def cutouts_plot(outpath, base_name, order_num, obj, flat, top_trace, bot_trace, trace):
pl.figure('traces', facecolor='white', figsize=(8, 5))
pl.cla()
pl.suptitle('order cutouts, {}, order {}'.format(base_name, order_num), fontsize=14)
pl.set_cmap('Blues_r')
obj_plot = pl.subplot(2, 1, 1)
try:
obj_plot.imshow(exposure.equalize_hist(obj))
except:
obj_plot.imshow(obj)
obj_plot.plot(np.arange(1024), top_trace, 'y-', linewidth=1.5)
obj_plot.plot(np.arange(1024), bot_trace, 'y-', linewidth=1.5)
obj_plot.plot(np.arange(1024), trace, 'y-', linewidth=1.5)
obj_plot.set_title('object')
# obj_plot.set_ylim([1023, 0])
obj_plot.set_xlim([0, 1023])
flat_plot = pl.subplot(2, 1, 2)
try:
flat_plot.imshow(exposure.equalize_hist(flat))
except:
flat_plot.imshow(flat)
flat_plot.plot(np.arange(1024), top_trace, 'y-', linewidth=1.5)
flat_plot.plot(np.arange(1024), bot_trace, 'y-', linewidth=1.5)
flat_plot.plot(np.arange(1024), trace, 'y-', linewidth=1.5)
flat_plot.set_title('flat')
# flat_plot.set_ylim([1023, 0])
flat_plot.set_xlim([0, 1023])
pl.tight_layout()
pl.savefig(constructFileName(outpath, base_name, order_num, 'cutouts.png'))
pl.close()
def sparect_plot(outpath, base_name, order_num, obj, flat):
pl.figure('spatially rectified', facecolor='white', figsize=(8, 5))
pl.cla()
pl.suptitle('spatially rectified, {}, order {}'.format(base_name, order_num), fontsize=14)
pl.set_cmap('Blues_r')
obj_plot = pl.subplot(2, 1, 1)
try:
obj_plot.imshow(exposure.equalize_hist(obj))
except:
obj_plot.imshow(obj)
obj_plot.set_title('object')
# obj_plot.set_ylim([1023, 0])
obj_plot.set_xlim([0, 1023])
flat_plot = pl.subplot(2, 1, 2)
try:
flat_plot.imshow(exposure.equalize_hist(flat))
except:
flat_plot.imshow(flat)
flat_plot.set_title('flat')
# flat_plot.set_ylim([1023, 0])
flat_plot.set_xlim([0, 1023])
pl.tight_layout()
pl.savefig(constructFileName(outpath, base_name, order_num, 'sparect.png'))
pl.close()
def traces_plot(outpath, base_name, order_num, obj, flat, top_trace, bot_trace):
pl.figure('traces', facecolor='white', figsize=(8, 5))
pl.cla()
pl.suptitle('order edge traces, {}, order {}'.format(base_name, order_num), fontsize=14)
pl.set_cmap('Blues_r')
pl.rcParams['ytick.labelsize'] = 8
obj_plot = pl.subplot(1, 2, 1)
obj_plot.imshow(exposure.equalize_hist(obj))
obj_plot.plot(np.arange(1024), top_trace, 'y-', linewidth=1.5)
obj_plot.plot(np.arange(1024), bot_trace, 'y-', linewidth=1.5)
obj_plot.set_title('object')
obj_plot.set_ylim([1023, 0])
obj_plot.set_xlim([0, 1023])
flat_plot = pl.subplot(1, 2, 2)
flat_plot.imshow(exposure.equalize_hist(flat))
flat_plot.plot(np.arange(1024), top_trace, 'y-', linewidth=1.5)
flat_plot.plot(np.arange(1024), bot_trace, 'y-', linewidth=1.5)
flat_plot.set_title('flat')
flat_plot.set_ylim([1023, 0])
flat_plot.set_xlim([0, 1023])
pl.tight_layout()
pl.savefig(constructFileName(outpath, base_name, order_num, 'traces.png'))
pl.close()
def tops_bots_plot(outpath, base_name, tops, bots):
pl.figure('edges', facecolor='white', figsize=(8, 5))
pl.cla()
pl.suptitle('top and bottom order edges, {}'.format(base_name), fontsize=14)
# pl.set_cmap('Blues_r')
pl.rcParams['ytick.labelsize'] = 8
