def callback(self, ros_data):
'''Callback function of subscribed topic.
Here images get converted and features detected'''
if VERBOSE:
print 'received image of type: "%s"' % ros_data.format
#### direct conversion to CV2 ####
np_arr = np.fromstring(ros_data, np.uint8)
image_np = cv2.imdecode(np_arr, cv2.CV_LOAD_IMAGE_COLOR)
# image_np = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) # OpenCV >= 3.0
detector = cv2.FeatureDetector_create("SIFT")
descriptor = cv2.DescriptorExtractor_create("SIFT")
kp1 = detector.detect(img1)
kp1, des1 = descriptor.compute(img1, kp1)
kp2 = detector.detect(img2)
kp2, des2 = descriptor.compute(img2, kp2)
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)
matches = list(
map(
lambda a: [a[0]],
filter(lambda a: a[0].distance < 0.75 * a[1].distance,
matches)))
matches = sorted(matches, key=lambda val: val[0].distance)
dist = np.mean(list(map(lambda x: x[0].distance, matches[:25])))
msg = str(dist)
# if dist < 170:
# Publish new image
self.image_pub.publish(msg)
self.subscriber.unregister()
def matches(path1, path2):
img1 = cv2.imread(path1, cv2.CV_LOAD_IMAGE_GRAYSCALE)
img2 = cv2.imread(path2, cv2.CV_LOAD_IMAGE_GRAYSCALE)
detector = cv2.FeatureDetector_create("SURF")
descriptor = cv2.DescriptorExtractor_create("BRIEF")
matcher = cv2.DescriptorMatcher_create("BruteForce-Hamming")
# detect keypoints
kp1 = detector.detect(img1)
kp2 = detector.detect(img2)
print '#keypoints in image1: %d, image2: %d' % (len(kp1), len(kp2))
# descriptors
k1, d1 = descriptor.compute(img1, kp1)
k2, d2 = descriptor.compute(img2, kp2)
print '#keypoints in image1: %d, image2: %d' % (len(d1), len(d2))
# match the keypoints
matches = matcher.match(d1, d2)
# visualize the matches
print '#matches:', len(matches)
dist = [m.distance for m in matches]
print 'distance: min: %.3f' % min(dist)
print 'distance: mean: %.3f' % (sum(dist) / len(dist))
print 'distance: max: %.3f' % max(dist)
# threshold: half the mean
thres_dist = (sum(dist) / len(dist)) * 0.5
# keep only the reasonable matches
sel_matches = [m for m in matches if m.distance < thres_dist]
print '#selected matches:', len(sel_matches)
'''
def callback(self, ros_data):
'''Callback function of subscribed topic.
Here images get converted and features detected'''
if VERBOSE:
print 'received image of type: "%s"' % ros_data.format
#### direct conversion to CV2 ####
np_arr = np.fromstring(ros_data.data, np.uint8)
image_np = cv2.imdecode(np_arr, cv2.CV_LOAD_IMAGE_COLOR)
#image_np = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) # OpenCV >= 3.0:
#### Feature detectors using CV2 ####
# "","Grid","Pyramid" +
# "FAST","GFTT","HARRIS","MSER","ORB","SIFT","STAR","SURF"
method = "GridFAST"
feat_det = cv2.FeatureDetector_create(method)
time1 = time.time()
# convert np image to grayscale
featPoints = feat_det.detect(cv2.cvtColor(image_np,
cv2.COLOR_BGR2GRAY))
time2 = time.time()
if VERBOSE:
print '%s detector found: %s points in: %s sec.' % (
method, len(featPoints), time2 - time1)
for featpoint in featPoints:
x, y = featpoint.pt
cv2.circle(image_np, (int(x), int(y)), 3, (0, 0, 255), -1)
cv2.imshow('cv_img', image_np)
cv2.waitKey(2)
#### Create CompressedIamge ####
msg = CompressedImage()
msg.header.stamp = rospy.Time.now()
msg.format = "jpeg"
msg.data = np.array(cv2.imencode('.jpg', image_np)[1]).tostring()
# Publish new image
self.image_pub.publish(msg)
def extract_features_sift(image, config):
detector = cv2.FeatureDetector_create('SIFT')
descriptor = cv2.DescriptorExtractor_create('SIFT')
detector.setDouble('edgeThreshold', config.get('sift_edge_threshold', 10))
sift_peak_threshold = float(config.get('sift_peak_threshold', 0.01))
while True:
print 'Computing sift with threshold {0}'.format(sift_peak_threshold)
t = time.time()
detector.setDouble("contrastThreshold", sift_peak_threshold)
