def auc_score(self, ground_truth, predictions, **kwargs):
""" Calculate the AUC score for this particular trial.
This will also calculate the F scores and ROC curves
Args:
ground_truth: vector of class labels
predictions: vector of predicted class labels
Returns:
AUC score for this trial
"""
# calculate f scores
thresholded = threshold(predictions[:, 1], threshmin=0.5)
thresholded = threshold(thresholded, threshmax=0.5, newval=1.0).astype(int)
fhalf_score = metrics.fbeta_score(ground_truth.astype(int), thresholded, beta=0.5)
f2_score = metrics.fbeta_score(ground_truth.astype(int), thresholded, beta=2)
f1_score = metrics.fbeta_score(ground_truth.astype(int), thresholded, beta=1)
# calculate ROC curve and AUC
fpr, tpr, _ = metrics.roc_curve(ground_truth, predictions[:, 1])
area = metrics.auc(fpr, tpr)
self.fhalf_scores_.append(fhalf_score)
self.f2scores_.append(f2_score)
self.f1scores_.append(f1_score)
self.rates_.append((fpr, tpr))
self.aucs_.append(area)
return area
def thresh(im, thresh):
"""
Threshold image with threshdold value thresh
"""
tlow = threshold(im, threshmin=thresh, newval=0)
thigh = threshold(tlow, threshmax=thresh, newval=255)
return thigh.astype(np.uint8)
def otsu( small_img ):
#filter black and gray
img_R = small_img[:,:,0]
img_G = small_img[:,:,1]
img_B = small_img[:,:,2]
black = (img_R < 10 ) & (img_G < 10) & (img_B < 10)
small_img[:,:,:3][black] = [255,255,255]
grey = (img_R >= 220 ) & (img_G >= 220) & (img_B >=220)
small_img[:,:,:3][grey] = [255,255,255]
gray = cv2.cvtColor( small_img , cv2.COLOR_RGB2HSV )
mask = (gray[:,:,0] < 0.06) & (gray[:,:,1] < 0.06) & (gray[:,:,2]< 70)
small_img[:,:,:3][mask] = [255,255,255]
return small_img
#changing to gray scale
img_gray = cv2.cvtColor( small_img, cv2.COLOR_RGB2GRAY )
# clahe
clahe = cv2.createCLAHE()
img_cl = clahe.apply(img_gray);
# otsu algorithm
try:
thresh = threshold_otsu(img_cl)
threshold( small_img[:,:,:3] , thresh , [255,255,255] )
except:
pass
return small_img
def render_image(self, dir):
input_file = h5py.File(self.file, 'r')
wires = input_file['image/wires']
n = 1
scale = 100
thresh = 25
self.logger.info("""Producing {} images with
scale {} and threshold {}""".format(n, scale, thresh))
try:
image = wires[0]
self.logger.info("Image: min: {}, max: {}".format(
np.min(image), np.max(image)))
buff = np.ndarray(shape=(image.shape[1], image.shape[2],
image.shape[0]),
dtype=np.float64)
for i in range(3):
buff[:, :, i] = image[i, :, :]
buff = buff * scale
buff = threshold(buff, threshmin=thresh) + threshold(
buff, threshmax=-thresh)
self.logger.info("Buffer: min: {}, max: {}".format(
np.min(buff), np.max(buff)))
output_file = os.path.join(dir, 'wires.png')
imsave(output_file, buff)
except Exception as e:
self.logger.warning("problem creating image")
self.logger.warning(e)
def create_gist_parameters(self, selected_path, video_name):
org_images = images.read_video_as_list_by_path(selected_path,
video_name)
background_model = cv2.BackgroundSubtractorMOG2(len(org_images),
varThreshold=266,
bShadowDetection=True)
for frame in org_images:
background_model.apply(
frame, len(org_images)) # given frame, learning-rate
th = 150
fgmask_set = []
dp_mask = []
for frame in org_images:
forward = background_model.apply(
frame
) # create foreground mask which is gray-scale(0~255) image.
tmp = cv2.cvtColor(forward, cv2.COLOR_GRAY2BGR) # convert to color
dp_mask.append(tmp)
#convert gray-scale foreground mask to binary image.
a = stats.threshold(forward, threshmin=th, threshmax=255, newval=0)
a = stats.threshold(a, threshmin=0, threshmax=th, newval=1)
fgmask_set.append(a)
return fgmask_set, org_images, dp_mask
def recv_cmd(self):
data, address = self.sck.recvfrom(296)
uData = self.rec_fmt.unpack(data)
tempImage = np.asarray(uData[1:65])
tSamp = uData[70]
tempImageMat = np.reshape(tempImage, (16, 4))
tempRow = tempImageMat.sum(axis=1)/4
tempRowSq = tempRow**2
# Select a threshold as a percentace above the RMS value
tempRms = np.sqrt(tempRowSq.sum()/tempRow.size)
threshold = tempRms*1.06
# Binarizing the signal (0=no reading, 1=person detected)
defaultVals = stats.threshold(tempRow, threshmin=threshold, newval=0)
defaultVals = stats.threshold(defaultVals, threshmax=threshold,
newval=1)
# print(defaultVals)
span = 3.2 # Width of sensed floor
# print(defaultVals)
values = np.dot([i for i, x in enumerate(defaultVals) if x == 1],
span/15)
self.pos_values.append((tSamp, defaultVals))
if len(self.pos_values) > 100:
self.pos_values.pop(0)
def channel_enhance(self, img, channel, level=1):
if channel == 'B':
blue_channel = img[:, :, 0]
# blue_channel = (blue_channel - 128) * (level) +128
blue_channel = blue_channel * level
blue_channel = stats.threshold(blue_channel,
threshmax=255,
newval=255)
img[:, :, 0] = blue_channel
elif channel == 'G':
green_channel = img[:, :, 1]
# green_channel = (green_channel - 128) * (level) +128
green_channel = green_channel * level