obj_plot = pl.subplot(1, 2, 1)
try:
obj_plot.imshow(exposure.equalize_hist(tops))
except:
obj_plot.imshow(tops)
obj_plot.set_title('top edges')
obj_plot.set_ylim([1023, 0])
obj_plot.set_xlim([0, 1023])
flat_plot = pl.subplot(1, 2, 2)
try:
flat_plot.imshow(exposure.equalize_hist(bots))
except:
flat_plot.imshow(bots)
flat_plot.set_title('bottom edges')
flat_plot.set_ylim([1023, 0])
flat_plot.set_xlim([0, 1023])
pl.tight_layout()
pl.savefig(constructFileName(outpath, base_name, None, 'top_bot_edges.png'))
pl.close()
def align_img(image_name, loss_fn=sum_of_squared_diff, max_disp = 15, big=False, name=None, hist_eq = False):
b,g,r = get_bgr(image_name)
if hist_eq == True:
b, g, r = exposure.equalize_hist(b), exposure.equalize_hist(g), exposure.equalize_hist(r)
print("Aligning green and blue: ")
ag = align(g, b, loss_fn=loss_fn, max_disp = max_disp, big=big, name=name)
print("Aligning blue and red: ")
ar = align(r, b, loss_fn=loss_fn, max_disp = max_disp, big=big, name=name)
# create a color image
im_out = np.dstack([ar, ag, b])
plt.show()
# save the image
iname = image_name.split('.')
iname[-1] = 'jpg'
image_name = '.'.join(iname)
fname = 'out_' + image_name
skio.imsave(fname, im_out)
# display the image
skio.imshow(im_out)
plt.show()
skio.show()
def test_equalize_masked():
img = skimage.img_as_float(test_img)
mask = np.zeros(test_img.shape)
mask[50:150, 50:250] = 1
img_mask_eq = exposure.equalize_hist(img, mask=mask)
img_eq = exposure.equalize_hist(img)
cdf, bin_edges = exposure.cumulative_distribution(img_mask_eq)
check_cdf_slope(cdf)
assert not (img_eq == img_mask_eq).all()
def slidingWindow(P,inX=3,outX=32,inY=3,outY=64,maxM=50,norm=True):
""" Enhance the constrast
Cut off extreme values and demean the image
Utilize scipy convolve2d to get the mean at a given pixel
Remove local mean with inner exclusion region
Args:
P: 2-d numpy array image
inX: inner exclusion region in the x-dimension
outX: length of the window in the x-dimension
inY: inner exclusion region in the y-dimension
outY: length of the window in the y-dimension
maxM: size of the output image in the y-dimension
norm: boolean to cut off extreme values
Returns:
Q: 2-d numpy contrast enhanced
"""
Q = P.copy()
m, n = Q.shape
Q = exposure.equalize_hist(Q.astype('Float32'), nbins = 65)
Q = detrend(Q.astype('Float32'), axis = 1)
Q = detrend(Q.astype('Float32'), axis = 0)
Q = wiener(Q.astype('Float32'), 4)
return Q[:maxM,:]
def twoDimOrderPlot(outpath, base_name, title, obj_name, base_filename, order_num, data, x):
pl.figure('2d order image', facecolor='white', figsize=(8, 5))
pl.cla()
pl.title(title + ', ' + base_name + ", order " + str(order_num), fontsize=14)
pl.xlabel('wavelength($\AA$)', fontsize=12)
pl.ylabel('row (pixel)', fontsize=12)
#pl.imshow(img, aspect='auto')
#pl.imshow(data, vmin=0, vmax=1024, aspect='auto')
pl.imshow(exposure.equalize_hist(data), origin='lower',
extent=[x[0], x[-1], 0, data.shape[0]], aspect='auto')
# from matplotlib import colors
# norm = colors.LogNorm(data.mean() + 0.5 * data.std(), data.max(), clip='True')
# pl.imshow(data, norm=norm, origin='lower',
# extent=[x[0], x[-1], 0, data.shape[0]], aspect='auto')
pl.colorbar()