points = detector.detect(image)
print 'Found {0} points in {1}s'.format(len(points), time.time() - t)
if len(points) < config.get('feature_min_frames',
0) and sift_peak_threshold > 0.0001:
sift_peak_threshold = (sift_peak_threshold * 2) / 3
print 'reducing threshold'
else:
print 'done'
break
points, desc = descriptor.compute(image, points)
if config.get('feature_root', False): desc = root_feature(desc)
points = np.array([(i.pt[0], i.pt[1], i.size, i.angle) for i in points])
return points, desc
def get_sift_descriptors_and_matches(img1, img2):
'''
img1 is test image, img2 is template
'''
kp1, des1 = SIFT.detectAndCompute(img1, None)
kp2, des2 = SIFT.detectAndCompute(img2, None)
# FLANN parameters
detector = cv2.FeatureDetector_create("SIFT")
descriptor = cv2.DescriptorExtractor_create("SIFT")
kps1 = detector.detect(img1)
kps1, descr1 = descriptor.compute(img1, kps1)
kps2 = detector.detect(img2)
kps2, descr2 = descriptor.compute(img2, kps2)
flann_params = dict(algorithm=1, trees=10)
flann = cv2.flann_Index(descr1, flann_params)
idx, dist = flann.knnSearch(descr2, 1, params={})
return idx, dist, descr1, descr2
def MatchAllCaptureGlass(save, maxdist=200):
from os.path import isfile, join
import freenect
from os import listdir
import cv2
import numpy as np
import itertools
import sys
#Prepare a list of different training images
pathGlass = "TrainingImages/Glass/"
GlassCups = [ f for f in listdir(pathGlass) if isfile(join(pathGlass,f)) and f[0]<>"."]
img, timestamp = freenect.sync_get_video()
depth, timestamp = freenect.sync_get_depth(format=freenect.DEPTH_REGISTERED)
detector = cv2.FeatureDetector_create("FAST")
descriptor = cv2.DescriptorExtractor_create("SIFT")
skp = detector.detect(img)
skp, sd = descriptor.compute(img, skp)
KeyPointsTotalList = []
DistsTotalList = []
for i in GlassCups:
temp = cv2.imread(str(pathGlass+"/"+i))
KeyPointsOut = findKeyPointsDist(img,temp,skp,sd,maxdist)
KeyPointsTotalList += KeyPointsOut[0]
DistsTotalList += KeyPointsOut[1]
indices = range(len(DistsTotalList))
indices.sort(key=lambda i: DistsTotalList[i])
DistsTotalList = [DistsTotalList[i] for i in indices]
KeyPointsTotalList = [KeyPointsTotalList[i] for i in indices]
img1 = img
if save == 1:
saveImageMappedPoints(img, KeyPointsTotalList, 1)
return KeyPointsTotalList, DistsTotalList, img, depth
def detectAndDescribe(self, image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 版本区别
if self.isv3:
# 使用SIFT_create()方法实例化DOG空间关键点
descriptor = cv2.xfeatures2d.SIFT_create()
#将关键点与特征向量分离
(kps, features) = descriptor.detectAndCompute(image, None)
else:
detector = cv2.FeatureDetector_create("SIFT")
kps = detector.detect(gray)
extractor = cv2.DescriptorExtractor_create("SIFT")
(kps, features) = extractor.compute(gray, kps)
# 将关键点转化为Numpy数组
kps = np.float32([kp.pt for kp in kps])
# 返回关键点与特征向量的元组
return (kps, features)
def detectAndDescribe(self, image):
# convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# check to see if we are using OpenCV 3.X
if self.isv3:
# detect and extract features from the image
descriptor = cv2.xfeatures2d.SIFT_create()
(kps, features) = descriptor.detectAndCompute(image, None)
# otherwise, we are using OpenCV 2.4.X
else:
# detect keypoints in the image
detector = cv2.FeatureDetector_create("SIFT")
kps = detector.detect(gray)
# extract features from the image
extractor = cv2.DescriptorExtractor_create("SIFT")
(kps, features) = extractor.compute(gray, kps)
# use only keppoints of sand and line
# print(kps, features)
court_kps = []
court_features = []
for index, kp in enumerate(kps):
y, x = kp.pt
b, g, r = image[int(ceil(x)), int(ceil(y))]
if utils.isSand(r, g, b) or utils.isLine(r, g, b):
court_kps.append(kp)
court_features.append(features[index])
