.array([[np.mean(AR[i]), np.var(AR[i])]
for i in range(self.K)])
def fit(self, X, y, pix=False):
'''
Fit model to the observation (X,y) of detection
'''
y = np.array(y)
X_feat = []
AR = [[] for i in range(self.K)]
for i, im in enumerate(X):
ai = im.size[0]*1./im.size[1]
if y[i] < self.K:
AR[y[i]].append(ai)
im_r = im.resize((22,20))
if not pix:
X_feat.append(hog(im_r,4).reshape((-1,)))
else:
im_r = im_r.convert('L')
im_a = np.array(im_r.getdata()).reshape((-1,))
X_feat.append(im_a)
for i in range(self.K):
y_feat = (y==i)
self.model[i].fit(X_feat, y_feat)
self.AR = np.array([[np.mean(AR[i]),np.var(AR[i])]
for i in range(self.K)])
def detect(gray, frame, model):
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
#label = model.predict(lbp_hist(gray[y:y+h, x:x+w] ,25,8).reshape(1,-1))
label = model.predict(hog(cv2.resize(gray[y:y+h, x:x+w], (150,150))).reshape(1,-1))
cv2.putText(frame, label[0], (x+w,y-5), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,0), 2, cv2.LINE_AA)
#print(label)
return frame
def detection(self, im, th, th1=0.1, pix=False):
'''
Perform the sliding window detection on im
return a list with (x, y, class, proba)
'''
w = self.width
h = self.height
a = w * 1. / h
w0 = im.size[0] - w
h0 = im.size[1] - h
res = []
Ntot = w0 * h0
for i in range(w0):
for j in range(h0):
sys.stdout.write('\rChar detect...{:6.2%}'.format(
(i * h0 + j) * 1. / Ntot))
sys.stdout.flush()
X = im.crop((i, j, i + w, j + h))
X_r = X.resize((22, 20))
X_feat = []
if not pix:
X_feat.append(hog(X_r, 4).reshape((-1, )))
else:
im_r = im_r.convert('L')
im_a = np.array(X_r.getdata()).reshape((-1, ))
X_feat.append(im_a)
p = []
for k in range(self.K):
p.append(self.model[k].predict_proba(X_feat)[0, 1])
cj = np.argmax(p)
muj, sigj = self.AR[cj]
GS = p[cj] * np.exp(-(muj - a)**2 / (2 * sigj))
if GS > th1:
res.append((i, j, p, cj, p[cj], GS))
print '\rChar detect...done '
return self.NMS(res, th)
def detection(self, im, th, th1=0.1, pix=False):
'''
Perform the sliding window detection on im
return a list with (x, y, class, proba)
'''
w = self.width
h = self.height
a = w*1./h
w0 = im.size[0] - w
h0 = im.size[1] - h
res = []
Ntot = w0*h0
for i in range(w0):
for j in range(h0):
sys.stdout.write('\rChar detect...{:6.2%}'.format(
(i*h0+j)*1./Ntot))
sys.stdout.flush()
X = im.crop((i,j,i+w,j+h))
X_r = X.resize((22,20))
X_feat = []
if not pix:
X_feat.append(hog(X_r,4).reshape((-1,)))
else:
im_r = im_r.convert('L')
im_a = np.array(X_r.getdata()).reshape((-1,))
X_feat.append(im_a)
p = []
for k in range(self.K):
p.append(self.model[k].predict_proba(X_feat)[0,1])
cj = np.argmax(p)
muj,sigj = self.AR[cj]
GS = p[cj]*np.exp(-(muj-a)**2/(2*sigj))
if GS > th1:
res.append((i,j,p, cj, p[cj], GS))
print '\rChar detect...done '
return self.NMS(res, th)
def test(self, X, y, pix=False):
'''
Test model on the observation (X,y)
'''
y = np.array(y)
X_feat = []
AR = []
for i, im in enumerate(X):
ai = im.size[0] * 1. / im.size[1]
AR.append(ai)
im_r = im.resize((22, 20))
if not pix:
X_feat.append(hog(im_r, 4).reshape((-1, )))
else:
im_r = im_r.convert('L')
im_a = np.array(im_r.getdata()).reshape((-1, ))
X_feat.append(im_a)
p = []
for k in range(self.K):
p.append(self.model[k].predict_proba(X_feat)[:, 1])
p = np.array(p)
i0 = p.argmax(axis=0)
AR = np.array(AR)
detect = np.exp(-(self.AR[i0, 0] - AR[i0])**2 / (2 * self.AR[i0, 1]))
GS = np.multiply(p[i0, range(y.shape[0])], detect)
err2 = ((y - i0).nonzero()[0].shape[0]) * 1. / y.shape[0]