pl.set_cmap('jet')
# pl.set_cmap('Blues_r')
fn = constructFileName(outpath, base_name, order_num, base_filename)
pl.savefig(fn)
log_fn(fn)
pl.close()
# np.save(fn[:fn.rfind('.')], data)
return
def mirror_mirror():
pca, clf = gen_classifier()
cam = cv2.VideoCapture(0)
last_emo = []
while True:
ret, img = cam.read()
if not ret:
continue
gray = cv2.cvtColor(img, cv.CV_BGR2GRAY)
faces = detect_and_scale_face(gray)
if not faces:
continue
for scaled in faces:
test_x = exposure.equalize_hist(scaled.reshape((1, -1)))
face_pca = pca.transform(test_x)
pred = int(clf.predict(face_pca)[0])
last_emo.append(pred)
if len(last_emo) == 4:
last_emo.pop(0)
if last_emo[0] != OTHER_LABEL and \
np.all(np.array(last_emo) == last_emo[0]):
sb.call(['say', TARGET_NAMES[pred]])
def preprocessing(image):
# Contrast stretching
#TODO TEST
#p2, p98 = np.percentile(image, (2, 98))
#image_rescale = exposure.rescale_intensity(image, in_range=(p2, p98))
gray = rgb2gray(image)
return exposure.equalize_hist(gray)
def preproc(self, img, size, pixel_spacing, equalize=True, crop=True):
"""crop center and resize"""
# TODO: this is stupid, you could crop out the heart
# But should test this
if img.shape[0] < img.shape[1]:
img = img.T
# Standardize based on pixel spacing
img = transform.resize(img, (int(img.shape[0]*(1.0/np.float32(pixel_spacing[0]))), int(img.shape[1]*(1.0/np.float32(pixel_spacing[1])))))
# we crop image from center
short_egde = min(img.shape[:2])
yy = int((img.shape[0] - short_egde) / 2)
xx = int((img.shape[1] - short_egde) / 2)
if crop:
crop_img = img[yy : yy + short_egde, xx : xx + short_egde]
# resize to 64, 64
resized_img = transform.resize(crop_img, (size, size))
else:
resized_img = img
#resized_img = gaussian_filter(resized_img, sigma=1)
#resized_img = median_filter(resized_img, size=(3,3))
if equalize:
resized_img = equalize_hist(resized_img)
resized_img = adjust_sigmoid(resized_img)
resized_img *= 255.
return resized_img.astype("float32")
def alineacion(image,rgbCopia,escala):
centerPoint=(140,165)
extents=15
#if escala>1:
# extents=extents*escala
cropImg=np.empty((extents*2,extents*2,3),dtype=float)
cropSizes=[]
cropSizes.append([0,0,0,0])
cropImg[:,:,0]=cropImage(image[:,:,0],centerPoint,extents)
for i in range(image.shape[2]-1):
cropImg[:,:,i+1]=cropImage(image[:,:,i+1],centerPoint,extents*escala)[::escala,::escala]
cropImg[:,:,i+1]=cropImg[:,:,i+1]-np.mean(cropImg[:,:,i+1])
channelR=cropImg[:,:,0]
for i in range(2):
channelRGradient=np.gradient(channelR)
cropGradient=np.gradient(cropImg[:,:,i+1])
#correlation=correlationMatrix(channelR,cropImg[:,:,i+1])
correlation=correlationMatrix(channelR,cropImg[:,:,i+1])
correlation=correlation-np.mean(correlation)
print "Filtro size ",correlation.shape
position=np.where(correlation==np.amax(correlation))
despVector=[position[0][0]-correlation.shape[0]/2,position[1][0]-correlation.shape[1]/2]
fila=despVector[0]
columna=despVector[1]
print "fila",fila
print "columna", columna
generateNewRGB(i,rgbCopia,fila,columna)
rgbCopia=equalize_hist(rgbCopia)
rgbCopia=rgbCopia*255/np.max(rgbCopia)
return rgbCopia
