def saveSamplesMosaic(kitti_base, pos_labels, mosaicShape, tileShape, fname):
import random
imgShape = (mosaicShape[0] * tileShape[0], mosaicShape[1] * tileShape[1])
mosaic_img = np.zeros((imgShape[0],imgShape[1],3), np.float32)
# avg_img = np.zeros((imgShape[0],imgShape[1]), np.float32)
numTiles = mosaicShape[0]*mosaicShape[1]
labels = pos_labels
if len(pos_labels) >= numTiles:
labels = random.sample(pos_labels, numTiles)
index = 0
for r in range(mosaicShape[0]):
for c in range(mosaicShape[1]):
if index >= len(labels):
break
label = labels[index]
index += 1
sample = getCroppedSampleFromLabel(kitti_base, label)
resized = resizeSample(sample, tileShape, label)
trs = tileShape[0]
tcs = tileShape[1]
tr = r * trs
tc = c * tcs
mosaic_img[tr:tr+trs, tc:tc+tcs] = resized
cv2.imwrite(fname, mosaic_img)
def saveAverageImage(kitti_base, pos_labels, shape, fname, avg_num=None):
num_images = float(len(pos_labels))
avg_num = min(avg_num, num_images)
if avg_num is None:
avg_num = num_images
# avg_img = np.zeros((shape[0],shape[1],3), np.float32)
avg_img = np.zeros(shape, np.float32)
progressbar = ProgressBar('Averaging ' + fname, max=len(pos_labels))
num = 0
for label in pos_labels:
if num >= avg_num:
break
num += 1
progressbar.next()
sample = getCroppedSampleFromLabel(kitti_base, label)
# sample = np.float32(sample)
resized = resizeSample(sample, shape, label)
resized = auto_canny(resized)
resized = np.float32(resized)
avg_img = cv2.add(avg_img, resized / float(avg_num))
progressbar.finish()
cv2.imwrite(fname, avg_img)
def saveSamplesMosaic(kitti_base, pos_labels, mosaicShape, tileShape, fname):
import random
imgShape = (mosaicShape[0] * tileShape[0], mosaicShape[1] * tileShape[1])
mosaic_img = np.zeros((imgShape[0], imgShape[1], 3), np.float32)
# avg_img = np.zeros((imgShape[0],imgShape[1]), np.float32)
numTiles = mosaicShape[0] * mosaicShape[1]
labels = pos_labels
if len(pos_labels) >= numTiles:
labels = random.sample(pos_labels, numTiles)
index = 0
for r in range(mosaicShape[0]):
for c in range(mosaicShape[1]):
if index >= len(labels):
break
label = labels[index]
index += 1
sample = getCroppedSampleFromLabel(kitti_base, label)
resized = resizeSample(sample, tileShape, label)
trs = tileShape[0]
tcs = tileShape[1]
tr = r * trs
tc = c * tcs
mosaic_img[tr:tr + trs, tc:tc + tcs] = resized
cv2.imwrite(fname, mosaic_img)
def saveAverageImage(kitti_base, pos_labels, shape, fname, avg_num=None):
num_images = float(len(pos_labels))
avg_num = min(avg_num, num_images)
if avg_num is None:
avg_num = num_images
# avg_img = np.zeros((shape[0],shape[1],3), np.float32)
avg_img = np.zeros(shape, np.float32)
progressbar = ProgressBar('Averaging ' + fname, max=len(pos_labels))
num = 0
for label in pos_labels:
if num >= avg_num:
break
num += 1
progressbar.next()
sample = getCroppedSampleFromLabel(kitti_base, label)
# sample = np.float32(sample)
resized = resizeSample(sample, shape, label)
resized = auto_canny(resized)
resized = np.float32(resized)
avg_img = cv2.add(avg_img, resized / float(avg_num))
progressbar.finish()
cv2.imwrite(fname, avg_img)
def find_label_clusters(kitti_base,
kittiLabels,
shape,
num_clusters,
descriptors=None):
if descriptors is None:
progressbar = ProgressBar('Computing descriptors',
max=len(kittiLabels))
descriptors = []
for label in kittiLabels:
progressbar.next()
img = getCroppedSampleFromLabel(kitti_base, label)
# img = cv2.resize(img, (shape[1], shape[0]), interpolation=cv2.INTER_AREA)
img = resizeSample(img, shape, label)
hist = get_hog(img)
descriptors.append(hist)
progressbar.finish()
else:
print 'find_label_clusters,', 'Using supplied descriptors.'
print len(kittiLabels), len(descriptors)
assert (len(kittiLabels) == len(descriptors))
# X = np.random.randint(25,50,(25,2))
# Y = np.random.randint(60,85,(25,2))
# Z = np.vstack((X,Y))
# convert to np.float32
Z = np.float32(descriptors)
# define criteria and apply kmeans()
K = num_clusters
print 'find_label_clusters,', 'kmeans:', K
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
attempts = 10
ret, label, center = cv2.kmeans(Z, K, None, criteria, attempts,
cv2.KMEANS_RANDOM_CENTERS)
# ret,label,center=cv2.kmeans(Z,2,criteria,attempts,cv2.KMEANS_PP_CENTERS)
print 'ret:', ret
# print 'label:', label
# print 'center:', center
# # Now separate the data, Note the flatten()
# A = Z[label.ravel()==0]
# B = Z[label.ravel()==1]
clusters = partition(kittiLabels, label)
return clusters
def find_label_clusters(kitti_base, kittiLabels, shape, num_clusters, descriptors=None):
if descriptors is None:
progressbar = ProgressBar('Computing descriptors', max=len(kittiLabels))
descriptors = []
for label in kittiLabels:
progressbar.next()
img = getCroppedSampleFromLabel(kitti_base, label)
# img = cv2.resize(img, (shape[1], shape[0]), interpolation=cv2.INTER_AREA)
img = resizeSample(img, shape, label)
hist = get_hog(img)
descriptors.append(hist)
progressbar.finish()
else:
print 'find_label_clusters,', 'Using supplied descriptors.'
print len(kittiLabels), len(descriptors)
assert(len(kittiLabels) == len(descriptors))
# X = np.random.randint(25,50,(25,2))
# Y = np.random.randint(60,85,(25,2))
# Z = np.vstack((X,Y))
# convert to np.float32
Z = np.float32(descriptors)
# define criteria and apply kmeans()
K = num_clusters
print 'find_label_clusters,', 'kmeans:', K
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
attempts = 10
ret,label,center=cv2.kmeans(Z,K,None,criteria,attempts,cv2.KMEANS_RANDOM_CENTERS)
# ret,label,center=cv2.kmeans(Z,2,criteria,attempts,cv2.KMEANS_PP_CENTERS)
print 'ret:', ret
# print 'label:', label
# print 'center:', center
# # Now separate the data, Note the flatten()
# A = Z[label.ravel()==0]
# B = Z[label.ravel()==1]
clusters = partition(kittiLabels, label)
return clusters