# convert the keypoints from KeyPoint objects to NumPy
# arrays
court_kps = np.float32([kp.pt for kp in court_kps])
court_features = np.asarray(court_features)
# return a tuple of keypoints and features
return (court_kps, court_features)
def detectPiecesSIFT(self, correct_board):
self.board = np.zeros((8, 8))
sift_detector = cv2.FeatureDetector_create("SIFT")
sift_extractor = cv2.DescriptorExtractor_create("SIFT")
centers = np.load("feature_data/SIFT/centers.npy")
probabilities = np.zeros((7, 8, 8))
for piece in pieces:
piece_class = piece_classes[piece]
ratio = piece_to_ratio[piece]
winSize = (int(64 * ratio), 64)
classifier = joblib.load("classifiers/classifier_sift_" + piece +
".pkl")
for r in xrange(8):
for f in xrange(8):
bounding_box = self.getBoundingBox(r, f, piece)
x1 = bounding_box[0]
x2 = bounding_box[1]
y1 = bounding_box[2]
y2 = bounding_box[3]
subimage = self.image[y1:y2, x1:x2]
subimage = cv2.resize(subimage, winSize)
features = preprocessing.generateBOWFeatures(
subimage, centers, sift_detector, sift_extractor)
prob = classifier.predict_proba(features)
probabilities[piece_class, 7 - r, f] = prob[0, 1]
# print(probabilities[0,:,:])
# print(probabilities[1,:,:])
# print(probabilities[2,:,:])
self.board = np.argmax(probabilities, axis=0)
self.probabilities = probabilities
cross_entropy = self.cross_entropy(correct_board)
detection_accuracy = self.detection_error(correct_board)
classification_accuracy = self.classification_error(correct_board)
return (cross_entropy, detection_accuracy, classification_accuracy)
def extract_features_sift(image, config):
sift_edge_threshold = config['sift_edge_threshold']
sift_peak_threshold = float(config['sift_peak_threshold'])
if context.OPENCV3:
try:
detector = cv2.xfeatures2d.SIFT_create(
edgeThreshold=sift_edge_threshold,
contrastThreshold=sift_peak_threshold)
except AttributeError as ae:
if "no attribute 'xfeatures2d'" in ae.message:
logger.error('OpenCV Contrib modules are required to extract SIFT features')
raise
descriptor = detector
else:
detector = cv2.FeatureDetector_create('SIFT')
descriptor = cv2.DescriptorExtractor_create('SIFT')
detector.setDouble('edgeThreshold', sift_edge_threshold)
while True:
logger.debug('Computing sift with threshold {0}'.format(sift_peak_threshold))
t = time.time()
if context.OPENCV3:
detector = cv2.xfeatures2d.SIFT_create(
edgeThreshold=sift_edge_threshold,
contrastThreshold=sift_peak_threshold)
else:
detector.setDouble("contrastThreshold", sift_peak_threshold)
points = detector.detect(image)
logger.debug('Found {0} points in {1}s'.format(len(points), time.time() - t))
if len(points) < config['feature_min_frames'] and sift_peak_threshold > 0.0001:
sift_peak_threshold = (sift_peak_threshold * 2) / 3
logger.debug('reducing threshold')
else:
logger.debug('done')
break
points, desc = descriptor.compute(image, points)
if config['feature_root']:
desc = root_feature(desc)
points = np.array([(i.pt[0], i.pt[1], i.size, i.angle) for i in points])
return points, desc
def up_to_step_1(imgs):
"""Complete pipeline up to step 1: Detecting features and descriptors"""
# construct a DoG keypoint detector and a SURF feature extractor
surf_detector = cv2.FeatureDetector_create("SURF")
surf_detector.setInt("hessianThreshold", 1500)
surf_extractor = cv2.DescriptorExtractor_create("SURF")
for img in imgs:
# convert to gray image
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# detect keypoints in the image
kp = surf_detector.detect(img_gray, None)
# extract features from the image
(kp, features) = surf_extractor.compute(img_gray, kp)
# draw features on image
img = cv2.drawKeypoints(
image=img,
outImage=img,
keypoints=kp,
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
return imgs
def obtainSimilarityScore(img1, img2):
detector = cv2.FeatureDetector_create("SIFT")
descriptor = cv2.DescriptorExtractor_create("SIFT")