green_channel = stats.threshold(green_channel,
threshmax=255,
newval=255)
img[:, :, 0] = green_channel
elif channel == 'R':
red_channel = img[:, :, 2]
# red_channel = (red_channel - 128) * (level) +128
red_channel = red_channel * level
red_channel = stats.threshold(red_channel,
threshmax=255,
newval=255)
img[:, :, 0] = red_channel
img = img.astype(np.uint8)
return img
def brightness_contrast(self, img, alpha=1.0, beta=0):
img_contrast = img * (alpha)
img_bright = img_contrast + (beta)
# img_bright = img_bright.astype(int)
img_bright = stats.threshold(img_bright, threshmax=255, newval=255)
img_bright = stats.threshold(img_bright, threshmin=0, newval=0)
img_bright = img_bright.astype(np.uint8)
return img_bright
def test_basic(self):
a = [-1, 2, 3, 4, 5, -1, -2]
assert_array_equal(stats.threshold(a), a)
assert_array_equal(stats.threshold(a, 3, None, 0),
[0, 0, 3, 4, 5, 0, 0])
assert_array_equal(stats.threshold(a, None, 3, 0),
[-1, 2, 3, 0, 0, -1, -2])
assert_array_equal(stats.threshold(a, 2, 4, 0), [0, 2, 3, 4, 0, 0, 0])
def brightness_contrast(self, img, alpha = 1.0, beta = 0): img_contrast = img * (alpha) img_bright = img_contrast + (beta) # img_bright = img_bright.astype(int) img_bright = stats.threshold(img_bright,threshmax=255, newval=255) img_bright = stats.threshold(img_bright,threshmin=0, newval=0) img_bright = img_bright.astype(np.uint8) return img_bright
def threshold_values(vector):
"""
Returns a new vector with all values < 0.5 set to 0, and all values >= 0.5
set to 1.
"""
tmp_vec = stats.threshold(vector, None, 0.5, 1)
tmp_vec = stats.threshold(tmp_vec, 0.5, None, 0)
return tmp_vec
def main():
connection = pg.connect("dbname = rem user = wireless password = wireless")
df = psql.read_sql("select occ, noise_floor, timetag from spectruminfo order by timetag DESC LIMIT 1000", connection)
tempocc = df['occ'].values
tempnf = df['noise_floor'].values
occ = np.zeros((df.shape[0],16))
nf = np.zeros((df.shape[0],16))
for i in range (0, len(occ)-1):
occ[i,:] = np.copy(np.array(tempocc[i]))
nf[i,:] = np.copy(np.array(tempnf[i]))
fitness = np.zeros((16,1))
plt.subplot(411)
for i in range(195,210):
plt.plot(occ[i,:])
plt.subplot(412)
plt.plot(occ[:,6])
#plt.plot(occ[:,6])
plt.subplot(413)
plt.plot(occ[:,13])
plt.subplot(414)
plt.plot(occ[:,12])
for i in range(0,16):
thr = np.mean(nf[:,i])
print 10.0/np.fabs(thr)
#print np.mean(occ[:,i])
occ[:,i] = stats.threshold(occ[:,i],threshmax = 10.0/np.fabs(thr), newval=1)
occ[:,i] = stats.threshold(occ[:,i],threshmin = 0.9, newval=0)
plt.subplot(413)
plt.plot(occ[:,13])
plt.subplot(412)
plt.plot(occ[:,8])
plt.subplot(414)
plt.plot(occ[:,1])
print bd.enumerate_bursts(occ[:,8], 'burstLabel')
# print zero_runs(occ[:,8])
#plt.hist(np.histogram(occ[:,0]), bins = [0, 1])
plt.show()
def test_basic(self):
a = [-1,2,3,4,5,-1,-2]
assert_array_equal(stats.threshold(a),a)
assert_array_equal(stats.threshold(a,3,None,0),
[0,0,3,4,5,0,0])
assert_array_equal(stats.threshold(a,None,3,0),
[-1,2,3,0,0,-1,-2])
assert_array_equal(stats.threshold(a,2,4,0),
[0,2,3,4,0,0,0])
def _minmaxThreshold(simMat, targetL):
nNodes = simMat.shape[0]
targetL *= 0.75 # heuristic adjustment to get roughly the right number of (undirected) links
# maxVals = simMat.copy()
maxVals = simMat.max(axis=1)
# print("MaxVals: " + str(maxVals))
maxVals[maxVals ==
0.0] = 1.0 # set max of empty rows to max possible sim value
threshold = float(min(maxVals))
thrSimMat = sps.threshold(simMat,
threshmin=threshold,
threshmax=None,
newval=0)
nLinks = np.count_nonzero(thrSimMat)
print("Thresholded to %d links" % nLinks)
if nLinks < targetL: # thresolding cut off too many links, use full matrix
print("Using full matrix")
thrSimMat = sps.threshold(simMat,
threshmin=-1,
threshmax=None,
newval=0)
nLinks = np.count_nonzero(thrSimMat)
if nLinks > targetL: # thresholding cut off too few links
newSimMat = np.matrix(np.zeros(simMat.shape))
frac = float(targetL) / nLinks
print("Keep frac = %f of the links" % frac)
# keep frac links in each row
nLinksRow = [np.count_nonzero(row) for row in thrSimMat]
for i in range(nNodes):
rowSim = thrSimMat[i].ravel()
linksTarget = int(math.floor(frac * nLinksRow[i])) + 1
idx = nNodes - linksTarget
threshold = np.partition(rowSim, idx)[idx]
rowLinks = sps.threshold(rowSim,
threshmin=threshold,
threshmax=None,
newval=0)
nRowLinks = np.count_nonzero(rowLinks)
if nRowLinks > 1.1 * linksTarget: # got too many, select at random, set unselected to zero
linkIdx = np.nonzero(rowLinks)[
0] # get indices of nonzero row elements
linkVals = rowLinks[
linkIdx] # compute link weights - 0.5*threshold
topIdx = np.argsort(
linkVals)[len(linkVals) -
linksTarget:] # get highest similarity links
linkIdx = linkIdx[topIdx]
# linkIdx = rnd.choice(linkIdx, linksTarget, replace=False, p=linkVals/np.sum(linkVals)) # (weighted) random draw the ones to keep
linkVals = rowLinks[linkIdx] # save the selected links