def compute(self, src):
image = img_as_ubyte(src)
# denoise image
denoised = denoise_tv_chambolle(image, weight=0.05)
denoised_equalize= exposure.equalize_hist(denoised)
# find continuous region (low gradient) --> markers
markers = rank.gradient(denoised_equalize, disk(5)) < 10
markers = ndi.label(markers)[0]
# local gradient
gradient = rank.gradient(denoised, disk(2))
# labels
labels = watershed(gradient, markers)
# display results
fig, axes = plt.subplots(2,3)
axes[0, 0].imshow(image)#, cmap=plt.cm.spectral, interpolation='nearest')
axes[0, 1].imshow(denoised, cmap=plt.cm.spectral, interpolation='nearest')
axes[0, 2].imshow(markers, cmap=plt.cm.spectral, interpolation='nearest')
axes[1, 0].imshow(gradient, cmap=plt.cm.spectral, interpolation='nearest')
axes[1, 1].imshow(labels, cmap=plt.cm.spectral, interpolation='nearest', alpha=.7)
plt.show()
def loadDataMontgomery(df, path, im_shape):
"""Function for loading Montgomery dataset"""
X, y = [], []
for i, item in df.iterrows():
img = img_as_float(io.imread(path + item[0]))
gt = io.imread(path + item[1])
l, r = np.where(img.sum(0) > 1)[0][[0, -1]]
t, b = np.where(img.sum(1) > 1)[0][[0, -1]]
img = img[t:b, l:r]
mask = gt[t:b, l:r]
img = transform.resize(img, im_shape)
img = exposure.equalize_hist(img)
img = np.expand_dims(img, -1)
mask = transform.resize(mask, im_shape)
mask = np.expand_dims(mask, -1)
X.append(img)
y.append(mask)
X = np.array(X)
y = np.array(y)
X -= X.mean()
X /= X.std()
print '### Data loaded'
print '\t{}'.format(path)
print '\t{}\t{}'.format(X.shape, y.shape)
print '\tX:{:.1f}-{:.1f}\ty:{:.1f}-{:.1f}\n'.format(X.min(), X.max(), y.min(), y.max())
print '\tX.mean = {}, X.std = {}'.format(X.mean(), X.std())
return X, y
def visualize_depth_image(data):
data[data == 0.0] = np.nan
maxdepth = np.nanmax(data)
mindepth = np.nanmin(data)
data = data.copy()
data -= mindepth
data /= (maxdepth - mindepth)
gray = np.zeros(list(data.shape) + [3], dtype=data.dtype)
data = (1.0 - data)
gray[..., :3] = np.dstack((data, data, data))
# use a greenish color to visualize missing depth
gray[np.isnan(data), :] = (97, 160, 123)
gray[np.isnan(data), :] /= 255
gray = exposure.equalize_hist(gray)
# set alpha channel
gray = np.dstack((gray, np.ones(data.shape[:2])))
gray[np.isnan(data), -1] = 0.5
return gray * 255
def hsi_equalize_hist():
image=data.astronaut()
h=color.rgb2hsv(image)
h[:,:,2]=exposure.equalize_hist(h[:,:,2])
image_equal=color.hsv2rgb(h)
io.imshow(image_equal)
io.imsave('astronautequal.png',image_equal)
def test_equalize_ubyte():
with expected_warnings(['precision loss']):
img = skimage.img_as_ubyte(test_img)
img_eq = exposure.equalize_hist(img)
cdf, bin_edges = exposure.cumulative_distribution(img_eq)
check_cdf_slope(cdf)
def histograms_of_stuff(cls, ref):
# First we show the image, and its histogram
neg = 1. - ref
figs = []
f1, axs1 = plt.subplots(2, 2, figsize=(8, 8))
for i, im in enumerate(((ref, 'tire'), (neg, 'negative'))):
axs1[i,0].imshow(im[0], cmap='gray')
axs1[i,1].hist(im[0].ravel(), 256, range=(0., 1.), fc='k', ec='k')
axs1[i,0].set_title(im[1])
axs1[i,1].set_title(im[1] + ': histogram')
figs.append(f1)