err = 1 - (GS > 0.1).mean()
print('\nThis model miss {:6.2%} of the'
' character in the test db').format(err)
print(
'This model failed to recognize {:6.2%} character '
'of the test db').format(err2)
def test(self, X, y, pix=False):
'''
Test model on the observation (X,y)
'''
y = np.array(y)
X_feat = []
AR = []
for i, im in enumerate(X):
ai = im.size[0]*1./im.size[1]
AR.append(ai)
im_r = im.resize((22,20))
if not pix:
X_feat.append(hog(im_r,4).reshape((-1,)))
else:
im_r = im_r.convert('L')
im_a = np.array(im_r.getdata()).reshape((-1,))
X_feat.append(im_a)
p = []
for k in range(self.K):
p.append(self.model[k].predict_proba(X_feat)[:,1])
p = np.array(p)
i0 = p.argmax(axis=0)
AR = np.array(AR)
detect = np.exp(-(self.AR[i0,0]-AR[i0])**2/(2*self.AR[i0,1]))
GS = np.multiply(p[i0, range(y.shape[0])], detect)
err2 = ((y-i0).nonzero()[0].shape[0])*1./y.shape[0]
err = 1- (GS>0.1).mean()
print ('\nThis model miss {:6.2%} of the'
' character in the test db').format(err)
print ('This model failed to recognize {:6.2%} character '
'of the test db').format(err2)
import features
import cv2
import numpy as np
import matplotlib.pyplot as plt
im1 = cv2.imread('output/000045.png')
template = cv2.imread('output/template.png')
template = cv2.cvtColor(template, cv2.COLOR_BGR2RGB)
im1 = im1[im1.shape[0] - 720:, im1.shape[1] - 720:, :]
template = template[50:350, 50:350, :]
print(template.shape, im1.shape)
tmp = features.hog(template)
img = features.hog(im1)
print(img.shape, tmp.shape)
r, c, _ = tmp.shape
sse = np.zeros(img.shape[:2])
# print
for i in range(img.shape[0] - r):
for j in range(img.shape[1] - c):
sse[i, j] = np.sqrt(
np.square(img[i:i + r, j:j + c, :] - tmp).sum(axis=(0, 1))).sum()
tgt = np.argmin(sse[:img.shape[0] - r, :img.shape[1] - c])
match = np.unravel_index(tgt, (img.shape[0] - r, img.shape[1] - c))
print(match)
a = plt.figure()
ax = a.add_subplot(121)
print(result_CV)
print("Groundtruth: ")
print(Yval)
precision = np.sum(Yval == result_CV) / len(result_CV)
print("Precision: %f" % precision)
print("Time: ", time.time() - temps)
print('____________________')
# Compute HOG vector on testing set
HOG_test = np.zeros((len(X_test), 324))
for i in range(len(X_test)):
im = X_test.ix[i].values.reshape(3, 32, 32).transpose(1, 2, 0)
# imGray = rgb2gray(im)
# HOG_test[i, :] = hog(imGray, cells_per_block=(2,2))
HOG_test[i, :] = hog(im)
Xtrain = HOG_train
Ytrain = Y_train['Prediction'].as_matrix()
Xtest = HOG_test
print("Fitting")
parameters = one_vs_all(Xtrain, Ytrain, C, kernel)
print('____________________')
print("Predicting")
temps = time.time()
number_of_classes = len(np.unique(Ytrain))
result = predict_multiclass(Xtest, number_of_classes, parameters)
backgrounds = []
for k, frame in enumerate(frames[0:100:10]):
im = frame[:360].astype('double') / 255.0
#im = skimage.color.rgb2hsv((im * 255).astype('uint8'))
#plt.imshow(im[:, :, 0], interpolation = 'NONE', cmap = plt.cm.hsv);
#plt.colorbar()
#plt.show()
#plt.imshow(im[:, :, 1], interpolation = 'NONE', cmap = plt.cm.hsv);
#plt.colorbar()
#plt.show()
#plt.imshow(im[:, :, 2], interpolation = 'NONE', cmap = plt.cm.hsv);
#plt.colorbar()
#plt.show()
tmp = time.time()
hog = features.hogpad(features.hog(im, b))
rgb = features.rgbhist(im, b)
print time.time() - tmp
#plt.imshow(im, interpolation = 'NONE')
#plt.show()
#plt.imshow(skimage.color.rgb2gray(im), interpolation = 'NONE', cmap = plt.cm.gray)
#plt.imshow(rgb, interpolation = 'NONE')
#plt.show()
Y = rgb.shape[0]