rowLinks = np.zeros(nNodes) # new links, all zero
rowLinks[linkIdx] = linkVals # assign the ones that were saved
newSimMat[i] = rowLinks
# print("Row %i contains %f links, should have %d. Threshold = %f"%(i, np.count_nonzero(newSimMat[i]), frac*nLinksRow[i], threshold))
simMat = newSimMat
else:
simMat = thrSimMat
return simMat
def hue_saturation(self, img_rgb, alpha = 1, beta = 1): img_hsv = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2HSV) hue = img_hsv[:,:,0] saturation = img_hsv[:,:,1] hue = stats.threshold(hue * alpha ,threshmax=179, newval=179) saturation = stats.threshold(saturation * beta,threshmax=255, newval=255) img_hsv[:,:,0] = hue img_hsv[:,:,1] = saturation img_transformed = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR) return img_transformed
def hue_saturation(self, img_rgb, alpha=1, beta=1):
img_hsv = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2HSV)
hue = img_hsv[:, :, 0]
saturation = img_hsv[:, :, 1]
hue = stats.threshold(hue * alpha, threshmax=179, newval=179)
saturation = stats.threshold(saturation * beta,
threshmax=255,
newval=255)
img_hsv[:, :, 0] = hue
img_hsv[:, :, 1] = saturation
img_transformed = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)
return img_transformed
def silence_ratio(signal, arg_dict):
t = arg_dict['silenceThreshold']
if np.isclose(np.max(signal), 0):
signal = threshold(signal, threshmin=t, newval=-100)
silent = np.where(signal == -100)[0]
return len(silent) / len(signal)
else:
# Normalization needed to max = 1
signal = signal / np.max(signal)
signal = threshold(signal, threshmin=t, newval=-100)
# print(signal)
silent = np.where(signal == -100)[0]
return len(silent) / len(signal)
def train(self):
D = self.A_true.shape[1]
for t in range(self.epo):
self.show_error()
for i in range(D):
start = time.time()
Yi = self.Y - self.A * self.Z + self.A[:, i] * self.Z[i, :]
fi = self.A[:, i].copy()
gi = self.Z[i, :].copy().transpose()
self.A[:, i] = 1.0/(norm(gi) * norm(gi)) * threshold(Yi * gi, threshmin = 0)
self.Z[i, :] = (1.0/(norm(fi) * norm(fi)) * threshold(Yi.transpose() * fi, threshmin = 0)).transpose()
end = time.time()
self.time = self.time + end - start
def detect_hand(color, depth):
thresh = threshold(
depth, threshmin=thresh_lo, threshmax=thresh_hi,
newval=0) #throw out all values that are too small or too large
thresh = threshold(thresh, threshmax=1,
newval=255) #make remaining values 255
thresh = thresh.astype(np.uint8)
kernel = np.ones((5, 5), np.uint8)
thresh = cv2.erode(thresh, kernel, iterations=1)
kernel = np.ones((7, 7), np.uint8)
thresh_dilation = cv2.dilate(thresh, kernel, iterations=1)
_, contours, _ = cv2.findContours(thresh_dilation, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
rois = [
] #regions of interest. each one is a tuple: (contour, area, center)
#print
print 'len(contours): ', len(contours)
for contour in contours:
r = cv2.minAreaRect(contour)
(r_cent, (w, h), r_angle) = r
if cv2.contourArea(contour) > min_contour and min(
[x for [[x, y]] in contour]) > 50:
M = cv2.moments(contour)
rois.append((contour, M['m00'], (int(M['m10'] / M['m00']),
int(M['m01'] / M['m00']))))
if (len(rois) > 0):
hand = max(
rois, key=lambda roi: roi[1])[0] # get the biggest roi as the hand
else:
return None, None, 0
#find the first local max enclosed circle greater than min_palm_circle
palm_cent = None
palm_radius = 0
miny = min([y for [(x, y)] in hand])
maxy = max([y for [(x, y)] in hand])
(vx, vy, x0, y0) = cv2.fitLine(hand, cv2.DIST_L2, 0, 0.01, 0.01)
for y in range(int(miny), int(maxy), 10):
x = float((y - y0) * vx / vy + x0)
radius = cv2.pointPolygonTest(hand, (x, y), True)
if (radius > min_palm_circle and radius > palm_radius):
palm_cent = (int(x), int(y))
palm_radius = radius
if (palm_cent != None and radius < 0.9 * palm_radius):
#hand = filter_hsv(color, depth, hand, palm_cent, palm_radius)
break #we reached a local min
return hand, palm_cent, palm_radius
def find_tstar(phi0, discretization=200):
""" finds tstar and t_opt (at discretization) for a given phi0"""
# define a periodic spline for the
start_time = pos_start_root + phi0 * 23.7 / (2 * np.pi)
prc_pos = stats.threshold(-pmodel.pPRC_interp(times + start_time)[:, 15],
threshmin=0)
prc_neg = stats.threshold(-pmodel.pPRC_interp(times + start_time)[:, 15],
threshmax=0)
prc = -pmodel.pPRC_interp(times + start_time)[:, 15]
prc_pos_spl = ut.PeriodicSpline(times, prc_pos, period=pmodel.T)
prc_neg_spl = ut.PeriodicSpline(times, prc_neg, period=pmodel.T)
def dphitot_dt(phis, t): # integrates everything
[phi_osc_pos, phi_osc_neg, phi_shift_pos, phi_shift_neg] = phis
dphi_osc_pos_dt = (2*np.pi)/pmodel.T +\
umax*prc_pos_spl(phi_osc_pos*pmodel.T/(2*np.pi))
dphi_osc_neg_dt = (2*np.pi)/pmodel.T +\
umax*prc_neg_spl(phi_osc_neg*pmodel.T/(2*np.pi))