# Some gamma correction ...
gammas = (0.5, 1.3)
f2, axs2 = plt.subplots(2, 2, figsize=(8,8))
for i, gamma in enumerate(gammas):
x = ref ** gamma
axs2[i,0].imshow(x, cmap='gray')
axs2[i,1].hist(x.ravel(), 256, range=(0.,1.), fc='k', ec='k')
for z in axs2[i]:
z.set_title('gamma = ' + str(gamma))
figs.append(f2)
# Histogram Equalization
f3, axs3 = plt.subplots(1, 2, figsize=(8, 4))
eq_ref = exposure.equalize_hist(ref)
axs3[0].imshow(eq_ref, cmap='gray')
axs3[1].hist(eq_ref.ravel(), 256, range=(0.,1.), fc='k', ec='k')
for x in axs3:
x.set_title('Histogram Equalization')
figs.append(f3)
return figs
def _equalizeHistogram(img):
'''
histogram equalisation not bounded to int() or an image depth of 8 bit
works also with negative numbers
'''
#to float if int:
intType = None
if 'f' not in img.dtype.str:
TO_FLOAT_TYPES = { np.dtype('uint8'):np.float16,
np.dtype('uint16'):np.float32,
np.dtype('uint32'):np.float64,
np.dtype('uint64'):np.float64}
intType = img.dtype
img = img.astype(TO_FLOAT_TYPES[intType], copy=False)
#get image deph
DEPTH_TO_NBINS = {np.dtype('float16'):256, #uint8
np.dtype('float32'):32768, #uint16
np.dtype('float64'):2147483648} #uint32
nBins = DEPTH_TO_NBINS[img.dtype]
#scale to -1 to 1 due to skikit-image restrictions
mn, mx = np.amin(img), np.amax(img)
if abs(mn) > abs(mx):
mx = mn
img /= mx
img = exposure.equalize_hist(img, nbins=nBins)
img *= mx
if intType:
img = img.astype(intType)
return img
def extract_sift(image,lm=None, shape=[200,300], fix_points='outer',ttype='affine'):
if lm==None:lm = landmarks(image)
if np.any(np.isnan(lm)):
return np.nan*np.ones([out_shape,out_shape,image.shape[2]]).astype(np.float16), np.nan*np.zeros_like(lm)
dst = mean_face[:,p[fix_points]]
dst = dst-dst.mean(1)[:,None]
dst = dst/np.abs(dst).max()
dst *=shape[0]/2
dst +=shape[1]/2
print(dst.min())
print(dst.max())
src = lm[:,p[fix_points]]
tform = transform.estimate_transform(ttype, src.T,dst.T)
lm_reg = tform(lm.T).T
image = transform.warp(image,inverse_map=tform.inverse,output_shape=[shape[1],shape[1]])
image = exposure.equalize_hist(image,mask=image!=0)
S = 12
for l1,l2 in lm_reg.T:
x = np.arange(l2-S,l2+S)
y = np.arange(l1-S,l1+S)
for x_ in x:
for y_ in y:
image[x_,y_,0]=255
return image, lm_reg
def equalize(inputs):
"""equalize the data"""
new_data = []
for i in inputs:
new_i = exposure.equalize_hist(i)
new_data.append(new_i)
return np.array(new_data)
def make_lungs():
path = '/path/to/JSRT/All247images/'
for i, filename in enumerate(os.listdir(path)):
img = 1.0 - np.fromfile(path + filename, dtype='>u2').reshape((2048, 2048)) * 1. / 4096
img = exposure.equalize_hist(img)
io.imsave('/path/to/JSRT/new/' + filename[:-4] + '.png', img)
print 'Lung', i, filename
def transform(self, Xb, yb):
Xb, yb = super(EqualizeHistBatchIteratorMixin, self).transform(Xb, yb)
Xb_transformed = np.asarray([
[equalize_hist(img_ch) for img_ch in img] for img in Xb
])
Xb_transformed = Xb_transformed.astype(Xb.dtype)
return Xb_transformed, yb
def classify_emotions(pca, clf, faces):
emotions = []
for scaled in faces:
test_x = exposure.equalize_hist(scaled.reshape((1, -1)))
face_pca = pca.transform(test_x)
emotions.append(int(clf.predict(face_pca)[0]))
return emotions
def add_auto_masks_area(areaFile,addMasks=False):
from skimage.morphology import binary_dilation, binary_erosion, disk
from skimage import exposure
from skimage.transform import hough_circle
from skimage.morphology import convex_hull_image
from skimage.feature import canny
from skimage.draw import circle_perimeter
import h5py
MASKs = np.zeros(areaFile.attrs['ROI_patches'].shape)
if 'ROI_masks' in (areaFile.attrs.iterkeys()):
print 'Masks have already been created'
awns = raw_input('Would you like to redo them from scratch: (answer y/n): ')
