def getHOG3(imgs, ori=8, ppc=(4, 4), cpb=(4, 4)):
# determine the shape of the output
fd = hog(imgs[0, :, :, 0], orientations=ori, pixels_per_cell=ppc, cells_per_block=cpb, visualise=False)
# print fd.shape
hogs = np.zeros((imgs.shape[0], fd.shape[0] * 3))
# HOG
for i in range(imgs.shape[0]):
# zimgs[i,:] = exposure.equalize_hist(imgs[i,:])
# imgs[i,:] = rank.equalize(imgs[i,:]/255,selem=disk(0))
# plt.imshow(imgs[i,:]),plt.show()
hogs[i, 0 : fd.shape[0]] = hog(
imgs[i, :, :, 0], orientations=ori, pixels_per_cell=ppc, cells_per_block=cpb, visualise=False
)
hogs[i, fd.shape[0] : (2 * fd.shape[0])] = hog(
imgs[i, :, :, 1], orientations=ori, pixels_per_cell=ppc, cells_per_block=cpb, visualise=False
)
hogs[i, 2 * fd.shape[0] : (3 * fd.shape[0])] = hog(
imgs[i, :, :, 2], orientations=ori, pixels_per_cell=ppc, cells_per_block=cpb, visualise=False
)
sys.stdout.write("\rIteration {0}/{1}".format((i + 1), imgs.shape[0]))
sys.stdout.flush()
mean = np.mean(hogs, axis=0)
hogs -= mean
return hogs
def extract_features():
des_type = 'HOG'
# If feature directories don't exist, create them
if not os.path.isdir(pos_feat_ph):
os.makedirs(pos_feat_ph)
# If feature directories don't exist, create them
if not os.path.isdir(neg_feat_ph):
os.makedirs(neg_feat_ph)
print "Calculating the descriptors for the positive samples and saving them"
for im_path in glob.glob(os.path.join(pos_im_path, "*")):
#print im_path
im = imread(im_path, as_grey=True)
if des_type == "HOG":
fd = hog(im, orientations, pixels_per_cell, cells_per_block, visualize, normalize)
fd_name = os.path.split(im_path)[1].split(".")[0] + ".feat"
fd_path = os.path.join(pos_feat_ph, fd_name)
joblib.dump(fd, fd_path)
print "Positive features saved in {}".format(pos_feat_ph)
print "Calculating the descriptors for the negative samples and saving them"
for im_path in glob.glob(os.path.join(neg_im_path, "*")):
im = imread(im_path, as_grey=True)
if des_type == "HOG":
fd = hog(im, orientations, pixels_per_cell, cells_per_block, visualize, normalize)
fd_name = os.path.split(im_path)[1].split(".")[0] + ".feat"
fd_path = os.path.join(neg_feat_ph, fd_name)
joblib.dump(fd, fd_path)
print "Negative features saved in {}".format(neg_feat_ph)
print "Completed calculating features from training images"
def SingleDecisionTreeClassifier(pix):
print "\nCreating HOG Dataset from MNIST Data"
start_time = time.time()
training_image_data_hog = [hog(img, orientations=9, pixels_per_cell=(pix,pix), cells_per_block=(3, 3))
for img in training_image_data]
testing_image_data_hog = [hog(img, orientations=9, pixels_per_cell=(pix, pix), cells_per_block=(3, 3))
for img in testing_image_data]
end_time = time.time() - start_time
print "It took "+ str(end_time) + " to make the HOG Images"
print '\nTraining data'
start_time = time.time()
single_decision_tree_classifier = DecisionTreeClassifier()
single_decision_tree_classifier.fit(training_image_data_hog, training_label_data)
end_time = time.time() - start_time
print "It took "+ str(end_time) + " to train the classifier"
print 'Training Completed'
print '\nTesting data '
start_time = time.time()
single_decision_tree_classifier_accuracy = single_decision_tree_classifier.score(testing_image_data_hog, testing_label_data)
end_time = time.time() - start_time
print "It took "+ str(end_time) + " to test the data "
#
print '\n# printing Accuracy'
print "\nTesting for Single Decision Tree Classifier with pixels per cell = ("+str(pix)+','+str(pix)+') :'
print "-------------------------------------------------"
print "\nSingleDecisionTreeClassifier accuracy for ("+str(pix)+','+str(pix)+") : "+ str(single_decision_tree_classifier_accuracy)
return single_decision_tree_classifier_accuracy
def extract(self, image):
features = np.array([])
vec = []
if 'raw' in self.features:
vec = image.flatten()
features = np.append(features, vec)
vec = []
if 'textons' in self.features:
import gen_histogram as tx
vec = np.array(tx.histogram(image, self.centers))
features = np.append(features, vec)
vec = []
if 'hog' in self.features:
vec = hog(image, cells_per_block=(3, 3))
vec = np.append(vec, hog(image, cells_per_block=(4, 4)))
vec = np.append(vec, hog(image, cells_per_block=(1, 1)))
vec = np.append(vec, hog(image, cells_per_block=(2, 2)))
features = np.append(features, vec)
vec = []
if 'lbp' in self.features:
vec = local_binary_pattern(image, 24, 3).flatten()
features = np.append(features, vec)
vec = []
if 'daisy' in self.features:
vec = daisy(image).flatten()
features = np.append(features, vec)
return features
def classifier():
train_images,train_labels = processData('train')
test_images,test_labels = processData('test')
# sss = StratifiedShuffleSplit(train_labels, 3, test_size=0.5, random_state=0)
# print sss
# raw_input()
# for train_index, test_index in sss:
# print train_labels[train_index]
# raw_input()
train_images, train_labels = refineSets(train_images, train_labels, 1111)
test_images, test_labels = refineSets(test_images, test_labels, 111)
hog_train_images = [ hog(image) for image in train_images]
hog_test_images = [ hog(image) for image in test_images]
print 'Accuracy on test data : '
#DistanceMetric.get_metric(metric)
forest_sizes = [100,200,300,400,500]
for size in forest_sizes:
clf = RandomForestClassifier(n_estimators=size,criterion='entropy',n_jobs=-1)
clf.fit(hog_train_images,train_labels)
print size,"->",clf.score(hog_test_images,test_labels)
def extractHOG(inputimg, showHOG=False):
# convert image to single-channel, grayscale
image = color.rgb2gray(inputimg)
#extract HOG features
if showHOG:
fd, hog_image = feature.hog(image, orientations=36,
pixels_per_cell=(16, 16),
cells_per_block=(2, 2),
visualise=showHOG)
else:
fd = feature.hog(image, orientations=8, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualise=showHOG)
if(showHOG):
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)
ax1.axis('off')
ax1.imshow(image, cmap=plt.cm.gray)
ax1.set_title('Input image')
ax1.set_adjustable('box-forced')
# Rescale histogram for better display
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))
ax2.axis('off')
ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)
ax2.set_title('Histogram of Oriented Gradients')
ax1.set_adjustable('box-forced')
plt.show()
return fd
def getHOG(imgs, ori=8, ppc=(4, 4), cpb=(4, 4), vis=True):
# determine the shape of the output
if vis:
fd, im = hog(imgs[0, :], orientations=ori, pixels_per_cell=ppc, cells_per_block=cpb, visualise=vis)
imgs2 = imgs
else:
fd = hog(imgs[0, :], orientations=ori, pixels_per_cell=ppc, cells_per_block=cpb, visualise=vis)
hogs = np.zeros((imgs.shape[0], fd.shape[0]))
# HOG
for i in range(imgs.shape[0]):
# zimgs[i,:] = exposure.equalize_hist(imgs[i,:])
# imgs[i,:] = rank.equalize(imgs[i,:]/255,selem=disk(0))
# plt.imshow(imgs[i,:]),plt.show()
if vis:
hogs[i, :], imgs2[i] = hog(
imgs[i, :], orientations=ori, pixels_per_cell=ppc, cells_per_block=cpb, visualise=vis
)
else:
hogs[i, :] = hog(imgs[i, :], orientations=ori, pixels_per_cell=ppc, cells_per_block=cpb, visualise=vis)
sys.stdout.write("\rIteration {0}/{1}".format((i + 1), imgs.shape[0]))
sys.stdout.flush()
mean = np.mean(hogs, axis=0)
hogs -= mean
if vis:
return hogs, imgs2
else:
return hogs
def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):
"""
Extract the HOG features from the input image.