skp = detector.detect(img1)
skp, sd = descriptor.compute(img1, skp)
tkp = detector.detect(img2)
tkp, td = descriptor.compute(img2, tkp)
num1 = 0
for i in range(len(sd)):
kp_value_min = np.inf
kp_value_2min = np.inf
for j in range(len(td)):
kp_value = 0
for k in range(128):
kp_value = (sd[i][k] - td[j][k]) * (sd[i][k] -
td[j][k]) + kp_value
if kp_value < kp_value_min:
kp_value_2min = kp_value_min
kp_value_min = kp_value
if kp_value_min < 0.8 * kp_value_2min:
num1 = num1 + 1
num2 = 0
for i in range(len(td)):
kp_value_min = np.inf
kp_value_2min = np.inf
for j in range(len(sd)):
kp_value = 0
for k in range(128):
kp_value = (td[i][k] - sd[j][k]) * (td[i][k] -
sd[j][k]) + kp_value
if kp_value < kp_value_min:
kp_value_2min = kp_value_min
kp_value_min = kp_value
if kp_value_min < 0.8 * kp_value_2min:
num2 = num2 + 1
K1 = num1 * 1.0 / len(skp)
K2 = num2 * 1.0 / len(tkp)
SimilarityScore = 100 * (K1 + K2) * 1.0 / 2
return SimilarityScore
def getKeypointsDescriptors(filenames,detector_type,descriptor_type):
detector=cv2.FeatureDetector_create(detector_type)
if not(descriptor_type == 'color'):
if not (descriptor_type == 'HOG' or descriptor_type == 'LBP'):
descriptor = cv2.DescriptorExtractor_create(descriptor_type)
K = []
D = []
print 'Extracting Local Descriptors'
init=time.time()
for filename in filenames:
ima=cv2.imread(filename)
gray=cv2.cvtColor(ima,cv2.COLOR_BGR2GRAY)
if descriptor_type == 'HOG':
des = extractHOGfeatures(gray, detector)
D.append(des)
elif descriptor_type == 'LBP':
des = extractLBPfeatures(gray, detector)
D.append(des)
else:
kpts=detector.detect(gray)
kpts,des=descriptor.compute(gray,kpts)
K.append(kpts)
D.append(des)
end=time.time()
print 'Done in '+str(end-init)+' secs.'
if(descriptor_type == 'color'):
K = []
print 'Extracting Local Descriptors'
init=time.time()
for filename in filenames:
ima=cv2.imread(filename)
gray=cv2.cvtColor(ima,cv2.COLOR_BGR2GRAY)
kpts=detector.detect(gray)
K.append(kpts)
D = getLocalColorDescriptors(filenames, K, 0)
return(K,D)
def __init__(self, source=0, bb=None):
self.mouse_p1 = None
self.mouse_p2 = None
self.mouse_drag = False
self.bb = None
self.img = None
self.source = source
self.detector = cv2.FeatureDetector_create(sys.argv[1])
self.descriptor = cv2.DescriptorExtractor_create(sys.argv[1])
self.flann_params = dict(algorithm=1, trees=4)
if source:
self.cam = cv2.VideoCapture(source)
else:
self.cam = cv2.VideoCapture(0)
if not bb:
_, self.img = self.cam.read()
self.start()
else:
self.bb = bb
_, self.img = self.cam.read()
self.SIFT()
def CheckSVM(self, img):
logData("checking image on svm classifier")
clf, classes_names, stdSlr, k, voc = joblib.load(
SVM_TRAINED_FILE_LOCATION)
fea_det = cv.FeatureDetector_create(
SVM_FEATURE_DETECTOR_EXTRACTOR_TYPE)
des_ext = cv.DescriptorExtractor_create(
SVM_FEATURE_DETECTOR_EXTRACTOR_TYPE)
des_list = []
kpts = fea_det.detect(img)
kpts, des = des_ext.compute(img, kpts)
des_list.append((self.image_path, des))
# Stack all the descriptors vertically in a numpy array
descriptors = des_list[0][1]
for image_path, descriptor in des_list[0:]:
descriptors = np.vstack((descriptors, descriptor))
#
test_features = np.zeros((self.image_paths_len, k), "float32")
for i in xrange(1):
words, distance = vq(des_list[i][1], voc)
for w in words:
test_features[i][w] += 1
# Perform Tf-Idf vectorization
nbr_occurences = np.sum((test_features > 0) * 1, axis=0)
idf = np.array(
np.log(
(1.0 * self.image_paths_len + 1) / (1.0 * nbr_occurences + 1)),
'float32')
# Scale the features
test_features = stdSlr.transform(test_features)
# Perform the predictions
predictions = [classes_names[i] for i in clf.predict(test_features)]
predictionForReturn = clf.predict(test_features)
for prediction in predictions:
logData("SVM PREDICTIONS ARE [" + prediction +