else:
awns = 'y'
if awns=='y':
MASKs = np.zeros(areaFile.attrs['ROI_patches'].shape)
for i in range(areaFile.attrs['ROI_patches'].shape[2]):
patch = areaFile.attrs['ROI_patches'][:,:,i]
tt0 = exposure.equalize_hist(patch)
tt = 255*tt0/np.max(tt0)
thresh = 1*tt>0.3*255
thresh2 = 1*tt<0.1*255
tt[thresh] = 255
tt[thresh2] = 0
edges = canny(tt, sigma=2, low_threshold=20, high_threshold=30)
try_radii = np.arange(3,5)
res = hough_circle(edges, try_radii)
ridx, r, c = np.unravel_index(np.argmax(res), res.shape)
r, c, try_radii[ridx]
image = np.zeros([20,20,4])
cx, cy = circle_perimeter(c, r, try_radii[ridx]+2)
try:
image[cy, cx] = (220, 80, 20, 1)
original_image = np.copy(image[:,:,3])
chull = convex_hull_image(original_image)
image[chull,:] = (255, 0, 0, .4)
except:
pass
MASKs[:,:,i] = (1*image[:,:,-1]>0)
if addMasks:
areaFile.attrs['ROI_masks'] = MASKs
else:
pass
return np.array(MASKs)
def getImage():
fn = r'/home/pi/tmp.jpg'
proc = subprocess.Popen('raspistill -o %s -w 640 -h 480 -n -t 3' % (fn),
shell=True, stderr=subprocess.STDOUT)
proc.wait()
im = io.imread(fn, as_grey=True)
im = exposure.equalize_hist(im)
return skimage.img_as_ubyte(im)
def post_process(img):
"""
Post-processes the results using skimage filters
"""
filt_1 = ski_filter.hprewitt(img)
filt_2 = exposure.equalize_hist(filt_1)
return filt_2
def boost_contrast_global(image):
"""Returns a new image with contrast boosted globally.
Args:
image: assumed to be grayscale.
"""
glob = exposure.equalize_hist(image) * 255
return exposure.rescale_intensity(image)
def visualize_derivatives(image):
laplacian = scipy.ndimage.filters.laplace(image)
lhist = mean_center(
blur(exposure.equalize_hist(unitize(laplacian)),1))
plt.imshow(lhist,
origin='lower',interpolation='nearest',cmap='gray',extent=(0,64,)*2)
plt.title('Laplacian')
return gradient, laplacian
def preprocess(self, regions):
# decoder expects input shape [samples, 1, 64, 64]
crop = CropTransformation(translation=0, crop_shape=(64, 64))
cropped_rois = crop(regions)
if self.uses_hist_equalization:
cropped_rois = np.stack([equalize_hist(roi) for roi in cropped_rois])
cropped_rois = 2 * cropped_rois - 1
return cropped_rois
def histeq(I):
return equalize_hist(I)
def order_tracer(dir_name, file_name, X_half_width, step, min_height, aperture,
adaptive, view):
poly_order = 2 # order of polynomial fit
#read image
hdulist = pyfits.open(dir_name.joinpath(file_name))
ordim = hdulist[0].data.copy()
hdulist.close()
trace_im = np.zeros((ordim.shape[0], ordim.shape[1]), dtype=np.float)
# Search for local maxima in the cross sections and construct the mask of orders. 1 - max, 0 - rest of pixels
for x_t in range(X_half_width, ordim.shape[1] - X_half_width,
X_half_width):
slic = np.median(ordim[:, x_t - X_half_width:x_t + X_half_width - 1],
1)
peaks_coord, _ = find_peaks(slic, height=min_height, width=2)
trace_im[peaks_coord, x_t] = 1
# Now rearrange the data for the futher clustering
ones = np.array(np.where(trace_im == 1))
ord_mask = ones.T
model = AgglomerativeClustering(n_clusters=None,
affinity='euclidean',
linkage='single',
compute_full_tree=True,
distance_threshold=5)
model.fit(ord_mask)
labels = model.labels_
n_clusters = len(list(set(labels)))
order_tab = [] # output table of traces
width_tab = [] # output table of sizes
center_x = ordim.shape[1] / 2 - 1
for i in range(n_clusters):
if len(ord_mask[labels == i,
1]) >= (ordim.shape[1] / X_half_width - 2) * 0.5:
cheb_coef = chebfit(ord_mask[labels == i, 1],
ord_mask[labels == i, 0], poly_order)
cheb_coef = np.insert(cheb_coef, 0, chebval(center_x, cheb_coef))
order_tab.append(cheb_coef)
order_tab = np.asarray(order_tab) # convert list into array