Parameters:
img: Input image.
orient: Number of orientation bins.
pix_per_cell: Size (in pixels) of a cell.
cell_per_block: Number of cells in each block.
vis: Visualization flag.
feature_vec: Return the data as a feature vector.
"""
if vis == True:
features, hog_image = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
else:
features = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=True,
visualise=vis, feature_vector=feature_vec)
return features
def transfer_to_hog(file_name):
if file_name =='train':
csv_file_object = csv.reader(open('train.csv', 'rb'))
hog_write = csv.writer(open('hog.csv', 'wb'))
header = csv_file_object.next()
imsize=(28,28)
for row in csv_file_object:
hogr=[]
hogr.append(int(row[0]))
image=map(int,row[1:])
image=np.reshape(image, imsize)
fd= hog(image, orientations=8, pixels_per_cell=(4, 4),
cells_per_block=(1, 1))
hogr.extend(fd)
fd= hog(image[2:26,2:26], orientations=8, pixels_per_cell=(4, 4),
cells_per_block=(1, 1))
hogr.extend(fd)
hog_write.writerow(hogr)
if file_name =='test':
csv_file_object = csv.reader(open('test.csv', 'rb'))
hog_write = csv.writer(open('hog_test.csv', 'wb'))
header = csv_file_object.next()
imsize=(28,28)
for row in csv_file_object:
hogr=[]
image=map(int,row)
image=np.reshape(image, imsize)
fd= hog(image, orientations=8, pixels_per_cell=(4, 4),
cells_per_block=(1, 1))
hogr.extend(fd)
fd= hog(image[2:26,2:26], orientations=8, pixels_per_cell=(4, 4),
cells_per_block=(1, 1))
hogr.extend(fd)
hog_write.writerow(hogr)
def ComputeDescriptors(self,RGB,Depth,dep_mask,h):
dep = np.float32(Depth)
dep_mask =cv2.bitwise_not(dep_mask)
ret, mask = cv2.threshold(dep, 1.7, 1, cv2.THRESH_BINARY_INV)
mask = np.uint8(mask)
ret, mask2 = cv2.threshold(dep, 0.01, 1, cv2.THRESH_BINARY)
mask2 = np.uint8(mask2)
mask = cv2.bitwise_and(mask,mask2)
mask = cv2.bitwise_and(mask,dep_mask)
if h:
masked_data = cv2.bitwise_and(RGB, RGB, mask=mask)
masked_data = cv2.bitwise_and(masked_data, masked_data, mask=mask2)
sp = cv2.cvtColor(masked_data, cv2.COLOR_RGB2GRAY)
sp = cv2.GaussianBlur(sp, (5, 5),10)
fd, imn = hog(dep, self.orientations, self.pixels_per_cell, self.cells_per_block,
self.visualize, self.normalize)
if self.HogDepth:
fdn,im = hog(sp, self.orientations, self.pixels_per_cell, self.cells_per_block,
self.visualize, self.normalize)
fd = np.concatenate((fd, fdn))
else:
fd = []
fgrid = np.array([])
for i in xrange(4):
for j in xrange(4):
sub = RGB[25*i:25*(i+1),25*j:25*(j+1)]
sub_mask = mask[25*i:25*(i+1),25*j:25*(j+1)]
fsub = self.ComputeHC(sub,sub_mask)
fgrid = np.concatenate((fgrid,fsub))
fd2 = fgrid.copy()
return fd,fd2,masked_data
def test_img(svm, img_path, scales, subwindow=None):
base_img = cv2.imread(img_path)
prev_img_path = utils.get_prev_img(img_path)
base_prev_img = cv2.imread(prev_img_path)
windows = []
windows_features = []
sc = []
for scale in scales:
img = cv2.resize(base_img, (0, 0), fx=scale, fy=scale)
img_bw = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
prev_img = cv2.resize(base_prev_img, (0, 0), fx=scale, fy=scale)
prev_img_bw = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY)
height, width, _ = img.shape
flow = cv2.calcOpticalFlowFarneback(prev_img_bw, img_bw, 0.5, 3, 15, 3, 5, 1.2, 0)
hsv = np.zeros_like(img)
hsv[..., 1] = 255
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
hsv[..., 0] = ang * 180/ np.pi / 2
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
flowRGB = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
flow_bw = cv2.cvtColor(flowRGB, cv2.COLOR_BGR2GRAY)
if subwindow == None:
nsx, nsy, nw, nh = 0, 0, width, height
else:
nsx, nsy, nw, nh = utils.getDetectionWindow(subwindow, width, height, scale)
for x in range(nsx, nsx + nw - 64, 16):
for y in range(nsy, nsy + nh - 128, 16):
img_crop = img_bw[y:y + 128, x:x + 64]
hog_gray = hog(img_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
flow_crop = flow_bw[y:y + 128, x:x + 64]
fd_flow = hog(flow_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
fd = hog_gray + fd_flow
windows.append((x, y))
windows_features.append(fd)
sc.append(scale)
classes = svm.predict(windows_features)
results = []
for i in range(0, len(windows)):
if classes[i] == 1:
scale = sc[i]
results.append((int(windows[i][0] / scale), int(windows[i][1] / scale), int(64 / scale), int(128 / scale)))
return results
def test_hog_output_equivariance_multichannel():
img = data.astronaut()
img[:, :, (1, 2)] = 0
hog_ref = feature.hog(img, multichannel=True, block_norm='L1')
for n in (1, 2):
hog_fact = feature.hog(np.roll(img, n, axis=2), multichannel=True,
block_norm='L1')
assert_almost_equal(hog_ref, hog_fact)
def get_spat_arrng_ftrs(gray_img): # resize img to 600 * 600 resized_img = transform.resize(gray_img, (600,600)) left = resized_img.transpose()[:300].transpose() right = resized_img.transpose()[300:].transpose() I_anti = np.identity(600)[::-1] # anti - diagonal identity matrix inner = feature.hog(left) - feature.hog(I_anti.dot(right)) return dict(symmetry = np.linalg.norm(inner))
def get_set(metadataFile, classType):
set = []
with open(metadataFile, "r") as f:
entries = f.readlines()
for entry in entries:
entry = entry.split()
filePath = entry[0]
x, y, scale = int(entry[1]), int(entry[2]), float(entry[3])
img = cv2.imread(filePath)
img = cv2.resize(img, (0, 0), fx=scale, fy=scale)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_gray_crop = img_gray[y:y+128, x:x+64]
hog_gray = hog(img_gray_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
prevFilePath = utils.get_prev_img(filePath)
prev_img = cv2.imread(prevFilePath)
prev_img = cv2.resize(prev_img, (0, 0), fx=scale, fy=scale)
prev_img_gray = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY)
flow = cv2.calcOpticalFlowFarneback(prev_img_gray, img_gray, 0.5, 3, 15, 3, 5, 1.2, 0)
# flowx, flowy = flow[..., 0], flow[..., 1]
# flowx_crop, flowy_crop = flowx[y:y+128, x:x+64], flowy[y:y+128, x:x+64]
#
# hog_flow_x = hog(flowx_crop, orientations=9, pixels_per_cell=(8, 8),
# cells_per_block=(2, 2), visualise=False)
# hog_flow_y = hog(flowy_crop, orientations=9, pixels_per_cell=(8, 8),
# cells_per_block=(2, 2), visualise=False)
hsv = numpy.zeros_like(img)
hsv[..., 1] = 255
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
hsv[..., 0] = ang * 180/ numpy.pi / 2
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
flowRGB = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
flow_gray = cv2.cvtColor(flowRGB, cv2.COLOR_BGR2GRAY)
flow_gray_crop = flow_gray[y:y+128, x:x+64]
hog_flow = hog(flow_gray_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
desc = hog_gray + hog_flow
set.append(desc)
return set, [classType] * len(entries)
def test_img_new(svm, img_path, scales, subwindow=None):