dphi_shft_pos_dt = umax * prc_pos_spl(phi_osc_pos * pmodel.T /
(2 * np.pi))
dphi_shft_neg_dt = umax * prc_neg_spl(phi_osc_neg * pmodel.T /
(2 * np.pi))
return (dphi_osc_pos_dt, dphi_osc_neg_dt, dphi_shft_pos_dt,
dphi_shft_neg_dt)
int_times = np.linspace(0, 3 * pmodel.T, 10001)
delta_phis_total = integrate.odeint(dphitot_dt, [0, 0, 0, 0],
int_times,
hmax=0.001)
# get the delta phis
delta_phi_fs = np.linspace(0, 2 * np.pi, discretization)
cross_loc = np.min(
np.where(delta_phis_total[:, 2] - delta_phis_total[:, 3] >= 2 * np.pi))
t_star = int_times[cross_loc]
phi_star = delta_phis_total[cross_loc, 3] % (2 * np.pi)
t_opts = []
for phif in delta_phi_fs:
if phif > phi_star:
time_to_reach = np.min(int_times[np.where(
delta_phis_total[:, 3] + 2 * np.pi <= phif)])
else:
time_to_reach = np.min(
int_times[np.where(delta_phis_total[:, 2] >= phif)])
t_opts.append(time_to_reach)
return delta_phi_fs, np.asarray(t_opts), t_star, phi_star
def thresholdSimilarityMatrix(simMat, targetLS, fixed=False, directed=False):
nNodes = simMat.shape[0]
targetL = min(targetLS * nNodes, nNodes * (nNodes - 1) / 2)
nNonZero = np.count_nonzero(simMat)
print("[SimToNetwork.thresholdSimilarityMatrix] targetL nNonZero " +
str(targetL) + ' ' + str(nNonZero))
if nNonZero > 0: # sim matrix must have at least one non-zero entry
targetL *= 2
if fixed or nNonZero <= targetL: # keep links with sim above some value
idx = nNodes * nNodes - (min(targetL, nNonZero) - 1) - 1
threshold = simMat.flatten()
threshold.partition(idx)
print(threshold.shape)
threshold = float(threshold[idx])
print("partitioned threshold " + str(threshold))
simMat = np.matrix(sps.threshold(simMat, threshold, None, 0))
else: # threshold by min max sim so all nodes have at least one link, then threshold preserving relative number of links
simMat = _minmaxThreshold(simMat, targetL)
# simMat = _locallyAdaptiveThreshold(simMat, targetL)
if not directed:
# combine upper and lower triangles into final upper triangular similarity matrix
upper = np.triu(simMat, 1) # upper triangle
lower = np.tril(simMat, -1).T # transposed lower triangle
simMat = np.maximum(upper, lower) # take max
print("[SimToNetwork.thresholdSimilarityMatrix] newSimMat nNonZero " +
str(np.count_nonzero(simMat)))
return simMat
def threshold(array, threshold=5):
"""Set all pixels below threshold to zero.
Parameters
----------
array : `~numpy.ndarray`
Input array
threshold : float, optional
Minimum threshold
Returns
-------
data : `~numpy.ndarray`
Copy of input array with pixels below threshold set to zero.
"""
# TODO: np.clip is simpler, no?
from scipy import stats
# NaNs are set to 1 by thresholding, which is not
# what we want for detection, so we replace them with 0 here.
data = np.nan_to_num(array)
data = stats.threshold(data, threshold, None, 0)
# Note that scipy.stats.threshold doesn't binarize,
# it only sets values below the threshold to 0,
# which is not what we want here.
return data.astype(np.bool).astype(np.uint8)
def convertdigits_to_ampere(digits):
ampere = digits
ampere *= 0.04132
ampere += 0.3
ampere = threshold(ampere, 0.6)
return ampere
def compute_mean_of_non_zero(li_val, thres):
t1 = threshold(li_val, thres)
#t1 = li_val > bin_thres
t2 = np.count_nonzero(t1)
t3 = sum(t1)
mean = t3 / t2
return mean
def skeletonize(self, image):
image = grey_closing(image, footprint=circle(8), mode='constant', cval=0.0)
image = add_zero_mat(image)
prev_binary_image = np.zeros_like(image)
image_bit_depth = (image.dtype.itemsize * 8) / 2
print "image_bit_depth: " + str(image_bit_depth)
#image_thresholds = range(2**image_bit_depth,-1,-16)
image_thresholds = [2**x for x in range(image_bit_depth, 3, -1)] + range(15, 0, -1)
print "image_thresholds: " + str(image_thresholds)
for curr_threshold in image_thresholds:
print "curr_threshold: " + str(curr_threshold)
curr_thresh_image = threshold(image, curr_threshold)
curr_binary_image = curr_thresh_image.astype(np.bool).astype(np.int)
imsave(skeleton_images_path + "binary_" + str(curr_threshold) + ".png", curr_binary_image)
curr_sum_image = (prev_binary_image + curr_binary_image)
curr_skeleton_image = self.thin_pixels(curr_sum_image)
imsave(skeleton_images_path + "skeleton_" + str(curr_threshold) + ".png", curr_skeleton_image)
print "curr_skeleton max: " + str(curr_skeleton_image.max())
prev_binary_image = curr_skeleton_image
return remove_zero_mat(prev_binary_image)
def getFrame(frameNo, ID):
"""
@param frameNo int identifying the number of the frame in one file desired
@param ID int identifying the binary file currently being accessesed, suffix
of the filename of the binary file
@return im uint16 array containing the intensity data of one frame
with the background subtracted off and intensity above the threshold
This function returns the desired frame with the intensity data above the
threshold and the background data subtracted off.