order_tab = order_tab[order_tab[:, 0].argsort()] # Sort array by ascending
order_tab[:, 0] = np.arange(
len(order_tab[:, 0]),
dtype=int) # insert the number of order in the 0th column
n_orders = int(order_tab[-1, 0]) + 1
print(f"Found {n_orders} orders")
logging.info(f"Found {n_orders} orders")
# Recentering orders and the final tracing
#get points for order tracing, start from center column of image
n_points = np.floor((ordim.shape[1] / 2 - X_half_width) / step)
print(f"{1+2*n_points} points in each order for fitting")
logging.info(f"{1+2*n_points} points in each order for fitting")
trace_x = np.arange(ordim.shape[1] // 2 - X_half_width - n_points * step,
ordim.shape[1] - step,
step,
dtype=int)
x_coord = np.arange(ordim.shape[1])
orders = []
for i in range(n_orders):
print(f"Re-trace order {i}")
logging.info(f"Re-trace order {i}")
xfit = []
centr = []
width = []
for x in trace_x:
xt = np.arange(x - step, x + step, 1, dtype=int)
yc = chebval(x, order_tab[i, 1:])
if yc > 0 and yc + X_half_width + 2 < ordim.shape[0]:
yy = np.arange(yc - X_half_width,
yc + X_half_width + 2,
1,
dtype=int)
prof = np.median(ordim[yy[0]:yy[-1] + 1, xt], axis=1)
if prof.shape[0] != 0 and max(
prof
) > 20.: # Fit only well-exposured part of the order
# Re-fit the cross-sections
moffat = lambda x, A, B, C, D, x0: A * (1 + (
(x - x0) / B)**2)**(-C) + D
Y = yy - yc
p0 = np.array([max(prof), 3.0, 3.0, 0.0, 3.0])
try:
popt, pcov = curve_fit(moffat,
Y,
prof,
p0,
maxfev=10000)
except RuntimeError:
pass
else:
fwhm = 2 * popt[1] * np.sqrt(2**(1 / (popt[2])) - 1)
if np.isfinite(fwhm) and fwhm > 1 and fwhm < 10:
xfit.append(x)
centr.append(popt[4] + yc)
width.append(fwhm)
med_fwhm = np.median(width)
# print(f"Order {i}, median FWHM: {med_fwhm:.2f}, mean FWHM: {np.mean(width):.2f}")
print(f"Fit {len(xfit)} points, median FWHM {med_fwhm:.3f}")
logging.info(f"Fit {len(xfit)} points, median FWHM {med_fwhm:.3f}")
coef_center = chebfit(xfit, centr, poly_order)
coef_width = chebfit(xfit, width, poly_order)
## Check the limits of the orders
if adaptive:
width = chebval(x_coord, coef_width)
else:
width = np.repeat(med_fwhm, ordim.shape[1])
if np.min(chebval(x_coord, coef_center) -
width) < 1. or np.max(chebval(x_coord, coef_center) +
width) >= ordim.shape[0] - 1:
print(f"Skip incomplete order #{i}")
logging.info(f"Skip incomplete order #{i}")
else:
width_tab.append(width)
orders.append(coef_center)
# Output data
width_tab = np.asarray(width_tab)
orders = np.asarray(orders)
text_file = open(dir_name.joinpath('temp', 'traces.txt'),
"w") # Save results
for i in range(orders.shape[0]):
text_file.write("Order" + '\t' + str(i) + '\n')
for j in x_coord:
text_file.write(
format('%.2f' % chebval(j, orders[i, :])) + '\t' +
format('%.2f' % width[j]) + '\n')
text_file.close()
print(f"Data saved to {dir_name.joinpath('temp/traces.txt')}")
logging.info(f"Data saved to {dir_name.joinpath('temp/traces.txt')}")
# Display the results
fig = plt.figure(figsize=(15, 15 / (ordim.shape[1] / ordim.shape[0])),
tight_layout=True)
ax0 = fig.add_subplot(1, 1, 1)
ax0.imshow(equalize_hist(ordim), cmap='gist_gray')
ax0.set_xlabel("CCD X")
ax0.set_ylabel("CCD Y")
for i in range(orders.shape[0]):
ax0.plot(x_coord,
chebval(x_coord, orders[i, :]) + aperture * width_tab[i, :],
'b-',
lw=0.4)
ax0.plot(x_coord,
chebval(x_coord, orders[i, :]) - aperture * width_tab[i, :],
'r-',
lw=0.4)
ax0.text(x_coord[15],
chebval(x_coord[15], orders[i, :]),
i + 1,
color='k',
backgroundcolor='yellow',
fontsize=8)
plt.gca().invert_yaxis()
fig.savefig(dir_name.joinpath('orders_map.pdf'), dpi=350)
if view:
plt.show()
return None
"""
Equalizing the histogram of an image
=====================================
Histogram equalizing makes images have a uniform histogram.
