def saveImage(data, nodeAllocations, opts):
global WIDTH
global HEIGHT
global PAGESIZE
# Collapse "step" rows that are completely empty.
step = 4
collapser = numpy.zeros(WIDTH*HEIGHT, numpy.uint32)
for begin, end in ((WIDTH*y, WIDTH*y + WIDTH*step)
for y in xrange(0, HEIGHT, step)):
if any(data[begin:end]):
collapser[begin:end] = 1
data = data.compress(collapser)
# Create the output false colors picture. Rescale the values in the
# data array so that they are normalised to 245 and convert the array
# to 8 bit so that it can be easily used with the paletted mode.
# Notice that we leave the last ten colors (245 - 255) to highlight
# fragmented memory allocations coming from a unique stacktrace node.
# Determine the 10 most fragmented allocations coming from the same symbol
# and highlight the pages they touch.
# Reshape the array to be bidimensional rather than linear.
data *= 245
data /= PAGESIZE
# Calculate an highlight mask to hightlight top MEM_LIVE allocations.
if topTen[0]:
highlightNode, highlighNodeName = topTen[4][0]
for (pos, size) in nodeAllocations[highlightNode]:
paintAllocation(data, pos, size, 246)
WIDTH = int(sqrt(len(data)))
data = numpy.reshape(data, (len(data)/WIDTH, WIDTH))
HEIGHT, WIDTH = data.shape
pilImage = fromarray(numpy.array(data, dtype=numpy.uint8), 'P')
lut = [0,0,0]
for x in (int(i / 245. * 255.) for i in range(245)):
lut.extend([x*0.8, x*0.8 , x*0.8])
# Extra colors are all purple.
for x in range(10):
lut.extend([186., 7., 17.])
pilImage.putpalette(lut)
d = ImageDraw(pilImage)
for c in xrange(256):
w, h = WIDTH*0.8 / 256, HEIGHT*0.01
x, y = c * w+WIDTH*0.1, HEIGHT*0.9 - h
d.rectangle((x, y, x+w, y+h), fill=c)
pilImage = pilImage.resize((WIDTH*2, HEIGHT*2))
pilImage.save(opts.output)
def saveImage(data, nodeAllocations, opts):
global WIDTH
global HEIGHT
global PAGESIZE
# Collapse "step" rows that are completely empty.
step = 4
collapser = numpy.zeros(WIDTH * HEIGHT, numpy.uint32)
for begin, end in ((WIDTH * y, WIDTH * y + WIDTH * step)
for y in xrange(0, HEIGHT, step)):
if any(data[begin:end]):
collapser[begin:end] = 1
data = data.compress(collapser)
# Create the output false colors picture. Rescale the values in the
# data array so that they are normalised to 245 and convert the array
# to 8 bit so that it can be easily used with the paletted mode.
# Notice that we leave the last ten colors (245 - 255) to highlight
# fragmented memory allocations coming from a unique stacktrace node.
# Determine the 10 most fragmented allocations coming from the same symbol
# and highlight the pages they touch.
# Reshape the array to be bidimensional rather than linear.
data *= 245
data /= PAGESIZE
# Calculate an highlight mask to hightlight top MEM_LIVE allocations.
if topTen[0]:
highlightNode, highlighNodeName = topTen[4][0]
for (pos, size) in nodeAllocations[highlightNode]:
paintAllocation(data, pos, size, 246)
WIDTH = int(sqrt(len(data)))
data = numpy.reshape(data, (len(data) / WIDTH, WIDTH))
HEIGHT, WIDTH = data.shape
pilImage = fromarray(numpy.array(data, dtype=numpy.uint8), 'P')
lut = [0, 0, 0]
for x in (int(i / 245. * 255.) for i in range(245)):
lut.extend([x * 0.8, x * 0.8, x * 0.8])
# Extra colors are all purple.
for x in range(10):
lut.extend([186., 7., 17.])