"""
im = np.zeros(size)
if ID == 0:
IDstr = ''
else:
IDstr = str(ID)
filename = directory + filePrefix + IDstr
# reads the frame from the binary file into a string to be converted
if not os.path.isfile(filename):
sys.stdout.write("Cannot find the file: {0}. Check if it is in the right directory, or the filePrefix is correct.".format(filename))
sys.exit()
f = open(filename,'rb')
offset = header + (frameNo - 1)*frameSize
f.seek(offset)
im_data_hex = f.read(frameSize)
im = convertBin(im_data_hex)
f.close()
im = stats.threshold(im,threshmin = threshold, newval = 0)
return im
def err(parameter_shifts, future_ext_phases, mpc_time):
# assert that args are good
assert len(parameter_shifts)==len(future_ext_phases), \
"length mismatch between u, phi_ext"
osc_phases = np.zeros(len(parameter_shifts) + 1)
osc_phases[0] = mpc_phi
for i, pshift in enumerate(parameter_shifts):
# next oscilltor phase = curr phase + norm prog +integr pPRC
def dphidt(phi, t0):
return 2 * np.pi / (pmodel.T) + pshift * prcs(
pmodel._phi_to_t(phi))[:, control_vectors[0]]
osc_phases[i + 1] = integrate.odeint(
dphidt, osc_phases[i], single_step_ts)[-1]
#calc difference
p1 = osc_phases[1:]
p2 = future_ext_phases
differences = np.asarray([(p1 - p2) % (2 * np.pi),
(p2 - p1) % (2 * np.pi)]).min(0)
differences = stats.threshold(differences, threshmin=0.1)
#quadratic cost in time
weights = (np.arange(len(differences)) + 1)
return np.sum(weights * differences**2) + 0.001 * np.sum(
parameter_shifts**2)
def threshold(array, threshold=5):
"""Set all pixels below threshold to zero.
Parameters
----------
array : `~numpy.ndarray`
Input array
threshold : float, optional
Minimum threshold
Returns
-------
data : `~numpy.ndarray`
Copy of input array with pixels below threshold set to zero.
"""
# TODO: np.clip is simpler, no?
from scipy import stats
# NaNs are set to 1 by thresholding, which is not
# what we want for detection, so we replace them with 0 here.
data = np.nan_to_num(array)
data = stats.threshold(data, threshold, None, 0)
# Note that scipy.stats.threshold doesn't binarize,
# it only sets values below the threshold to 0,
# which is not what we want here.
return data.astype(np.bool).astype(np.uint8)
def save_results(batch_label, batch_infer, batch_diff_infer, batch_name,
result_list):
for i in xrange(len(batch_name)):
image_name = batch_name[i].split(" ")[0].replace(
".resnet_hypercolumn", ".jpg")
image_base_name = convert_image_name(image_name)
image = iuf.load_image(image_name)
image = iuf.resize_image(image, (FLAGS.label_row, FLAGS.label_col))
label_norm = iuf.repeat_image(iuf.norm_image(batch_label[i]))
num_car_label = np.sum(batch_label[i])
num_car_infer = np.sum(batch_infer[i]) + batch_diff_infer[i][0]
batch_infer[i] = ss.threshold(batch_infer[i], threshmin=0.0, newval=0)
infer_norm = iuf.repeat_image(iuf.norm_image(batch_infer[i]))
stack_image = np.hstack((image, label_norm, infer_norm))
#iuf.show_image(stack_image, normalize = False)
iuf.save_image(
stack_image, FLAGS.result_dir + "/" +
image_base_name.replace(".jpg", "resdeconv_result.jpg"))
batch_infer[i].tofile(FLAGS.result_dir + "/" +
image_base_name.replace(".jpg", ".npy"))
print("label: %.2f, infer: %.2f" % (num_car_label, num_car_infer))
result_list.append(image_name + " " + str(num_car_label) + " " +
str(num_car_infer))
def image_to_sketch(img):
"""
:param image: An image represented in numpy array with shape (height, width, 3) or (batch, height, width, 3)
:return: A sketch of the image with shape (height, width) or (batch, height, width)
"""
# We must apply a lower threshold. Otherwise the sketch image will be filled with non-zero values that may provide
# hints to the cnn trained. (It is unlikely to occur in human provided sketches that we have many pixels with
# brightness lower than 32. )
SKETCH_LOWEST_BRIGHTNESS = 32
if len(img.shape) == 4:
img_diff_dilation_gray = np.array([image_to_sketch(img[i,...]) for i in range(img.shape[0])])
return img_diff_dilation_gray
elif len(img.shape) == 3:
assert img.dtype == np.float32 # Otherwise the conversion does not work properly.
kernel = np.ones((5, 5), np.uint8)
img_dilation = cv2.dilate(img, kernel, iterations=1)
img_diff_dilation = np.abs(np.subtract(img, img_dilation))
img_diff_dilation_gray = cv2.cvtColor(img_diff_dilation, cv2.COLOR_RGB2GRAY)
img_diff_dilation_gray_thresholded = threshold(img_diff_dilation_gray, SKETCH_LOWEST_BRIGHTNESS)
return img_diff_dilation_gray_thresholded
else:
print('Image has to be either of shape (height, width, num_features) or (batch_size, height, width, num_features)')
raise AssertionError
def create_mask(foreground, background, blur_sigma, thresh, opacity):
"""
Given two RGB images, foreground and background,
give a mask of the areas where foreground differs from the
background by more than thresh.
Apply blur_sigma amount of blurring, and set the opacity of
the nonzero parts of the mask to opacity.