X = rgb.shape[1]
rgbf = numpy.zeros((rgb.shape[0], rgb.shape[1], sy * sx * rgb.shape[2]))
indices[k] = []
def hog(self, mode='dense', algorithm='dalaltriggs', num_bins=9,
cell_size=8, block_size=2, signed_gradient=True, l2_norm_clip=0.2,
window_height=1, window_width=1, window_unit='blocks',
window_step_vertical=1, window_step_horizontal=1,
window_step_unit='pixels', padding=True, verbose=False,
constrain_landmarks=True):
r"""
Represents a 2-dimensional HOG features image with C number of
channels. The output object's class is either MaskedImage or Image
depending on the input image.
Parameters
----------
mode : 'dense' or 'sparse'
The 'sparse' case refers to the traditional usage of HOGs, so
default parameters values are passed to the ImageWindowIterator.
The sparse case of 'dalaltriggs' algorithm sets the window height
and width equal to block size and the window step horizontal and
vertical equal to cell size. Thse sparse case of 'zhuramanan'
algorithm sets the window height and width equal to 3 times the
cell size and the window step horizontal and vertical equal to cell
size. In the 'dense' case, the user can change the
ImageWindowIterator related parameters (window_height,
window_width, window_unit, window_step_vertical,
window_step_horizontal, window_step_unit, padding).
Default: 'dense'
window_height : float
Defines the height of the window for the ImageWindowIterator
object. The metric unit is defined by window_unit.
Default: 1
window_width : float
Defines the width of the window for the ImageWindowIterator object.
The metric unit is defined by window_unit.
Default: 1
window_unit : 'blocks' or 'pixels'
Defines the metric unit of the window_height and window_width
parameters for the ImageWindowIterator object.
Default: 'blocks'
window_step_vertical : float
Defines the vertical step by which the window in the
ImageWindowIterator is moved, thus it controls the features
density. The metric unit is defined by window_step_unit.
Default: 1
window_step_horizontal : float
Defines the horizontal step by which the window in the
ImageWindowIterator is moved, thus it controls the features
density. The metric unit is defined by window_step_unit.
Default: 1
window_step_unit : 'pixels' or 'cells'
Defines the metric unit of the window_step_vertical and
window_step_horizontal parameters for the ImageWindowIterator
object.
Default: 'pixels'
padding : bool
Enables/disables padding for the close-to-boundary windows in the
ImageWindowIterator object. When padding is enabled, the
out-of-boundary pixels are set to zero.
Default: True
algorithm : 'dalaltriggs' or 'zhuramanan'
Specifies the algorithm used to compute HOGs.
Default: 'dalaltriggs'
cell_size : float
Defines the cell size in pixels. This value is set to both the
width and height of the cell. This option is valid for both
algorithms.
Default: 8
block_size : float
Defines the block size in cells. This value is set to both the
width and height of the block. This option is valid only for the
'dalaltriggs' algorithm.
Default: 2
num_bins : float
Defines the number of orientation histogram bins. This option is
valid only for the 'dalaltriggs' algorithm.
Default: 9
signed_gradient : bool
Flag that defines whether we use signed or unsigned gradient
angles. This option is valid only for the 'dalaltriggs' algorithm.
Default: True
l2_norm_clip : float
Defines the clipping value of the gradients' L2-norm. This option
is valid only for the 'dalaltriggs' algorithm.