pilImage.putpalette(lut)
d = ImageDraw(pilImage)
for c in xrange(256):
w, h = WIDTH * 0.8 / 256, HEIGHT * 0.01
x, y = c * w + WIDTH * 0.1, HEIGHT * 0.9 - h
d.rectangle((x, y, x + w, y + h), fill=c)
pilImage = pilImage.resize((WIDTH * 2, HEIGHT * 2))
pilImage.save(opts.output)
def save_to_image(filename, img):
"""
Save to ".jpg", the image
"""
# Append ".jpg" to filename if not present
filename = append_type(filename, '.jpg')
# If img is numpy array, convert it to PIL image
if isinstance(img, ndarray):
img = fromarray(uint8(img))
# Convert the image as necessary
if not img.mode == 'L':
img = img.convert('L')
img.save(filename, 'jpeg')
def make(**kw):
step, noise, decay = [float(kw[k]) for k in ['--step','--noise','--decay']]
X, Y = [int(kw[k]) for k in ['--width', '--height']]
H = X/2
gray = ones((Y,X),dtype=float)
for x in range(H):
dI = step * exp(-decay*float(H-x))
gray[:,0+x+0] += dI
gray[:,X-x-1] -= dI
scatter = ((random.random((Y,X))*2)-1) * noise
image = (gray+scatter)*127
saved = fromarray(image)
name = 'egray/egray.s.%f.n.%f.d.%f.gif' % (step, noise, decay)
saved.save(name)
toimage(saved)
def make(**kw):
step = kw.get( 'step', 3e-2)
scale = kw.get('scale', 3e-1)
shape = (Y, X) = [kw.get(edge, 512) for edge in ['Y', 'X']]
X0, X1, X2, X3 = 0, X/2-1, X/2, X-1
gray = ones(shape, dtype=float)
gray[:,X0:X1] -= step
gray[:,X2:X3] += step
noise = ((random.random((Y,X))*2)-1) * scale
image = (gray+noise)*127
print gray.max(), noise.max(), image.max()
print gray.min(), noise.min(), image.min()
saved = fromarray(image)
name = 'gray/gray.step.%f.scale.%f.gif' % (step, scale)
print name
saved.save(name)
def save(self, kernel, R, dx=0, dy=0, **kw):
rescaled = (255.0 * kernel).astype('uint8')
name = "kernels/Airy/kernel.Airy.R%d.dx%1.2f.dy%1.2f" % (R, dx, dy)
image = fromarray(rescaled)
image.save(name+".png")
scipy.save(name+".npy", kernel)
pos3 = line.rfind(' ')
pos4 = line.find(')')
extent.append(float(line[4:pos1]))
extent.append(float(line[pos1+1:pos2]))
extent.append(float(line[pos2+1:pos3]))
extent.append(float(line[pos3+1:pos4]))
f2.close()
"""
if cellsize == "":
w = extent[2] - extent[0]
h = extent[1] - extent[3]
image_w = 600
image_h = h * image_w / w
cellsize = max(w / image_w, h / image_h)
std_x = std(x)
std_y = std(y)
Q_x = quantile(x)
Q_y = quantile(y)
IQR_x = Q_x[2] - Q_x[0]
IQR_y = Q_y[2] - Q_y[0]
h_x = 0.9 * min(std_x, IQR_x / 1.34) * pow(n, -0.2)
h_y = 0.9 * min(std_y, IQR_y / 1.34) * pow(n, -0.2)
bandwidth = max(h_x, h_y) * 2
else:
cellsize = float(cellsize)
bandwidth = float(bandwidth)
itv_pts_ids = range(n)
arr, rows, cols, gmin, gmax = call_kde(n, x, y, itv_pts_ids, extent, bandwidth, cellsize, kernel, gradient, opaque)
fromarray(arr).save("test1.png")
def shaded_relief(in_file, raster_band, color_file, out_file_name,
azimuth=315, angle_altitude=45):
'''
The main function. Reads the input image block by block to improve the performance, and calculates the shaded relief image
'''
if exists(in_file) is False:
raise Exception('[Errno 2] No such file or directory: \'' + in_file + '\'')
dataset = gdal.Open(in_file, GA_ReadOnly )
if dataset == None:
raise Exception("Unable to read the data file")
band = dataset.GetRasterBand(raster_band)
block_sizes = band.GetBlockSize()
x_block_size = block_sizes[0]
y_block_size = block_sizes[1]
#If the block y size is 1, as in a GeoTIFF image, the gradient can't be calculated,
#so more than one block is used. In this case, using8 lines gives a similar
#result as taking the whole array.