"""
blurred_fg = gaussian_filter(foreground, blur_sigma).astype(int)
blurred_bg = gaussian_filter(background, blur_sigma).astype(int)
diff = np.sum(np.abs(fg - bg), axis=2)
diff = threshold(diff, threshmin=thresh, newval=0)
diff = threshold(diff, threshmax=thresh+1, newval=opacity)
diff = gaussian_filter(diff, blur_sigma).astype(np.uint8)
return diff
def plot_W(W, threshmin=None, threshmax=None):
W_filtered = threshold(np.abs(W), threshmin=threshmin, threshmax=threshmax)
row_indices, column_indices = np.indices(W_filtered.shape)
plt.scatter(column_indices.flatten(),
row_indices.flatten(),
s=W_filtered.flatten(),
color='black',
marker='s')
def prepareDistributionImage(image):
valmax = np.amax(image)
if valmax > 0:
scale = valmax / 255.0
else:
scale = 1
img = stats.threshold(image, np.median(image))
return img / scale
def spread_spectrum(img):
img = stats.threshold(img, threshmin=12, newval=0)
# see http://docs.opencv.org/3.1.0/d5/daf/tutorial_py_histogram_equalization.html
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
img = clahe.apply(img)
return img
def prepareDistributionImage(image):
valmax = np.amax(image)
if valmax>0:
scale = valmax/255.0
else:
scale = 1
img = stats.threshold(image, np.median(image))
return img/scale
def compare_rows(a,row):
"""take numpy array a and compare to row row
Return new numpy array of same size as a containing 0's and 1's
0's indicate element of a was greater than corresponding element of row
1's indicate element of a was less than corresponding element of row"""
import numpy as np
from scipy import stats
(number_rows,number_cols) = np.shape(a)
b = np.zeros_like(a)
for i in range(number_rows):
b[i,:] = a[i,:] - row[:]
b = stats.threshold(b, threshmin=0, newval=-1)
b = stats.threshold(b, threshmax=0, newval=0)
b = -1*b
return b
def remove_background_percent(frame, thresh=.5, average=None):
if average is None:
max = np.max(frame)
else:
max = n_largest(frame, average)
max = int(np.mean(max))
threshold = max - (thresh * max)
frame = sp.threshold(frame, threshold, None, 0)
return frame
def rgb2gray_and_binary(raw):
"""
converts color img to binary image( foreground 1 , background 0)
:param raw: image list.
:return: binary image list
"""
fg_list = []
if len(raw[0].shape) == 2:
for i in range(len(raw)):
fg_list.append(
stats.threshold(raw[i], threshmin=0, threshmax=0, newval=1))
else:
for i in range(len(raw)):
tmp = cvtColor(raw[i], COLOR_RGB2GRAY)
fg_list.append(
stats.threshold(tmp, threshmin=0, threshmax=0, newval=1))
return fg_list
def plot_2d_max(image, layer, th=-300):
# p = image.transpose(2,1,0)
# p = p[:,:,::-1]
p = image.transpose(0, 1, 2)
p = threshold(p, threshmax=th)
print p.shape
plt.imshow(p[layer], cmap=plt.cm.gray)
plt.show()
def compare_rows(a, row):
"""take numpy array a and compare to row row
Return new numpy array of same size as a containing 0's and 1's
0's indicate element of a was greater than corresponding element of row
1's indicate element of a was less than corresponding element of row"""
import numpy as np
from scipy import stats
(number_rows, number_cols) = np.shape(a)
b = np.zeros_like(a)
for i in range(number_rows):
b[i, :] = a[i, :] - row[:]
b = stats.threshold(b, threshmin=0, newval=-1)
b = stats.threshold(b, threshmax=0, newval=0)
b = -1 * b
return b
def proj_unit_disk(w, t=None):
"""
we receive a vector [v1,v2,...,vp] and project [v1,v2,...,vp-1] on the positive unit disk.
"""
v = threshold(w, threshmin=0, newval=0)
if np.linalg.norm(v)**2 <= 1:
return v
else:
return v / np.linalg.norm(v)
def map_channels(i_x):
i, x = i_x
x = (x * 255).astype(np.uint8)
if x.max() > 0.35 * 255:
threshold = np.fabs(x.max() - x.max() * .65)
else:
threshold = 255
threshImage = stats.threshold(x, threshmin=threshold)
threshImage[threshImage > 0] = i
return threshImage
def processPixels (self, pixels):
pixels_np = np.array(pixels)
# Difference from baseline
pixels_np = self.baseline - pixels_np
# normalize
p_max = np.max(pixels_np) * 1.0
p_min = np.min(pixels_np) * 1.0
pixels_np = (pixels_np - p_min) / (p_max - p_min)
# std > 0.1 -> no coins
#print("std:", np.std(pixels_np))
# low pass filter
pixels_np = stats.threshold(pixels_np, threshmin=0.70, newval=0.0)
# set all else to max (255)
pixels_np = stats.threshold(pixels_np, threshmax=0.1, newval=255.0)
return pixels_np.tolist()
def remove_background_farthest(frame, thresh, average=None):
if average is None:
max = np.min(frame)
else:
max = n_smallest(frame, average)
max = int(np.mean(max))
if max == 0:
max = np.mean(frame)
threshold = max + (thresh * max)
frame = sp.threshold(frame, threshold, None, 0)
return frame
def channel_enhance(self, img, channel, level=1): if channel == 'B': blue_channel = img[:,:,0] # blue_channel = (blue_channel - 128) * (level) +128 blue_channel = blue_channel * level blue_channel = stats.threshold(blue_channel,threshmax=255, newval=255) img[:,:,0] = blue_channel elif channel == 'G': green_channel = img[:,:,1] # green_channel = (green_channel - 128) * (level) +128 green_channel = green_channel * level green_channel = stats.threshold(green_channel,threshmax=255, newval=255) img[:,:,0] = green_channel elif channel == 'R': red_channel = img[:,:,2] # red_channel = (red_channel - 128) * (level) +128 red_channel = red_channel * level red_channel = stats.threshold(red_channel,threshmax=255, newval=255) img[:,:,0] = red_channel img = img.astype(np.uint8) return img
def xyzs(self, drift_vel=None, clock=None, pads=None, peaks_only=False):
"""Find the scatter points of the event in space.