Default: 0.2
constrain_landmarks : bool
Flag that if enabled, it constrains landmarks that ended up outside
of the features image bounds.
Default: True
verbose : bool
Flag to print HOG related information.
Default: False
Raises
-------
ValueError
HOG features mode must be either dense or sparse
ValueError
Algorithm must be either dalaltriggs or zhuramanan
ValueError
Number of orientation bins must be > 0
ValueError
Cell size (in pixels) must be > 0
ValueError
Block size (in cells) must be > 0
ValueError
Value for L2-norm clipping must be > 0.0
ValueError
Window height must be >= block size and <= image height
ValueError
Window width must be >= block size and <= image width
ValueError
Window unit must be either pixels or blocks
ValueError
Horizontal window step must be > 0
ValueError
Vertical window step must be > 0
ValueError
Window step unit must be either pixels or cells
"""
# compute hog features and windows_centres
hog, window_centres = fc.hog(self._image.pixels, mode=mode,
algorithm=algorithm, cell_size=cell_size,
block_size=block_size, num_bins=num_bins,
signed_gradient=signed_gradient,
l2_norm_clip=l2_norm_clip,
window_height=window_height,
window_width=window_width,
window_unit=window_unit,
window_step_vertical=window_step_vertical,
window_step_horizontal=
window_step_horizontal,
window_step_unit=window_step_unit,
padding=padding, verbose=verbose)
# create hog image object
hog_image = self._init_feature_image(hog,
window_centres=window_centres,
constrain_landmarks=
constrain_landmarks)
# store parameters
hog_image.hog_parameters = {'mode': mode, 'algorithm': algorithm,
'num_bins': num_bins,
'cell_size': cell_size,
'block_size': block_size,
'signed_gradient': signed_gradient,
'l2_norm_clip': l2_norm_clip,
'window_height': window_height,
'window_width': window_width,
'window_unit': window_unit,
'window_step_vertical':
window_step_vertical,
'window_step_horizontal':
window_step_horizontal,
'window_step_unit': window_step_unit,
'padding': padding,
'original_image_height':
self._image.pixels.shape[0],
'original_image_width':
self._image.pixels.shape[1],
'original_image_channels':
self._image.pixels.shape[2]}
return hog_image
# loop over the images in each sub-folder
for file in images:
# get the image file name
file_path = os.path.join(current_dir, file)
# read the image and resize it to a fixed-size
image = cv2.imread(file_path)
image = cv2.resize(image, fixed_size)
####################################
# Global Feature extraction
####################################
fv_histogram = fd_haralick(image)
fv_kaze = kaze_func(image)
fv_hog = hog(image)
fv_fast = fd_Fast(image)
fv_lin = lin(image)
fv_ORB = fd_ORB(image)
#fv_hu_moments = fd_hu_moments(image)
# #fv_4 = fd_4(image)
###################################
# Concatenate global features
###################################
global_feature = np.hstack([fv_histogram, fv_kaze])
# update the list of labels and feature vectors
labels.append(current_label)
global_features.append(global_feature)
print("[STATUS] processed folder: {}".format(current_label))
#label = model.predict(lbp_hist(gray[y:y+h, x:x+w] ,25,8).reshape(1,-1))
label = model.predict(hog(cv2.resize(gray[y:y+h, x:x+w], (150,150))).reshape(1,-1))
cv2.putText(frame, label[0], (x+w,y-5), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,0), 2, cv2.LINE_AA)
#print(label)
return frame
if args['train'] is not None:
path = args["train"]
data_paths = [os.path.join(r, n) for r, _, f in os.walk(path) for n in f]
#data = np.asarray([lbp_hist(i ,25,8) for i in tqdm(data_paths)])
data = np.asarray([hog(i) for i in tqdm(data_paths)])
labels = np.asarray([i.split('\\')[1] for i in tqdm(data_paths)])
pkl.dump([data,labels], open('/data_array_hog.pkl', 'wb'))
# data, labels = pkl.load(open('/data_array.pkl', 'rb'))
arr = np.random.permutation(len(labels))
data = data[arr]
labels = labels[arr]
scaler = StandardScaler()
scaler.fit(data)
#scaler.transform(data)
x_train, x_test, y_train, y_test = train_test_split(data, labels, train_size = 0.7, random_state = 42)