if y_block_size < 8:
y_block_size = 8
xsize = band.XSize
ysize = band.YSize
max_value = band.GetMaximum()
min_value = band.GetMinimum()
#Reading the color table
color_table = readColorTable(color_file)
#Adding an extra value to avoid problems with the last & first entry
if sorted(color_table.keys())[0] > min_value:
color_table[min_value - 1] = color_table[sorted(color_table.keys())[0]]
if sorted(color_table.keys())[-1] < max_value:
color_table[max_value + 1] = color_table[sorted(color_table.keys())[-1]]
#Preparing the color table
classification_values = color_table.keys()
classification_values.sort()
max_value = band.GetMaximum()
min_value = band.GetMinimum()
if max_value == None or min_value == None:
stats = band.GetStatistics(0, 1)
max_value = stats[1]
min_value = stats[0]
out_array = zeros((3, ysize, xsize), 'uint8')
#The iteration over the blocks starts here
for i in range(0, ysize, y_block_size):
if i + y_block_size < ysize:
rows = y_block_size
else:
rows = ysize - i
for j in range(0, xsize, x_block_size):
if j + x_block_size < xsize:
cols = x_block_size
else:
cols = xsize - j
dem_array = band.ReadAsArray(j, i, cols, rows)
hs_array = hillshade(dem_array, azimuth,
angle_altitude)
rgb_array = values2rgba(dem_array, color_table,
classification_values, max_value, min_value)
hsv_array = rgb_to_hsv(rgb_array[:, :, 0],
rgb_array[:, :, 1], rgb_array[:, :, 2])
hsv_adjusted = asarray( [hsv_array[0],
hsv_array[1], hs_array] )
shaded_array = hsv_to_rgb( hsv_adjusted )
out_array[:,i:i+rows,j:j+cols] = shaded_array
#Saving the image using the PIL library
im = fromarray(transpose(out_array, (1,2,0)), mode='RGB')
im.save(out_file_name)
pos4 = line.find(')')
extent.append(float(line[4:pos1]))
extent.append(float(line[pos1+1:pos2]))
extent.append(float(line[pos2+1:pos3]))
extent.append(float(line[pos3+1:pos4]))
f2.close()
"""
if cellsize == "":
w = extent[2] - extent[0]
h = extent[1] - extent[3]
image_w = 600
image_h = h * image_w / w
cellsize = max(w / image_w, h / image_h)
std_x = std(x)
std_y = std(y)
Q_x = quantile(x)
Q_y = quantile(y)
IQR_x = Q_x[2] - Q_x[0]
IQR_y = Q_y[2] - Q_y[0]
h_x = 0.9 * min(std_x, IQR_x / 1.34) * pow(n, -0.2)
h_y = 0.9 * min(std_y, IQR_y / 1.34) * pow(n, -0.2)
bandwidth = max(h_x, h_y) * 2
else:
cellsize = float(cellsize)
bandwidth = float(bandwidth)
itv_pts_ids = range(n)
arr, rows, cols, gmin, gmax = call_kde(n, x, y, itv_pts_ids, extent, bandwidth,
cellsize, kernel, gradient, opaque)
fromarray(arr).save("test1.png")
def RingTracking2D(data, n_centers, threshold = 0.5, draw_dots = False, draw_mean = False, return_dots = False, outfolder = None, static = True):
# tracks a ring pattern in 2D. Locates particles using the Hough transfrom algorithm (center_find)
# data is a stack of images to track particles in, stored as xyt hyperstack
# n_centers is the number of ring patterns to track
# theshold is the treshold used in center_find
# draw_dots = True draws a label and a spot on each image from data where each center was found
# draw_mean = True Uses the mean coordinate from each track as the position of the spot to be drawn
# return_dots = True, retrun the images with the spots drawn on them
# outfolder = location to save images with spots/labels
# static = True uses first location of a particle to link tracks. Use False if the particle is moving
#
#
# returns an array containing the coordinates of the ring centers at each frame
# Also returns the images with a tracks labeled if return_dots = True
#find centers
from holopy.core.process.centerfinder import center_find
coords = []
print('finding particles')
#loop through each frame
for i in np.arange(0,data.shape[-1]):
if i%50 == 0:
print(i)
coord = center_find(data[:,:,i], centers = n_centers, threshold = threshold)
if n_centers ==1:
coord = np.reshape(coord, (1,2))
coords.append(coord)
coords = np.array(coords)
#link up particles between frames
print('linking tracks')
def get_distance(coords1, coords2):
return np.sqrt( (coords1[0]-coords2[0])**2 + (coords1[1]-coords2[1])**2 )
tracks = np.empty(coords.shape)
#loop through each particle
for particle in np.arange(0,coords.shape[1]):
#set initial point