base_img = cv2.imread(img_path)
prev_img_path = utils.get_prev_img(img_path)
base_prev_img = cv2.imread(prev_img_path)
windows = []
windows_features = []
sc = []
for scale in scales:
img = cv2.resize(base_img, (0, 0), fx=scale, fy=scale)
img_bw = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
prev_img = cv2.resize(base_prev_img, (0, 0), fx=scale, fy=scale)
prev_img_bw = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY)
height, width, _ = img.shape
flow = cv2.calcOpticalFlowFarneback(prev_img_bw, img_bw, 0.5, 3, 15, 3, 5, 1.2, 0)
flowx, flowy = flow[..., 0], flow[..., 1]
if subwindow == None:
nsx, nsy, nw, nh = 0, 0, width, height
else:
nsx, nsy, nw, nh = utils.getDetectionWindow(subwindow, width, height, scale)
for x in range(nsx, nsx + nw - 64, 16):
for y in range(nsy, nsy + nh - 128, 16):
img_crop = img_bw[y:y + 128, x:x + 64]
hog_gray = hog(img_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
flowx_crop, flowy_crop = flowx[y:y+128, x:x+64], flowy[y:y+128, x:x+64]
hog_flow_x = hog(flowx_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
hog_flow_y = hog(flowy_crop, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(2, 2), visualise=False)
fd = numpy.concatenate((hog_gray, hog_flow_x, hog_flow_y))
windows.append((x, y))
windows_features.append(fd)
sc.append(scale)
classes = svm.predict(windows_features)
results = []
for i in range(0, len(windows)):
if classes[i] == 1:
scale = sc[i]
results.append((int(windows[i][0] / scale), int(windows[i][1] / scale), int(64 / scale), int(128 / scale)))
return results
def get_hog_features(img, params):
if params['vis'] == True:
features, hog_image = hog(img, orientations=params['n_orient_bins'], pixels_per_cell=params['pixels_per_cell'],
cells_per_block=params['cell_per_block'], transform_sqrt=False,
visualise=params['vis'], feature_vector=params['collapse_feat'])
return features, hog_image
else:
features = hog(img, orientations=params['n_orient_bins'], pixels_per_cell=params['pixels_per_cell'],
cells_per_block=params['cell_per_block'], transform_sqrt=False,
visualise=params['vis'], feature_vector=params['collapse_feat'])
return features
def extract_features(self, sample):
kwargs = dict(normalize=self.normalize,
orientations=self.orientations,
pixels_per_cell=self.pixels_per_cell,
cells_per_block=self.cells_per_block)
features = hog(sample[:, :, 0], **kwargs)
features += hog(sample[:, :, 1], **kwargs)
features += hog(sample[:, :, 2], **kwargs)
return features
def calculate_hog_features(raw_training_data):
num_train = len(raw_training_data)
test = raw_training_data[0]
test = test.reshape((28,28))
fd = hog(test, orientations=12, pixels_per_cell=(7,7), cells_per_block=(1,1), visualise=False)
hf_length = len(fd)
hog_features = np.zeros((num_train,hf_length))
for i in range(num_train):
temp = raw_training_data[i]
temp = temp.reshape((28,28))
fd = hog(temp, orientations=12, pixels_per_cell=(7,7), cells_per_block=(1,1), visualise=False)
hog_features[i] = fd
return hog_features
def get_hog_features(img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True):
# Call with two outputs if vis==True
if vis == True:
features, hog_image = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=False,
visualise=vis, feature_vector=feature_vec)
return features, hog_image
# Otherwise call with one output
else:
features = hog(img, orientations=orient, pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block), transform_sqrt=False,
visualise=vis, feature_vector=feature_vec)
return features
def test_histogram_of_oriented_gradients():
img = img_as_float(data.astronaut()[:256, :].mean(axis=2))
fd = feature.hog(img, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(1, 1))
assert len(fd) == 9 * (256 // 8) * (512 // 8)
def getFeature(image, blocks=5):
'''
Given an cv2 Image object it returns its feature vector.
Args:
image (ndarray): image to process.
blocks (int, optional): number of block to subdivide the RGB space into.
Returns:
numpy array.
'''
image_resized = cv2.resize(image, (64, 64))
imgray = cv2.cvtColor(image_resized, cv2.COLOR_RGB2GRAY)
feature = np.zeros(blocks * blocks * blocks + 144) # 144 for hog
width, height, channel = image_resized.shape
pixel_count = width * height
r = ((image[:, :, 2]) / (256 / blocks)).astype("int")
g = ((image[:, :, 1]) / (256 / blocks)).astype("int")
b = ((image[:, :, 0]) / (256 / blocks)).astype("int")
result = r + g * blocks + b * blocks * blocks
unique, counts = np.unique(result, return_counts=True)
feature[unique] = counts
feature = feature / (pixel_count)
feature_hog = hog(
imgray, orientations=9,
pixels_per_cell=(16, 16), cells_per_block=(1, 1), visualise=False)
feature[blocks**3:] = feature_hog
return feature
def hogKmeansClustering(param, orientNum, DATA_FOLDER):
ts = ListaSet(param)
print "{} images in total".format(ts.get_num_images())
clustNum = 5
# d1 = {}
# Patches = np.empty([ts.get_num_images(), 13, 13 ], dtype=np.float32)
PatchArr = np.empty([ ts.get_num_images(), orientNum])
for i in range( ts.get_num_images()):
# Patches[i,:,:] = ts.get_input(i)
PatchArr[i, :] = hog( ts.get_input(i), orientations=orientNum, pixels_per_cell=(13, 13), cells_per_block=(1, 1), visualise=False)
# for 5x5 centered at 13x13 input
# PatchArr[i, :] = hog( ts.get_input(i)[4:9, 4:9], orientations=orientNum, pixels_per_cell=(5, 5), cells_per_block=(1, 1), visualise=False)
# for 17x17 input
# PatchArr[i, :] = hog( ts.get_input(i)[6:11, 6:11], orientations=orientNum, pixels_per_cell=(5, 5), cells_per_block=(1, 1), visualise=False)
if i % 10000 == 0:
print i
print "load existing codebook..."
codebook = np.load( DATA_FOLDER+'/4bins_5cls/codebookArtificial2.npy')
print codebook
print "assign codes for patches..."
code, dist = scipy.cluster.vq.vq( PatchArr, codebook)
d1 = {}
for i in range( clustNum):
d1['label'+str(i+1)+'_idx'] = np.where( code == i)[0].astype( np.uint32)
d1['labelArr'] = code.astype( np.uint32)
# d1['PatchArr'+str(i+1)] = Patches[ np.where( code == i)[0], :, :].astype( np.uint32)
# d1['PatchArr'] = Patches
scipy.io.savemat( DATA_FOLDER+'/4bins_5cls/trainlabelsArtificial13code2_sps100_r8.mat', d1)
def cal_hog(image,orientations=9, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualise=True):
# fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),
# cells_per_block=(1, 1), visualise=True)
fd, hog_image = hog(image, orientations, pixels_per_cell,
cells_per_block, visualise)
return fd,hog_image
def print_hog_image(image):
"""
image is expected to be in it's original format
function prints hog image
"""
print image.shape
image = color.rgb2gray(image)
fd, hog_image = hog(image, orientations=8, pixels_per_cell=(4, 4),
cells_per_block=(1, 1), visualise=True, normalise=True)
print "finished hog..."