"] sending result as is " + str(predictionForReturn[0]))
return predictionForReturn[0]
def _extract_feature(X, feature):
if feature == 'gray' or feature == 'surf':
X = [cv2.cvtColor(x, cv2.COLOR_BGR2GRAY) for x in X]
elif feature == 'hsv':
X = [cv2.cvtColor(x, cv2.COLOR_BGR2HSV) for x in X]
small_size = (32, 32)
X = [cv2.resize(x, small_size) for x in X]
if feature == 'surf':
surf = cv2.SURF(400)
surf.upright = True
surf.extended = True
num_surf_features = 36
dense = cv2.FeatureDetector_create("Dense")
kp = dense.detect(np.zeros(small_size).astype(np.uint8))
kp_des = [surf.compute(x, kp) for x in X]
X = [d[1][:num_surf_features, :] for d in kp_des]
elif feature == 'hog':
block_size = (small_size[0] / 2, small_size[1] / 2)
block_stride = (small_size[0] / 4, small_size[1] / 4)
cell_size = block_stride
num_bins = 9
hog = cv2.HOGDescriptor(small_size, block_size, block_stride,
cell_size, num_bins)
X = [hog.compute(x) for x in X]
elif feature is not None:
X = np.array(X).astype(np.float32) / 255
X = [x - np.mean(x) for x in X]
X = [x.flatten() for x in X]
return X
def _detectAndDescribe(self, image):
# convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# OpenCV 3.X
if self.isv3:
# detect and extract features from the image
descriptor = cv2.xfeatures2d.SIFT_create()
(kps, features) = descriptor.detectAndCompute(image, None)
# OpenCV 2.4.X
else:
# detect keypoints in the image
detector = cv2.FeatureDetector_create("SIFT")
kps = detector.detect(gray)
# extract features from the image
extractor = cv2.DescriptorExtractor_create("SIFT")
(kps, features) = extractor.compute(gray, kps)
# convert the keypoints from KeyPoint objects to NumPy arrays
kps = np.float32([kp.pt for kp in kps])
return (kps, features)
def generator_centers(data_root, K, downsample=1):
dense = cv2.FeatureDetector_create("Dense")
brief = cv2.DescriptorExtractor_create("SIFT")
image_filenames = []
for fpathe, dirs, fs in os.walk(data_root):
for f in fs:
name = str(f)
if name.endswith('.jpg'):
image_filenames.append(os.path.join(fpathe, f))
cnt = 0
points = []
for image_filename in image_filenames:
cnt += 1
if cnt % downsample > 0:
continue
img = cv2.imread(image_filename, cv2.IMREAD_GRAYSCALE)
if img.mean() > 250:
continue
kp = dense.detect(img, None)
kp, des = brief.compute(img, kp)
for t in des:
points.append(t)
#print("# kps: {}, descriptors: {}".format(len(kp), des.shape) )
print('sample points: {0}x{1}'.format(len(points), len(points[0])))
criteria = (cv2.TERM_CRITERIA_EPS, 3000, 0.01)
flags = cv2.KMEANS_PP_CENTERS
ret, labels, centers = cv2.kmeans(np.asarray(points), K, criteria, 10,
flags)
print('mse: {0}'.format(math.sqrt(ret) / len(points)))
with open('train1-{0}-{1}.sift.feature'.format(downsample, K), 'w') as f:
centers.dump(f)
return centers
def JellemzoFelismer(self, image):
# szürkeárnyalat
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# OpenCv ellenőrzés
if self.isv3:
# jellemzők felismerése
descriptor = cv2.xfeatures2d.SIFT_create()
(kps, features) = descriptor.detectAndCompute(image, None)
# OpenCv 2.4.X
else:
# kulcspont felismerés
detector = cv2.FeatureDetector_create("SIFT")
kps = detector.detect(gray)
# jellemzők meghatározása
extractor = cv2.DescriptorExtractor_create("SIFT")
(kps, features) = extractor.compute(gray, kps)
# NumPy vektorokba konvertálja
kps = np.float32([kp.pt for kp in kps])
return (kps, features)
def blob_proc(self, img):
# don't use me, im bad
d_red = cv2.RGB(150, 55, 65)
l_red = cv2.RGB(250, 200, 200)
detector = cv2.FeatureDetector_create('MSER')
fs = detector.detect(img)
fs.sort(key=lambda x: -x.size)
sfs = [x for x in fs if not self.supress(x, fs)]
for f in sfs:
cv2.circle(img, (int(f.pt[0]), int(f.pt[1])), int(f.size / 2),
d_red, 2, cv2.CV_AA)
cv2.circle(img, (int(f.pt[0]), int(f.pt[1])), int(f.size / 2),