tracks[0,particle,:] = coords[0,particle,:]
known_coord = tracks[0,particle,:] #location of the particle for comparison
#look in subsequent frames to find the closest particle to known_coord
for frame in np.arange(1,coords.shape[0]):
min_distance = 10000
if not static:
known_coord = tracks[frame-1,particle,:]
#compare to all center locations
for i in np.arange(0,coords.shape[1]):
distance = get_distance(known_coord, coords[frame,i,:])
if distance < min_distance:
min_distance = distance
closest_particle_index = i
#set track coordinate
tracks[frame,particle,:] = coords[frame, closest_particle_index, :]
if not draw_dots:
return tracks
#Draw a spot at each point that was found
else:
print('drawing tracks')
from Image import fromarray
from ImageDraw import Draw
#used for scaling images
im_max = data.max()
im_min = data.min()
if return_dots:
spot_images = []
if draw_mean:
tracksmean = np.mean(tracks,0)
#loop through each frame
for i in np.arange(0,data.shape[-1]):
#convert to an RGB image
spot_image = fromarray( 255*(data[:,:,i]-im_min)/(im_max-im_min) )
spot_image = spot_image.convert('RGB')
#draw spots and labels
draw = Draw(spot_image)
for j in np.arange(0,tracks.shape[1]):
if draw_mean:
spot_coord = [np.round(tracksmean[j,1]), np.round(tracksmean[j,0])]
else:
spot_coord = [np.round(tracks[i,j,1]), np.round(tracks[i,j,0])]
draw.point( (spot_coord[0],spot_coord[1]), (255,0,0))
draw.text( (spot_coord[0]-10,spot_coord[1]-10), str(j),(255,0,0))
if return_dots:
spot_images.append(spot_image)
if outfolder != None:
spot_image.save(outfolder + 'spot_image' + str(i).zfill(5) +'.png')
if return_dots:
return [tracks, spot_images]
else:
return tracks
def frompil(src):
return pil_to_ipl(fromarray(src))
def shaded_relief(in_file,
raster_band,
color_file,
out_file_name,
azimuth=315,
angle_altitude=45):
'''
The main function. Reads the input image block by block to improve the performance, and calculates the shaded relief image
'''
if exists(in_file) is False:
raise Exception('[Errno 2] No such file or directory: \'' + in_file +
'\'')
dataset = gdal.Open(in_file, GA_ReadOnly)
if dataset == None:
raise Exception("Unable to read the data file")
band = dataset.GetRasterBand(raster_band)
block_sizes = band.GetBlockSize()
x_block_size = block_sizes[0]
y_block_size = block_sizes[1]
#If the block y size is 1, as in a GeoTIFF image, the gradient can't be calculated,
#so more than one block is used. In this case, using8 lines gives a similar
#result as taking the whole array.
if y_block_size < 8:
y_block_size = 8
xsize = band.XSize
ysize = band.YSize
max_value = band.GetMaximum()
min_value = band.GetMinimum()
#Reading the color table
color_table = readColorTable(color_file)
#Adding an extra value to avoid problems with the last & first entry
if sorted(color_table.keys())[0] > min_value:
color_table[min_value - 1] = color_table[sorted(color_table.keys())[0]]
if sorted(color_table.keys())[-1] < max_value:
color_table[max_value + 1] = color_table[sorted(
color_table.keys())[-1]]
#Preparing the color table
classification_values = color_table.keys()
classification_values.sort()
max_value = band.GetMaximum()
min_value = band.GetMinimum()
if max_value == None or min_value == None:
stats = band.GetStatistics(0, 1)
max_value = stats[1]
min_value = stats[0]
out_array = zeros((3, ysize, xsize), 'uint8')
#The iteration over the blocks starts here
for i in range(0, ysize, y_block_size):
if i + y_block_size < ysize:
rows = y_block_size
else:
rows = ysize - i
for j in range(0, xsize, x_block_size):
if j + x_block_size < xsize:
cols = x_block_size
else:
cols = xsize - j
dem_array = band.ReadAsArray(j, i, cols, rows)
hs_array = hillshade(dem_array, azimuth, angle_altitude)
rgb_array = values2rgba(dem_array, color_table,
classification_values, max_value,
min_value)
hsv_array = rgb_to_hsv(rgb_array[:, :, 0], rgb_array[:, :, 1],
rgb_array[:, :, 2])
hsv_adjusted = asarray([hsv_array[0], hsv_array[1], hs_array])
shaded_array = hsv_to_rgb(hsv_adjusted)
out_array[:, i:i + rows, j:j + cols] = shaded_array
#Saving the image using the PIL library
im = fromarray(transpose(out_array, (1, 2, 0)), mode='RGB')
im.save(out_file_name)