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)
ax1.axis('off')
ax1.imshow(image, cmap=plt.cm.gray)
ax1.set_title('Input image')
ax1.set_adjustable('box-forced')
# Rescale histogram for better display
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))
ax2.axis('off')
ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)
ax2.set_title('Histogram of Oriented Gradients')
ax1.set_adjustable('box-forced')
plt.show()
def learn_hog(img):
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
global n_bin
global b_size
global c_size
global hog_list
global labels
w, l = np.shape(img)
img_list = list()
img_list.append(img)
img_list.append(img[5:w - 2, 7:l - 6])
img_list.append(img[4:, :])
img_list.append(img[0:w-4, :])
img_list.append(img[:, 4:])
img_list.append(img[:, :l-4])
img_list.append(img[7:, :])
img_list.append(img[0:w-7, :])
img_list.append(img[:, 7:])
img_list.append(img[:, :l-7])
img_list.append(img[12:, :])
img_list.append(img[0:w-12, :])
img_list.append(img[:, 12:])
img_list.append(img[:, :l-12])
index = 0
global show
for imgs in img_list:
imgs = cv2.resize(imgs, (115, 120), interpolation=cv2.INTER_AREA) # resize image
if SHOW == 1:
cv2.imshow('img' + str(index), imgs)
index += 1
HOG_LIST.append(hog(imgs, orientations=n_bin, pixels_per_cell=(c_size, c_size),
cells_per_block=(b_size / c_size, b_size / c_size), visualise=False))
labels.append(label)
def plothog(k1):
infile=os.path.join('C:\meenuneenu\project\libsvm-3.17\python\data',"IMG-%s.png" % k1)
#infile="C:/Python27/Lib/site-packages/temp/svm/window/window4.jpg"
#img = Image.open(infile)
img = Image.open(infile)
#image = color.rgb2gray(data.lena())
"""
width = 50
height = 50
# Resize it.
img = img.resize((width, height), Image.BILINEAR)
# Save it back to disk.
img.save(os.path.join("C:/Python27/Lib/site-packages/temp/svm/", 'resized.png'))
"""
img = img.convert('1')
img.save("C:/Python27/Lib/site-packages/temp/IMG-4.png")
fd = list(hog(img, orientations=8, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualise=False))
#filepath=os.path.join('C:/Python27/Lib/site-packages/temp',"data%s.txt" % k1)
filepath=os.path.join('C:\meenuneenu\project\libsvm-3.17\python\data',"data%s.txt" % k1)
file = open(filepath, "w")
for item in fd:
file.write("%s " % item)
file.close()
#delpath=os.path.join('rm C:/Python27/Lib/site-packages/temp/',"IMG-%s.png" % k)
os.system('rm C:/Python27/Lib/site-packages/temp/IMG-4.png')
def do_hog(im):
img = color.rgb2gray(im)
fd, hog_image = hog(img, orientations=8, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualise=True)
hog_image_rescaled = hog_image # exposure.rescale_intensity(hog_image, in_range=(0, 0.02))
return hog_image_rescaled
def neg_hog_rand(path, num_samples, window_size, num_window_per_image): rows = window_size[0] cols = window_size[1] features = [] cnt = 0 for dirpath, dirnames, filenames in walk(path): for my_file in filenames: if cnt < num_samples: print cnt,my_file cnt = cnt + 1 im = cv2.imread(path + my_file) image = color.rgb2gray(im) image_rows = image.shape[0] image_cols = image.shape[1] for i in range(0,num_window_per_image): x_min = random.randrange(0,image_rows - rows) y_min = random.randrange(0,image_cols - cols) x_max = x_min + rows y_max = y_min + cols image_hog = image[x_min:x_max , y_min:y_max] my_feature, _ = hog(image_hog, orientations=9, pixels_per_cell=(8, 8),cells_per_block=(2, 2), visualise=True) features.append(my_feature) return features
def extractPHOGFeature(image, level1, level2):
''' Extract PHOG feature from image '''
height, width = image.shape
orientationNum = 9
pHOGVec = np.array([])
widthBase = 4
heightBase = 3
for l in xrange(level1, level2 + 1):
multi = 2 ** (l - 1)
widthGridNum = widthBase * multi
heightGridNum = heightBase * multi
featureVector, hogImage = hog(image, orientations = orientationNum,
pixels_per_cell = (width/widthGridNum, height/heightGridNum),
cells_per_block=(1, 1), visualise = True)
featureVector = featureVector[np.newaxis].transpose() # transpose to column vector
if pHOGVec.size == 0:
pHOGVec = featureVector.copy()
else:
pHOGVec = np.vstack((pHOGVec, featureVector))
return pHOGVec
def getFeat(Data, mode, fileNames, positive):
num = 0
for image in Data:
gray = rgb2gray(image)
fd, hog_image = hog(gray, orientations, pixels_per_cell,
cells_per_block, block_norm, visualize, normalize, feature_vector)
if(visualize):
visualize(data, hog_image)
fd_name = str(fileNames[num]) + '.feat' # set file name
if mode == 'train':
if positive == True:
fd_path = os.path.join('./features/train/positive/', fd_name)
else:
fd_path = os.path.join(
'./features/train/negative/', fd_name)
else:
if positive == True:
fd_path = os.path.join('./features/test/positive/', fd_name)
else:
fd_path = os.path.join('./features/test/negative/', fd_name)
joblib.dump(fd, fd_path, compress=3) # save data to local
num += 1
print("%d saving: ." % (num))
ax1.axis('off')
ax2.imshow(image_gb5, vmin=image.min(), vmax=image.max(), cmap='gray')
ax2.set_title('gaussian blur 5')
ax2.axis('off')
fig.tight_layout()
fig.savefig('image-gaussian_blur.png')
# In[35]:
#hog feature
(hog, hog_image) = feature.hog(image_gb3,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(4, 4),
block_norm='L2-Hys',
visualize=True,
transform_sqrt=True)
plt.imshow(image_raw)
plt.imshow(hog_image, alpha=0.7, cmap='hsv')
plt.show()
#plt.imwrite('hog_gb.jpg', hog_image*255)
# In[162]:
img_rgb = image_gb5
# In[163]:
def extract_hog_feature(self, image):
hog_feature = hog(image, orientations=9, pixels_per_cell=(16, 16), cells_per_block=(2, 2))
return hog_feature
data = []
labels = []
#Read all images in training directory
print("Computing HOG...")
for file in paths.list_images(args["training"]):
file = file.replace('\\', '') #This treat if has some space in image name
img_original = cv2.imread(file)
img = cv2.cvtColor(img_original, cv2.COLOR_BGR2GRAY) #Image in grayscale
#Hog method
(H, hogImage) = hog(img,
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(2, 2),
transform_sqrt=True,
visualise=True,
block_norm='L2-Hys')
#Call this function to print the HOG image
#print_hog(hogImage, file.split('/')[-1], img_original)
labels.append(file.split('/')[-2])
data.append(H)
mode = args["machineLearning"]
#Training a model
if (int(mode) == 2):
print("Training a Model with SVC...")