l_red, 1, cv2.CV_AA)
h, w = orig.shape[:2]
vis = np.zeros((h, w * 2 + 5), np.uint8)
vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
vis[:h, :w] = orig
vis[:h, w + 5:w * 2 + 5] = img
return vis
def create_sample_default_detectors(yaml_dir):
"""
This function creates sample feature yaml files in the directory \
specified by yaml_dir to provide coverage of all the supported feature \
detectors in opencv's feat2d module and their parameters
:param string yaml_dir: full path to write sample feature yaml files
:returns: None
"""
global __CV2_FT_DETECTORS__
for detector in __CV2_FT_DETECTORS__:
fd = cv2.FeatureDetector_create(detector)
out_yaml = {
'detector': {
'name': detector,
'parameters': build_parameter_dictionary(fd)
}
}
out_name = os.path.join(yaml_dir +
'default_detector_%s.yaml' % detector)
outfile = file(out_name, 'w')
yaml.dump(out_yaml, stream=outfile)
outfile.close()
def __init__(self, robot_file):
rospack = rospkg.RosPack()
# loads the stock images for left, right and uturn signs
self.left_turn = rospack.get_path('computer_vision') + '/images/left_turn_real.png'
self.right_turn = rospack.get_path('computer_vision') + '/images/right_turn_real.png'
self.u_turn = rospack.get_path('computer_vision') + '/images/uturn_real.png'
self.robot_file = robot_file
descriptor_name = "SIFT"
self.left_sum = 0
self.right_sum = 0
self.u_sum = 0
self.threshold_sum = 20
self.detector = cv2.FeatureDetector_create(descriptor_name)
self.extractor = cv2.DescriptorExtractor_create(descriptor_name)
self.matcher = cv2.BFMatcher()
self.im = None
self.corner_threshold = 0.01
self.ratio_threshold = .6
def extract_ref(image_path, thumbnail_size):
img = Image.open(image_path)
img.load()
if img == None:
raise Exception("Error: Cannot read " + image_path)
if img.size > thumbnail_size:
img.thumbnail(thumbnail_size)
img_array = numpy.array(img)
img_gray = img_array
if len(img_array.shape) >= 3:
img_gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
img_gray = numpy.array(img_gray, numpy.uint8)
featureDetector = cv2.FeatureDetector_create("SURF")
keypoints = featureDetector.detect(img_gray)
kp_angles = []
if keypoints == None:
return []
for keypoint in keypoints:
kp_angles.append(keypoint.angle)
bins = range(0, 360, 10)
histogram = numpy.histogram(kp_angles, bins=bins, density=True)[0]
return histogram
def detect_mser(image,
mask,
layer_id,
smooth=True,
show=False,
show_now=True,
save_fig=False):
if image.ndim == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
detector = cv2.FeatureDetector_create('MSER')
keypts = detector.detect(image)
keypts = [kp for kp in keypts if mask[kp.pt[1], kp.pt[0]]]
if show or save_fig:
fig = plt.figure(figsize=(24, 14))
for kp in keypts:
plt.imshow(image, 'gray', interpolation='nearest')
plt.title('layer=%i' % (layer_id + 1))
r = int(0.5 * kp.size)
x, y = kp.pt
circ = plt.Circle((x, y), r, color='r', linewidth=2, fill=False)
plt.gca().add_patch(circ)
if save_fig:
fig_dir = '/home/tomas/Dropbox/Work/Dizertace/figures/keypoints/mser/'
dirs = fig_dir.split('/')
for i in range(2, len(dirs)):
subdir = '/'.join(dirs[:i])
if not os.path.exists(subdir):
os.mkdir(subdir)
fig.savefig(os.path.join(fig_dir,
'mser_layer_%i.png' % (layer_id + 1)),
dpi=100,
bbox_inches='tight',
pad_inches=0)
if show_now:
plt.show()
def compare(filename1, filename2):
img1 = cv2.imread(filename1) # queryImage
img2 = cv2.imread(filename2) # trainImage
# Initiate SIFT detector
detector = cv2.FeatureDetector_create("SIFT")
descriptor = cv2.DescriptorExtractor_create("SIFT")
# sift = cv2.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1 = detector.detect(img1)
kp1, des1 = descriptor.compute(img1, kp1)
kp2 = detector.detect(img2)
kp2, des2 = descriptor.compute(img2, kp2)