If a drift velocity and write clock frequency are provided, then the result gives the z dimension in
meters. Otherwise, the z dimension is measured in time buckets.
Parameters
----------
drift_vel : number or array-like
The drift velocity in the detector, in cm/us. This can be the scalar magnitude or a vector. A vectorial
drift velocity can be used to correct for the Lorentz angle.
clock : int or float, optional
The write clock rate, in MHz
pads : ndarray, optional
An array of pad vertices. If provided, these pads will be used instead of the default pad plane.
peaks_only : bool, optional
If True, only the peak of each activation curve will be used.
Returns
-------
xyzs : numpy.ndarray
A 4D array of points including (x, y, tb or z, activation)
See also
--------
pytpc.simulation.drift_velocity_vector
"""
if peaks_only:
traces_copy = numpy.copy(self.traces)
for i, tr in enumerate(traces_copy):
traces_copy[i]['data'][:] = threshold(tr['data'], threshmin=tr['data'].max(), newval=0)
nz = traces_copy['data'].nonzero()
else:
nz = self.traces['data'].nonzero()
if pads is None:
pads = generate_pad_plane()
pcenters = pads.mean(1)
xys = numpy.array([pcenters[self.traces[i]['pad']] for i in nz[0]])
zs = nz[1].reshape(nz[1].shape[0], 1)
cs = self.traces['data'][nz].reshape(nz[0].shape[0], 1)
xyzs = numpy.hstack((xys, zs, cs))
if drift_vel is not None and clock is not None:
xyzs = calibrate(xyzs, drift_vel, clock)
return xyzs
def pack_thresh(R, Th=0.3, PosOnly=0):
'''
A function to transform the correlation matrix to the uppder diagonal only,
and threshold with a given parameter. The resulting file compresses very
compactly.
input parameters:
R: A full correlation matrix of size V x V.
Th: A threshold to eliminate small correlation values.
For positive elements, R>Th
For negative elements, R<-Th (if PosOnly=0)
The default value of Th is 0.3.
PosOnly: A flag to indicate whether the thresholded correlation
matrix should only include positive values.
PosOnly=0: Both positive and negative correlations
are retained.
PosOnly=1: Only positive correlations are retained
PosOnly=-1: Only negative correlations are retained
(with R<-Th)
returns:
thrR_csr: The thresholded sparse correaltion matrix.
'''
# upper triangle only
uR = np.triu(R,1)
# thresholding
if PosOnly==0:
thuRpos = stats.threshold(uR, threshmin=Th, newval=0)
thuRneg = stats.threshold(uR, threshmax=-Th, newval=0)
thuR = thuRpos + thuRneg
elif PosOnly==1:
thuR = stats.threshold(uR, threshmin=Th, newval=0)
elif PosOnly==-1:
thuR = stats.threshold(uR, threshmax=-Th, newval=0)
# sparse matrix
thuR_csr = sparse.csr_matrix(thuR)
return thuR_csr
def threshold_frame( frame, nstd = None):
# Change the parameter to one's liking. Currently low threshold value is 3
# std more than mean.
mean = int(frame.mean())
std = int(frame.std())
if nstd is None:
nstd = 3
low = max(0, mean + (nstd * std))
high = int( frame.max() )
logging.debug("Thresholding at %s + %s * %s" % (mean, nstd, std))
logging.debug("|- low, high = %s, %s" % (low, high))
frame = stat.threshold( frame, low, high, newval = 0)
return to_grayscale( frame )
def thresh_com(x, y):
"""Threshold the results at the half-range level and find their
centre of mass.
:param x: The array of the last set of positions, sorted in
ascending order.
:param y: The brightness measurements corresponding to x.
:return: The centre of mass in position and the thresholded
brightness array."""
thresh_level = np.mean([np.min(y), np.max(y)])
thresh = stat.threshold(y, threshmin=thresh_level)
normalisation = integrate.simps(thresh, x)
numerator = integrate.simps(x * thresh, x)
return numerator / normalisation, thresh
def collabStandardK(self, X, Xflip, K, lenoflist):
#K=10
Y = dist.squareform(dist.pdist(Xflip, "jaccard"))
#find the closest K rows
Y = np.nan_to_num(Y) #house keeping for na's
U = np.argsort(Y)[:, :K]
#average them
out = np.mean(Xflip[U], 1)
#threshold back to binary
out = stats.threshold(out, threshmax=0.5, newval=1)
out = stats.threshold(out, threshmin=0.49, newval=0)
#plt.matshow(out,cmap='Greys_r')
print("\nComputed RMS Errors from Collaborative filtering")
print self.errors(X, out)
print("\n\n")
plt.title("CF - users")
figstring = "outputs/CF_plot_" + str(lenoflist)
plt.savefig(figstring)
#plt.show()
cnt = 0
for i in X:
for j in i:
if (j == 1):
cnt = cnt + 1
print("Count of 1's in X: " + str(cnt))
cnt = 0
for i in out:
for j in i:
if (j == 1):
cnt = cnt + 1
print("Count of 1's in result: " + str(cnt))
def _findLocalMax(self):
"""Find the centers of particles by thresholding and dilating."""
dilationKernel = im.makeCircularKernel(self._dilationRadius)
maxed = []
for image in self._morphed:
# set pixels below morph thresh to 0
threshed = stats.threshold(image, self._morphThreshold, newval=0.0)
dilated = cv2.dilate(threshed, dilationKernel)
# expThreshold is so named because the original algorithm
# originally exponentiated and then thresholded, which is the same
# as flipping the sign and exponentiating the threshold.
binary = (dilated - threshed) >= self._expThreshold
maxed.append(binary)
self._maxed = maxed
def _findLocalMax(self):
"""Find the centers of particles by thresholding and dilating."""
dilationKernel = im.makeCircularKernel(self._dilationRadius)
self._maxed = im.createImageArray(self, "morphMax",
dtype=np.bool,
shape = self._morphed[0].shape,
expectedrows=len(self._morphed))
for image in self._morphed:
# set pixels below morph thresh to 0
threshed = stats.threshold(image, self._morphThreshold, newval=0.0)
dilated = cv2.dilate(threshed, dilationKernel)
# expThreshold is so named because the original algorithm
# originally exponentiated and then thresholded, which is the same
# as flipping the sign and exponentiating the threshold.
binary = (dilated - threshed) >= self._expThreshold
self._maxed.append([binary])
self._maxed.flush()
def HRinst(dataset, secperunit=60, peak_threshold=98, filt=True):
"""
Takes the input data of the time and voltage to convert it into an array
with time and instantaneous heart rate.