# find contours in the edge map, keeping only the largest one which
# is presmumed to be the car logo
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
c = max(cnts, key=cv2.contourArea)
# extract the logo of the car and resize it to a canonical width
# and height
(x, y, w, h) = cv2.boundingRect(c)
logo = gray[y:y + h, x:x + w]
logo = cv2.resize(logo, (224, 224))
# extract Histogram of Oriented Gradients from the logo
H = feature.hog(logo, orientations=9, pixels_per_cell=(10, 10),
cells_per_block=(2, 2), feature_vector=True,transform_sqrt=True, block_norm="L1")
# update the data and labels
data.append(H)
labels.append(make)
# "train" the nearest neighbors classifier
print("[INFO] training classifier...")
model = KNeighborsClassifier(n_neighbors=1)
model.fit(data, labels)
print("[INFO] evaluating...")
# loop over the test dataset
for (i, imagePath) in enumerate(paths.list_images(args["test"])):
# load the test image, convert it to grayscale, and resize it to
print("brightness features")
brightness = util.loading_map(extraction.calculateDarktoBrightRatio, resized)
print("hsv 11 + std")
hsv_11_std = util.loading_map(
lambda x: extraction.split_image_features(
lambda y: extraction.color_features(y, mean=True, std=True), 11, x),
hsv)
print("luv features")
luv_features = util.loading_map(lambda x: extraction.pixel_features(x, 11),
luv)
print("hog features")
hog = util.loading_map(
lambda x: feature.hog(x,
orientations=6,
pixels_per_cell=(12, 12),
cells_per_block=(8, 8),
normalise=True), grayscaled)
print("corner features")
corners = util.loading_map(
lambda x: extraction.pixel_features(
numpy.sqrt(feature.corner_shi_tomasi(x, sigma=8)), 8), grayscaled)
n_folds = 10
model = Pipeline([("standard scaler", StandardScaler()),
("logistic regression",
LogisticRegression(solver='lbfgs',
multi_class='multinomial'))])
print("Evaluating brightness features")
def HOG_SVM_detector(image_path, model_path):
ori_img = cv2.imread(image_path)
if type(model_path) == str:
model = joblib.load(model_path)
else:
model = model_path
# print(type(model_path)==str)
# 将图像裁剪到最大宽度为400个像素，之所以降低我们的图像维度(其实就是之所以对我们的图像尺寸进行裁剪)主要有两个原因：
# 1. 减小图像的尺寸可以减少在图像金字塔中滑窗的数目，如此可以降低检测的时间，从而提高整体检测的吞吐量。
# 2. 调整图像的尺寸能够整体提高行人检测的精度。
minAxis_Image = 400
img = imutils.resize(ori_img,
width=min(minAxis_Image,
ori_img.shape[1])) # 修改的图像是以最小的那个数字对齐
# 用于之后复原图片
HO, WO, _ = ori_img.shape
HR, WR, _ = img.shape
# 改 程序运行时间过长，将参数改大，注释中是 论文参数
# 这个设置是按照开创性的 Dalal 和 Triggs 论文来设置的 Histograms of Oriented Gradients for Human Detection
win_size = (64, 128) # 窗口的大小固定在64*128像素大小
step_size = (5, 5) # 一个分别在x方向和y方向步长为(4,4)像素大小的滑窗
downscale = 1.2 # 尺度 scale=1.05 的图像金字塔
threshold = 0.6 # 设定 SVM 预测的阈值，即只有当预测的概率大于 0.6 时，才会确定支持向量机的预测
overlapThresh = 0.3 # nms
rectangles = []
scores = []
scale = 0
for img_scale in pyramid_gaussian(img, downscale=downscale):
if img_scale.shape[0] < win_size[1] or img_scale.shape[1] < win_size[0]:
break
for (x, y, img_window) in sliding_window(img_scale, win_size,
step_size):
if img_window.shape[0] != win_size[1] or img_window.shape[
1] != win_size[
0]: # ensure the sliding window has met the minimum size requirement
continue
fd = hog(rgb2gray(img_window),
orientations=9,
pixels_per_cell=(8, 8),
cells_per_block=(2, 2))
fd = fd.reshape(1, -1)
predict = model.predict(fd)
score = model.decision_function(fd)[0]
if predict == 1 and score > threshold:
x = int(x * (downscale**scale))
y = int(y * (downscale**scale))
w = int(win_size[0] * (downscale**scale))
h = int(win_size[1] * (downscale**scale))
rectangles.append((x, y, x + w, y + h))
scores.append(score)
scale += 1
rectangles = np.array(rectangles)
scores = np.array(scores)
bounding_boxes = non_max_suppression(rectangles,
probs=scores,
overlapThresh=overlapThresh)
# print("rectangles.shape : ", rectangles.shape)
# print("bounding_boxes.shape : ", bounding_boxes.shape)
# 如果检测到了内容
if rectangles.shape[0] > 0:
# 复原
rectangles = rectangles.astype(float)
rectangles[:, [0, 2]] *= (WO / WR)
rectangles[:, [1, 3]] *= (HO / HR)
rectangles = rectangles.astype(int)
bounding_boxes = bounding_boxes.astype(float)
bounding_boxes[:, [0, 2]] *= (WO / WR)
bounding_boxes[:, [1, 3]] *= (HO / HR)
bounding_boxes = bounding_boxes.astype(int)
# print("rectangles.shape : ", rectangles.shape)
# print("bounding_boxes.shape : ", bounding_boxes.shape)
# print(bounding_boxes)
# return rectangles, bounding_boxes
return bounding_boxes
labels = []
#fill the training dataset
# the flow is
# 1) load sample
# 2) resize it to (200,200) so that we have same size for all the images
# 3) get the HOG features of the resized image
# 4) save them in the data list that holds all the hog features
# 5) also save the label (target) of that sample in the labels list
for filename in brian_filenames:
#read the images
image = imread(filename, 1)
#flatten it
image = imresize(image, (200, 200))
hog_features = hog(image,
orientations=12,
pixels_per_cell=(16, 16),
cells_per_block=(1, 1))
data.append(hog_features)
labels.append(0)
print 'Finished adding brians samples to dataset'
for filename in alfin_filenames:
image = imread(filename, 1)
image = imresize(image, (200, 200))
hog_features = hog(image,
orientations=12,
pixels_per_cell=(16, 16),
cells_per_block=(1, 1))
data.append(hog_features)
labels.append(1)
print 'Finished adding alfins samples to dataset'
def get_hog(img):
hog_image = hog(img,
orientations=10,
pixels_per_cell=(5, 5),
cells_per_block=(3, 3))
return exposure.rescale_intensity(hog_image, in_range=(0, 0.9))
def calculate_hog(img):
return feature.hog(img,
orientations=8,
pixels_per_cell=(32, 32),
cells_per_block=(8, 8),
block_norm='L2-Hys')
# read the image
img = cv2.imread("dataset/digits.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# break up the image into individual digits
cells = [np.hsplit(row, 100) for row in np.vsplit(gray, 50)]
# compute the HOG features for each digit and make a dataset
dataset = []
for i in range(50):
for j in range(100):
feature = hog(cells[i][j],
pixels_per_cell=(10, 10),
cells_per_block=(1, 1))
dataset.append(feature)