# kp1, des1 = sift.detectAndCompute(img1,None)
# kp2, des2 = sift.detectAndCompute(img2,None)
# BFMatcher with default params
bf = cv2.BFMatcher()
# matches = bf.match(des1,des2)
matches = bf.knnMatch(des1, des2, k = 2)
matches = list(map(lambda a: [a[0]], filter(lambda a: a[0].distance < 0.75*a[1].distance, matches)))
# matches = sorted(matches, key=lambda val: val.distance)
matches = sorted(matches, key=lambda val: val[0].distance)
# img3 = drawMatches(img1,kp1,img2,kp2,matches[:25])
# img = np.zeros([max(img1.shape[0],img2.shape[0]),img1.shape[1]+img2.shape[1],3])
# img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,img,flags=2)
dist = np.mean(list(map(lambda x: x[0].distance, matches[:25])))
print(dist)
def init(self, img_shape, feature2d_detector, detector_config,
feature2d_descriptor, framesout, max_dist_as_img_fraction):
##
# Initialization
##
self.max_dist = max(img_shape) / max_dist_as_img_fraction
self.detector = cv2.FeatureDetector_create(feature2d_detector)
for name, value in detector_config[feature2d_detector].items():
cv2_setParam(self.detector, self.detector.paramType(name), name,
value)
self.descriptor = cv2.DescriptorExtractor_create(feature2d_descriptor)
FLANN_INDEX_KDTREE = 0
self.matcher = cv2.FlannBasedMatcher(
dict(algorithm=FLANN_INDEX_KDTREE, trees=5), dict(checks=50))
self.tracks = TrackCollection() #
# clear files
self.framesout = framesout
assert ("%04d" in framesout)
for framefile in glob.glob(framesout.replace("%04d", "*")):
os.remove(framefile)
def visualize_boxes_and_labels_on_image_array(image,
boxes,
classes,
scores,
category_index,
instance_masks=None,
keypoints=None,
use_normalized_coordinates=False,
max_boxes_to_draw=20,
min_score_thresh=.5,
agnostic_mode=False,
line_thickness=4):
box_to_display_str_map = collections.defaultdict(list)
box_to_color_map = collections.defaultdict(str)
box_to_instance_masks_map = {}
box_to_keypoints_map = collections.defaultdict(list)
################### CALL train.pkl ###################################
clf, classes_names, stdSlr, k, voc = joblib.load("train.pkl")
if not max_boxes_to_draw:
max_boxes_to_draw = boxes.shape[0]
for i in range(min(max_boxes_to_draw, boxes.shape[0])):
#>>>>>>>>>>>
elapsed = time.time() - start
#<<<<<<<<<<<<
if scores is None or scores[i] > min_score_thresh:
box = tuple(boxes[i].tolist())
####################### SIFT #######################################
try:
fea_det = cv2.FeatureDetector_create("SIFT")
des_ext = cv2.DescriptorExtractor_create("SIFT")
des_list = []
y = int(box[0] * 479.000)
yh = int(box[2] * 479.000)
x = int(box[1] * 639.000)
xh = int(box[3] * 639.000)
# print y, " ", yh, " ", x, " ",xh
im = image[y:yh, x:xh]
kpts = fea_det.detect(im)
kpts, des = des_ext.compute(im, kpts)
des_list.append((im, des))
descriptors = des_list[0][1]
for image2, descriptor in des_list[0:]:
descriptors = np.vstack((descriptors, descriptor))
test_features = np.zeros((1, k), "float32")
for i in xrange(1):
words, distance = vq(des_list[i][1], voc)
for w in words:
test_features[i][w] += 1
test_features = stdSlr.transform(test_features)
# print clf.predict(test_features) >>>>>> class n. [0] [1]...[n]
predictions = [
classes_names[i] for i in clf.predict(test_features)
]
p = str(predictions[0])
st = p + "#" + str(y) + "," + str(yh) + "," + str(
x) + "," + str(xh)
#print p
f.setPredic(st)
if instance_masks is not None:
box_to_instance_masks_map[box] = instance_masks[i]
if keypoints is not None:
box_to_keypoints_map[box].extend(keypoints[i])
if scores is None:
box_to_color_map[box] = 'black'
else:
if not agnostic_mode:
display_str = '{}'.format(
p) ############# predictions[0] ##################
box_to_display_str_map[box].append(display_str)
if agnostic_mode:
box_to_color_map[box] = 'DarkOrange'
else:
box_to_color_map[box] = STANDARD_COLORS[
classes[i] % len(STANDARD_COLORS)]
except:
print "..."