:param dataset: (tuple) Two elements, each a 1xN ndarray for time and
voltage values respectively
:param secperunit: (int or double) Conversion from unit of time ndarray
to seconds
:param peak_threshold: (double) percentage of maximum peak to set
thresholding
:param filt: (boolean) true if user wants to filter data, false if not
:returns: (ndarray) 2 columns. First column with time in s, second column
with heart rate in BPM. Each element in the ndarray is a float.
"""
import numpy as np
from scipy import stats
time = dataset[:][0]
voltage = dataset[:][1]
if filt:
voltage = data_filter(voltage)
thresholded = stats.threshold(voltage,
np.percentile(voltage, peak_threshold))
hrinst = np.zeros(len(thresholded))
is_increasing = np.roll(thresholded, 1) <= thresholded
will_decrease = np.roll(thresholded, -1) < thresholded
is_maximum = is_increasing * will_decrease
peakInd = np.asarray(np.where(is_maximum))
for i, val in enumerate(thresholded):
peaks = peakInd[peakInd < i]
if i > peakInd[0][1]:
hrinst[i] = \
secperunit / (time[int(peaks[-1])] - time[int(peaks[-2])])
else:
hrinst[i] = 0
hrinst[-1] = hrinst[-2]
return hrinst
def isolate_depths(frame, expand=1.0, length=256, blur=None):
"""Isolate depths of a greyscale depth image
frame: frame to analyse
expand: amount to expand the range of the isolation
length: possible values of pixels
blur: amount to blur, None for none
"""
if blur is not None:
frame = cv2_medianBlur(frame, blur, frame)
expand -= (expand - 1) / 2
count = __get_bins(length)
ranges = __get_depths_range(count).astype(float)
ranges[:, 1] *= expand
ranges[:, 0] /= expand
ranges = ranges.astype(uint8)
iso = [threshold(frame, start, stop - 1) for start, stop in ranges]
if len(iso) == 0:
return zeros_like(frame)
return np_sum(iso, 0).astype(uint8)
def detectFeatures(grayImage):
print 'Detecting features for image...'
gaussImage = ndimage.filters.gaussian_filter(grayImage, 5)
gradY, gradX = np.gradient(gaussImage)
gradXX = gradX * gradX
gradYY = gradY * gradY
gradXY = gradX * gradY
gauXX = ndimage.filters.gaussian_filter(gradXX, 5)
gauYY = ndimage.filters.gaussian_filter(gradYY, 5)
gauXY = ndimage.filters.gaussian_filter(gradXY, 5)
Det = gauXX * gauYY - gauXY * gauXY
Trace = gauXX + gauYY
R = Det - k * Trace * Trace
ThresR = stats.threshold(R, thresholdR)
ThresRdilate = ndimage.grey_dilation(ThresR, footprint=[[1, 1, 1], [1, 0, 1], [1, 1, 1]])
ThresRpeak = ThresR > ThresRdilate
features = ThresRpeak.nonzero()
features = zip(features[1], features[0]) #(column, row), (width, height) (x, y)
print 'Detected', len(features), 'features'
return features
def toImage(image_data):
"""
@param image_data uint16 array containing intensities from binary files
This function converts the intensity data from all files into high-res and low-res images.
"""
"""
Plots the intensity data using matplotlib; faster but high resolution is lost
"""
# Removes values below a threshold to make the background of the ring images
# black
image_data.shape = size
plt.imshow(np.minimum(stats.threshold(image_data,threshmin=threshold, newval=0), 255 + 0*image_data))
# Specifies the color map; can be modified if you want!
plt.hot()
plt.grid()
# Saves image to output directory
plt.savefig(imDirectory + output + "-lo.png")
"""
Plots the intensity map by converting intensities to RGB values; slower
but has higher resolution
"""
# Determines max intensity for the gradient
maxI = np.max(image_data)
# creates an mxnx3 array of zeros of type uint8; this array will store
# the RGB values that will be converted to an image
rgbArr = np.zeros((size[0],size[0],3),dtype = 'uint8')
sys.stdout.write("Converting to Image\n")
for i in range(size[0]):
for j in range(size[0]):
# Converts intensity to pixel
rgbArr[i,j] = toRGB(image_data[i][j],maxI)
image = Image.fromarray(rgbArr,'RGB')
# Saves image to output director provided
image.save(imDirectory + output + ".png")
return image_data
def toImage(image_data):
"""
@param im_data uint16 array containing intensities from binary files
This function converts the intensity data from all files into an image and saves it
"""
"""
Plots the intensity data using matplotlib; faster but high resolution is lost
"""
# Removes values below a threshold to make the background of the ring images
# black
image_data.shape = size
plt.imshow(np.minimum(stats.threshold(image_data,threshmin=threshold, newval=0), 255 + 0*image_data))
# Specifies the color map; can be modified if you want!
plt.hot()
# Saves image to output directory
plt.savefig(imDirectory + output + "-lo.png")
"""
Plots the intensity map by converting intensities to RGB values; slower
but has higher resolution
"""
# Determines max intensity for the gradient
maxI = np.max(image_data)
# creates an mxnx3 array of zeros of type uint8; this array will store
# the RGB values that will be converted to an image
rgbArr = np.zeros((size[0],size[0],3),dtype = 'uint8')
sys.stdout.write("Converting to Image\n")