dataset = np.array(dataset, 'float64')
# make labels for all 5000 digits
labels = []
for i in range(10):
labels = labels + [i] * 500
# use the KNN algorithm for training
clf = KNeighborsClassifier()
x_train, x_test, y_train, y_test = tts(dataset, labels, test_size=0.3)
im_gray1) # Extract out the object and place into output imag
out[mask == 255] = im_gray1[mask == 255]
lower_value = np.array([60])
upper_value = np.array([200])
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(out, lower_value, upper_value)
# Bitwise-AND mask and original image
out = cv2.bitwise_and(out, out, mask=mask)
v = out[out[:, :] > 0]
# print(v.mean())
out[out[:, :] < (u.mean())] = 255
out[out[:, :] > (u.mean() + 10)] = 0
fd = hog(out.reshape((512, 512)),
orientations=9,
pixels_per_cell=(14, 14),
cells_per_block=(1, 1),
visualise=False)
features.append(fd)
hog_features = np.array(features, 'float64')
labels.append("true")
# cv2.imshow("TestImageGry", im_gray)
# cv2.imshow("TestImage", imq)
i = i + 1
# cv2.waitKey()
# inp=input()
print(i)
if i is 32:
test = False
# curImageCropped.save("processed_alpha_test/" + imagename)
# Crop images to 900 x 900
# img2 = curImage.crop((0, 0, 900, 900))
# img2.save("processed_alpha_test/img_a.png")
# Save image > matrix
# Save label
list_hog_fd = []
for feature in letter_image_features:
print "feature", feature
fd = hog(feature.reshape((28, 28)),
orientations=9,
pixels_per_cell=(14, 14),
cells_per_block=(1, 1),
visualise=False)
list_hog_fd.append(fd)
hog_features = np.array(list_hog_fd, 'float64')
print "Count of digits in dataset", Counter(labels)
# Create an linear SVM object
clf = LinearSVC()
# Perform the training
clf.fit(hog_features, labels)
# Save the classifier
joblib.dump(clf, "digits_cls_alpha1.pkl", compress=3)
if testEdgeDetection:
edge = cv2.Canny(cv2.cvtColor(im, cv2.COLOR_RGB2GRAY),100,300)
plot.figure(plotIndex)
plotIndex += 1
plot.clf()
plot.imshow(edge)
# *************************************************
if trainClassifier:
l = []
for i in range(len(trainingImages)):
if trainFromHSVImage:
l.append(labels[i])
hsv = cv2.cvtColor(trainingImages[i], cv2.COLOR_RGB2HSV)
feat = hog(hsv[:,:,1], orientations=5, pixels_per_cell=(7, 7), cells_per_block=(2, 2), block_norm='L1-sqrt', visualise=False)
normalizedFeatures = StandardScaler().fit(feat.reshape(-1, 1)).transform(feat.reshape(-1, 1))
if (i == 0):
features = normalizedFeatures
else:
features = np.column_stack((features, normalizedFeatures))
if trainFromEdgeImage:
l.append(labels[i])
edge = cv2.Canny(cv2.cvtColor(trainingImages[i], cv2.COLOR_RGB2GRAY),50,100)
feat = hog(edge, orientations=5, pixels_per_cell=(7, 7), cells_per_block=(2, 2), block_norm='L1-sqrt', visualise=False)
normalizedFeatures = StandardScaler().fit(feat.reshape(-1, 1)).transform(feat.reshape(-1, 1))
if (i == 0) and not (trainFromHSVImage):
features = normalizedFeatures
else:
features = np.column_stack((features, normalizedFeatures))
svm = SVC(kernel='rbf').fit(features.T,np.array(l))
f = os.popen("ls /dev/ttyACM*")
usbPort = f.read()
usbPort = usbPort[0:len(usbPort) - 1]
ser = serial.Serial(usbPort, 9600)
print("connect succesfully")
while True:
img = cv2.imread('image.jpg', 0)
mi = 1.0
xx = 0
yy = 0
img = cv2.flip(img, 1)
img = cv2.resize(img, (160, 120))
for ii in range(16):
for jj in range(9):
frame = img[jj * 8:jj * 8 + 56, ii * 8:ii * 8 + 40]
out = feature.hog(frame, visualise=False, feature_vector=True)
compare = arr - out
x = 0.0
for i in compare:
x += i**2
x /= 1215.0
x = math.sqrt(x)
if x < mi:
mi = x
xx = ii
yy = jj
print('min:', mi, ' at ', xx, '-', yy)
if mi <= maxCorrect:
if yy > high:
cv2.rectangle(img, (xx * 8 - 1, yy * 8 - 1),
(xx * 8 + 40, yy * 8 + 56), (255, 255, 255))
from sklearn.model_selection import train_test_split
from sklearn import svm, datasets
from skimage import color,transform
import matplotlib.image as mpimg
from skimage.feature import hog
X = []
Y = []
dataset_size = 12500
datasetpath = "dataset/train/"
for i in range(dataset_size):
img_name = datasetpath+"cat." + str(i) + '.jpg'
img_cat = color.rgb2gray(mpimg.imread(img_name))
img = transform.resize(img_cat, (128,64) , mode='constant')
fcat, hog_img = hog(img, orientations = 9, pixels_per_cell = (8,8),cells_per_block = (2,2), visualise = True , block_norm='L2-Hys')
X.append(fcat)
Y.append(0)
for i in range(dataset_size):
img_name = datasetpath +"dog." + str(i) + '.jpg'
img_dog = color.rgb2gray(mpimg.imread(img_name))
img = transform.resize(img_dog, (128, 64) , mode='constant')
fdog, hog_img = hog(img, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(2, 2), visualise=True , block_norm='L2-Hys')
X.append(fdog)
Y.append(1)
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.30)
svc = svm.SVC(kernel='poly', C=1 , degree=1).fit(X_train, y_train)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 25 21:56:30 2021
@author: batuhan
"""
import matplotlib.pyplot as plt
from skimage.feature import hog
from skimage import data, exposure
image = data.astronaut()
fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualize=True, multichannel=True)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)
ax1.axis('off')
ax1.imshow(image, cmap=plt.cm.gray)
ax1.set_title('Input image')
# Rescale histogram for better display
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10))
ax2.axis('off')
ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)
ax2.set_title('Histogram of Oriented Gradients')
plt.show()
def testImage(path, args):
# Read the image
#im = imread(args["image"], as_grey=False)
im = cv2.imread(path, cv2.IMREAD_GRAYSCALE) # read img from file
min_wdw_sz = (24, 48)
step_size = (5, 5)
downscale = args['downscale']
visualize_det = args['visualize']
# Load the classifier
clf = joblib.load("../myData/traffic.model6")
# List to store the detections
detections = []
# The current scale of the image
scale = 0
# Downscale the image and iterate
for im_scaled in pyramid_gaussian(im, downscale=downscale):
# This list contains detections at the current scale
cd = []