# Draw all boxes onto image.
for box, color in box_to_color_map.items():
ymin, xmin, ymax, xmax = box
if instance_masks is not None:
draw_mask_on_image_array(image,
box_to_instance_masks_map[box],
color=color)
#return (ymin, ymax, xmin, xmax)
f.setYmin(ymin)
f.setYmax(ymax)
f.setXmin(xmin)
f.setXmax(xmax)
f.setimage(image)
#save_image_array_as_png(image,"/home/uawsscu/PycharmProjects/DetectML/object_recognition_detection/pic/l3l.png")
# print "(ymin ", ymin, ")(xmin ", xmin,")(ymax ", ymax,")(xmax ", xmax,")"
draw_bounding_box_on_image_array(
image,
ymin,
xmin,
ymax,
xmax,
color=color,
thickness=line_thickness,
display_str_list=box_to_display_str_map[box],
use_normalized_coordinates=use_normalized_coordinates)
if keypoints is not None:
draw_keypoints_on_image_array(
image,
box_to_keypoints_map[box],
color=color,
radius=line_thickness / 2,
use_normalized_coordinates=use_normalized_coordinates)
import cv2
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import scipy as sp
img_file = 'cn-tower-1.jpg'
img = cv2.imread(img_file)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
src = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sift = cv2.SIFT()
detector = cv2.FeatureDetector_create("SIFT")
descriptor = cv2.DescriptorExtractor_create("SIFT")
src = np.uint8(src)
kp = detector.detect(src, None)
from __future__ import print_function
import numpy as np
import cv2
import imutils
# load the image and convert it to grayscale
image = cv2.imread("kp_next.jpg")
orig = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# handle if we are detecting FAST keypoints in the image for OpenCV 2.4
if imutils.is_cv2():
detector = cv2.FeatureDetector_create("FAST")
kps = detector.detect(gray)
else:
detector = cv2.FastFeatureDetector_create()
kps = detector.detect(gray, None)
print("# of keypoints: {}".format(len(kps)))
# loop over the keypoints and draw them
for kp in kps:
r = int(0.5 * kp.size)
(x, y) = np.int0(kp.pt)
cv2.circle(image, (x, y), r, (0, 255, 255), 2)
cv2.imshow("Images", np.hstack([orig, image]))
cv2.waitKey(0)
orientations=8,
pixels_per_cell=(8, 8),
cells_per_block=(1, 1),
visualise=True)
# Rescale histogram for better display
hog_image_rescaled = exposure.rescale_intensity(hog_image,
in_range=(0, 0.02)) * 8
glcm = greycomatrix(hog_image_rescaled, [5], [0],
64,
symmetric=True,
normed=True)
#http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_brief/py_brief.html#brief
#BRIEF
star = cv2.FeatureDetector_create("STAR")
# Initiate BRIEF extractor
brief = cv2.DescriptorExtractor_create("BRIEF")
# find the keypoints with STAR
kp = star.detect(img, None)
# compute the descriptors with BRIEF
kp, des = brief.compute(img, kp)
des /= 8
glcm = greycomatrix(des, [5], [0], 64, symmetric=True, normed=True)
#http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.html#sift-intro
#SIFT
sift = cv2.SIFT()
kp, des = sift.detectAndCompute(img, None)
des /= 8
glcm = greycomatrix(des, [5], [0], 64, symmetric=True, normed=True)