# If the width or height of the scaled image is less than
# the width or height of the window, then end the iterations.
if im_scaled.shape[0] < min_wdw_sz[1] or im_scaled.shape[
1] < min_wdw_sz[0]:
break
for (x, y, im_window) in sliding_window(im_scaled, min_wdw_sz,
step_size):
if im_window.shape[0] != min_wdw_sz[1] or im_window.shape[
1] != min_wdw_sz[0]:
continue
# Calculate the HOG features
fd = hog(im_window,
orientations,
pixels_per_cell,
cells_per_block,
block_norm="L1",
visualise=False,
transform_sqrt=True,
feature_vector=True)
fd = [fd] # clf wants a list of samples
#pred = clf.predict(fd)
probs = list(clf.predict_proba(fd)[0])
#print(pred)
pred = probs.index(max(probs)) # index of max
# TODO: use all my labels
if pred != 0 and probs[pred] > 0.80:
print("prediction is " + str(pred))
print(probs)
#plt.imshow(im_window)
#plt.show()
print("Detection:: Location -> ({}, {})".format(x, y))
print("Scale -> {} | Confidence Score {} \n".format(
scale, probs[pred]))
detections.append((x, y, probs[pred],
int(min_wdw_sz[0] * (downscale**scale)),
int(min_wdw_sz[1] * (downscale**scale))))
cd.append(detections[-1])
# If visualize is set to true, display the working
# of the sliding window
if visualize_det:
clone = im_scaled.copy()
for x1, y1, _, _, _ in cd:
# Draw the detections at this scale
cv2.rectangle(
clone, (x1, y1),
(x1 + im_window.shape[1], y1 + im_window.shape[0]),
(0, 0, 0),
thickness=2)
cv2.rectangle(clone, (x, y),
(x + im_window.shape[1], y + im_window.shape[0]),
(255, 255, 255),
thickness=2)
cv2.imshow("Sliding Window in Progress", clone)
cv2.waitKey(30)
# Move the the next scale
scale += 1
# Display the results before performing NMS
clone = im.copy()
for (x_tl, y_tl, _, w, h) in detections:
# Draw the detections
cv2.rectangle(im, (x_tl, y_tl), (x_tl + w, y_tl + h), (0, 0, 0),
thickness=2)
cv2.imshow("Raw Detections before NMS", im)
cv2.waitKey()
# Perform Non Maxima Suppression
detections = nms(detections, threshold)
# Display the results after performing NMS
for (x_tl, y_tl, _, w, h) in detections:
# Draw the detections
cv2.rectangle(clone, (x_tl, y_tl), (x_tl + w, y_tl + h), (0, 0, 0),
thickness=2)
# save image to file
fname = path[::-1] # reverse the string
index = fname.find("/")
fname = fname[:index][::-1] # isolate just the file name and reverse back
cv2.imwrite(fname, clone)
print("saved to " + str(fname))
cv2.imshow("Final Detections after applying NMS", clone)
cv2.waitKey()
cv2.destroyAllWindows()
imgs = []
labels = []
label_counter = 0
file_counter = 0
for c in c_list:
curr_dir = os.path.join(args.data, c)
f_list = os.listdir(curr_dir)
f_list.sort()
print('Reading class:', c)
for f in f_list:
f_path = os.path.join(curr_dir, f)
img = imread(f_path)
feats = hog(img, orientations=9, pixels_per_cell=(16, 16), cells_per_block=(3, 3), block_norm='L2',
visualize=False, transform_sqrt=False, feature_vector=True, multichannel=True)
if args.subsample:
if file_counter % 10 == 0:
imgs.append(feats)
labels.append(label_counter)
else:
imgs.append(feats)
labels.append(label_counter)
file_counter += 1
label_counter += 1
imgs = np.vstack(imgs)
labels = np.array(labels)
mndata = MNIST('data')
tr_imgs, tr_lbs = mndata.load_training()
test_imgs, test_lbs = mndata.load_testing()
new_tr_lbs = [item for item in tr_lbs]
new_test_lbs = [item for item in test_lbs]
print(purity_score([2, 1, 2, 3, 2, 2, 3, 1], [1, 1, 2, 3, 2, 2, 3, 1]))
new_train = []
for img in tr_imgs:
pixels = np.array(img, dtype='uint8')
pixels = pixels.reshape((28, 28))
pixels = deskew(pixels)
pixels = np.pad(pixels, ((4, 4), (4, 4)), mode='constant')
img_hog = hog(pixels, block_norm='L2-Hys')
new_train.append(img_hog)
print('here')
new_test = []
for img in test_imgs:
pixels = np.array(img, dtype='uint8')
pixels = pixels.reshape((28, 28))
pixels = deskew(pixels)
# print(np.shape(pixels))
pixels = np.pad(pixels, ((4, 4), (4, 4)), mode='constant')
img_hog = hog(pixels, block_norm='L2-Hys')
# print(np.shape(img_hog))
new_test.append(img_hog)
print(np.shape(new_train))
def quantify_images(image):
features = feature.hog(image, orientations=9, pixels_per_cell=(10,10), cells_per_block=(2,2),
transform_sqrt=True, block_norm='L1')
return features
def hogProcess(image):
fd, hog_image = hog(image, orientations=8, pixels_per_cell=(8,8), cells_per_block=(1, 1), visualize=True, multichannel=False)
return hog_image
# cambio a binario
ret, im_th = cv2.threshold(im_gray, 90, 255, cv2.THRESH_BINARY_INV)
# busco la figura en la imagen
ctrs, hier = cv2.findContours(im_th.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# cuento figuras encontradas
rects = [cv2.boundingRect(ctr) for ctr in ctrs]
for rect in rects:
cv2.rectangle(im, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3)
leng = int(rect[3] * 1.4)
pt1 = int(rect[1] + rect[3] // 2 - leng // 2)
pt2 = int(rect[0] + rect[2] // 2.5 - leng // 2)
roi = im_th[pt1:pt1 + leng, pt2:pt2 + leng]
roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)
roi = cv2.dilate(roi, (3, 3))
roi_hog_fd = hog(roi,
orientations=9,
pixels_per_cell=(14, 14),
cells_per_block=(1, 1))
nbr = clf.predict(np.array([roi_hog_fd], 'float64'))
cv2.putText(im, str(int(nbr[0])), (rect[0], rect[1]),
cv2.FONT_HERSHEY_DUPLEX, 2, (0, 255, 255), 3)
cv2.imshow("Resulting Image with Rectangular ROIs", im)
cv2.waitKey()
print('removing', file_path)
os.unlink(file_path)
print("Calculating the descriptors for the positive samples and saving them")
for im_path in glob.glob(os.path.join(pos_im_path, "*")):
print("Working on file: ", im_path)
# Read image and convert to grayscale
im = cv2.imread(im_path)
gray_im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
resize_im = cv2.resize(gray_im, scale_size)
# Resize the image
fd = hog(resize_im,
orientations=orientations,
pixels_per_cell=pixels_per_cell,
cells_per_block=cells_per_block,
visualise=False)
# Save features
fd_name = os.path.split(im_path)[1].split(".")[0] + ".feat"
fd_path = os.path.join(pos_feat_dir, fd_name)
joblib.dump(fd, fd_path)
print("Positive features saved in {}".format(pos_feat_dir))
print("Calculating the descriptors for the negative samples and saving them")
for im_path in glob.glob(os.path.join(neg_im_path, "*")):
print("Working on file: ", im_path)
im = cv2.imread(im_path)
gray_im = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
img[..., 1] = np.reshape(data[1024:2048], (32, 32))
img[..., 2] = np.reshape(data[2048:3072], (32, 32))
return img
batches_meta = unpack(DATA_PATH + "batches.meta")
data_batches = [
unpack(DATA_PATH + "data_batch_" + str(i+1))
for i in range(5)
]
test_batch = unpack(DATA_PATH + "test_batch")
from skimage.color import rgb2gray
from skimage.feature import hog
for i in range(5):
data_batches[i][b"data"] = [hog(rgb2gray(reshape(img))) for img in data_batches[i][b"data"]]
test_batch[b"data"] = [hog(rgb2gray(reshape(img))) for img in test_batch[b"data"]]
hyperparam_train_data = data_batches[0][b"data"][:900]
hyperparam_train_labels = data_batches[0][b"labels"][:900]
hyperparam_test_data = data_batches[0][b"data"][900:1000]
hyperparam_test_labels = data_batches[0][b"labels"][900:1000]
import datetime
begin = datetime.datetime.now()
ks = [2, 3, 4, 5, 6, 7, 8, 9, 10]
correct_sums = []
for k in ks:
clf = neighbors.KNeighborsClassifier(k, weights="distance")
import numpy as np
import os
import scipy.ndimage
import imageio
from skimage.feature import hog
from skimage import data, color, exposure
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.externals import joblib
knn = joblib.load('model/knn_model.pkl')
image = imageio.imread('dataSet/9/IMG_49421.png')
image = color.rgb2gray(image)
df = hog(image,
orientations=8,
pixels_per_cell=(10, 10),
cells_per_block=(5, 5))
predict = knn.predict(df.reshape(1, -1))[0]
print(predict)
for i in vals:
if i[1] <= (int(q) + 50) and i[1] >= (int(q) - 50):
vds.append(i)
nums = []
for rect in vds:
cv2.rectangle(im, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0), 3)
leng = int(rect[3] * 1.6)
pt1 = int(rect[1] + rect[3] // 2 - leng // 2)
pt2 = int(rect[0] + rect[2] // 2 - leng // 2)
roi = im_th[pt1:pt1 + leng, pt2:pt2 + leng]
roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)
roi = cv2.dilate(roi, (3, 3))
roi_hog_fd = hog(roi,
orientations=9,
pixels_per_cell=(14, 14),
cells_per_block=(1, 1),
visualise=False)
nbr = clf.predict(np.array([roi_hog_fd], 'float64'))
nums.append(nbr[0])
cv2.putText(im, str(int(nbr[0])), (rect[0], rect[1]),
cv2.FONT_HERSHEY_DUPLEX, 2, (0, 255, 255), 3)
print('vals', vals)
print(nums)
print(vds)
cv2.imshow("Resulting Image with Rectangular ROIs", im)
cv2.waitKey()
# Import the modules
from sklearn.externals import joblib
from sklearn import datasets
from skimage.feature import hog
from sklearn.svm import LinearSVC
import numpy as np
X,y= datasets.fetch_openml('mnist_784', version=1, return_X_y=True) #importing datset and labels straight from sklearn
Images = np.array(X, 'int16') #converting list of Images into a numpy array.
labels = np.array(y, 'int')
list_hog_fd = []
for Image in Images:
fd = hog(Image.reshape((28, 28)), orientations=9, pixels_per_cell=(14, 14), cells_per_block=(1, 1), visualize=False,block_norm='L2') # using hog algorithm to extract features from each image
list_hog_fd.append(fd) #adding the hog features extracted from the image into a list
hog_features = np.array(list_hog_fd, 'float64') #converting the HOG feature list into a numpy array
'''
Two line code to train entire dataset
load the LinearSVC() model into clf
and then fit hog_features and list into clf
'''
clf = LinearSVC()
clf.fit(hog_features,labels)
print('Accuracy is:',clf.score(hog_features,labels)*100,'%')
joblib.dump(clf, "digits_cls.pkl", compress=3)
# In[17]:
#load image and convert into gray image
gray_sample = cv2.imread(file_list[0], 0)
# In[18]:
print(gray_sample.shape)
print(gray_sample)
# In[19]:
fd, hog_image = hog(gray_sample,
orientations=8,
pixels_per_cell=(16, 16),
cells_per_block=(1, 1),
visualise=True)
# In[20]:
print(hog_image.shape)
# In[21]:
print(hog_image)
# In[22]:
print(fd)
x_train = x_train_full[0:73500]
y_train = y_train_full[0:73500]
x_test = x_test_full[0:18000]
y_test = y_test_full[0:18000]
print(x_test.shape)
print(y_test.shape)
y_train = y_train.ravel()
y_test = y_test.ravel()
df_1 = []
for i in range(0, x_train.shape[0]):
df1 = hog(np.reshape(x_train[i], (32, 32)),
orientations=8,
pixels_per_cell=(8, 8),
cells_per_block=(4, 4))
df_1.append(df1)
x_train = np.array(df_1, 'float64')
df_2 = []
for i in range(0, x_test.shape[0]):
df2 = hog(np.reshape(x_test[i], (32, 32)),
orientations=8,
pixels_per_cell=(8, 8),
cells_per_block=(4, 4))
df_2.append(df2)
x_test = np.array(df_2, 'float64')
print('Shape of Training data after HOG is: ' + str(x_train.shape))
def compute_descriptor(im, sp, spPatch=15, colBins=20, hogCells=9,
hogBins=6, redirect=False):
"""
Compute region descriptors for NLC
Input:
im: (h,w,c) or (n,h,w,c): 0-255: np.uint8: RGB
sp: (h,w) or (n,h,w): 0-indexed regions, #regions <= numsp
spPatch: patchsize around superpixel for feature computation
Output:
regions: (k,d) where k < numsp*n
frameEnd: (n,): indices of regions where frame ends: 0-indexed, included
"""
sTime = time.time()
if im.ndim < 4:
im = im[None, ...]
sp = sp[None, ...]
hogCellSize = int(spPatch / np.sqrt(hogCells))
n, h, w, c = im.shape
d = 6 * colBins + hogCells * hogBins + 2
numsp = np.max(sp) + 1 # because sp are 0-indexed
regions = np.ones((numsp * n, d), dtype=np.float) * -1e6
frameEnd = np.zeros((n,), dtype=np.int)
count = 0
for i in range(n):
boxes = get_region_boxes(sp[i])
# get patchsize around center; corner cases handled inside loop
boxes[:, :2] = ((boxes[:, :2] + boxes[:, 2:] - spPatch) / 2)
boxes = boxes.astype(np.int)
boxes[:, 2:] = boxes[:, :2] + spPatch
for j in range(boxes.shape[0]):
# fix corner cases
xmin, xmax = np.maximum(0, np.minimum(boxes[j, [0, 2]], w - 1))
ymin, ymax = np.maximum(0, np.minimum(boxes[j, [1, 3]], h - 1))
xmax = spPatch if xmin == 0 else xmax
xmin = xmax - spPatch if xmax == w - 1 else xmin
ymax = spPatch if ymin == 0 else ymax
ymin = ymax - spPatch if ymax == h - 1 else ymin
imPatch = im[i, ymin:ymax, xmin:xmax]
hogF = hog(
color.rgb2gray(imPatch), orientations=hogBins,
pixels_per_cell=(hogCellSize, hogCellSize),
cells_per_block=(int(np.sqrt(hogCells)),
int(np.sqrt(hogCells))),
visualise=False)
colHist = color_hist(imPatch, colBins)
regions[count, :] = np.hstack((
hogF, colHist, [boxes[j, 1] * 1. / h, boxes[j, 0] * 1. / w]))
count += 1
frameEnd[i] = count - 1
if not redirect:
sys.stdout.write('Descriptor computation: [% 5.1f%%]\r' %
(100.0 * float((i + 1) / n)))
sys.stdout.flush()
regions = regions[:count]
eTime = time.time()
print('Descriptor computation finished: %.2f s' % (eTime - sTime))
return regions, frameEnd