"""
from skimage import data, exposure
import matplotlib.pyplot as plt
camera = data.camera()
camera_equalized = exposure.equalize_hist(camera)
plt.figure(figsize=(7, 3))
plt.subplot(121)
plt.imshow(camera, cmap='gray', interpolation='nearest')
plt.axis('off')
plt.subplot(122)
plt.imshow(camera_equalized, cmap='gray', interpolation='nearest')
plt.axis('off')
plt.tight_layout()
plt.show()
def plot_visualization(vid,
vid_orig,
vis_dir,
plot_img=False,
write_video=True):
def crop_center(img, cropx, cropy):
y, x, _ = img.shape
startx = x // 2 - (cropx // 2)
starty = y // 2 - (cropy // 2)
return img[starty:starty + cropy, startx:startx + cropx, :]
def plot_img_and_hist(image, axes, bins=256):
#Plot an image along with its histogram and cumulative histogram.
image = img_as_float(image)
ax_img, ax_hist = axes
ax_cdf = ax_hist.twinx()
# Display image
ax_img.imshow(image, cmap=plt.cm.gray)
ax_img.set_axis_off()
ax_img.set_adjustable('box-forced')
# Display histogram
ax_hist.hist(image.ravel(), bins=bins, histtype='step', color='black')
ax_hist.ticklabel_format(axis='y',
style='scientific',
scilimits=(0, 0))
ax_hist.set_xlabel('Pixel intensity')
ax_hist.set_xlim(0, 1)
ax_hist.set_yticks([])
# Display cumulative distribution
img_cdf, bins = exposure.cumulative_distribution(image, bins)
ax_cdf.plot(bins, img_cdf, 'r')
ax_cdf.set_yticks([])
return ax_img, ax_hist, ax_cdf
def PCA(data):
m, n = data.shape[0], data.shape[1]
#print(m, n)
mean = np.mean(data, axis=0)
data -= np.tile(mean, (m, 1))
# calculate the covariance matrix
cov = np.matmul(np.transpose(data), data)
evals, evecs = np.linalg.eigh(cov)
# sort eigenvalue in decreasing order
idx = np.argsort(evals)[::-1]
evecs = evecs[:, idx]
evals = evals[idx]
#print(evals)
evecs = evecs[:, 0]
return np.matmul(data, evecs), evals[0] / sum(evals)
width, height = 112, 112
video_histeq = []
for i in range(vid.shape[0]):
frame = crop_center(vid[i], 112, 112)
frame = np.reshape(frame, (112 * 112, 3))
frame, K = PCA(frame)
frame = np.reshape(frame, (112, 112))
max, min = np.max(frame), np.min(frame)
frame = ((frame - min) / (max - min) * 255).astype('uint8')
# Contrast stretching
p2, p98 = np.percentile(frame, (2, 98))
img_rescale = exposure.rescale_intensity(frame, in_range=(p2, p98))
# Equalization
img_eq = exposure.equalize_hist(frame)
video_histeq.append(img_eq)
# # Adaptive Equalization
# img_adapteq = exposure.equalize_adapthist(frame, clip_limit=0.03)
if plot_img:
# Display results
fig = plt.figure(figsize=(12, 16))
axes = np.zeros((4, 3), dtype=np.object)
axes[0, 0] = fig.add_subplot(4, 3, 1)
for j in range(1, 3):
axes[0, j] = fig.add_subplot(4,
3,
1 + j,
sharex=axes[0, 0],
sharey=axes[0, 0])
for j in range(3, 12):
axes[j // 3, j % 3] = fig.add_subplot(4, 3, 1 + j)
ax_img, ax_hist, ax_cdf = plot_img_and_hist(frame, axes[0:2, 0])
ax_img.set_title('PCA on 3 channels ({:.4f})'.format(K))
y_min, y_max = ax_hist.get_ylim()
ax_hist.set_ylabel('Number of pixels')
ax_hist.set_yticks(np.linspace(0, y_max, 5))
ax_img, ax_hist, ax_cdf = plot_img_and_hist(
img_rescale, axes[0:2, 1])
ax_img.set_title('Contrast stretching')
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[0:2, 2])
ax_img.set_title('Histogram equalization')
#ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[0:1, 3])
#ax_img.set_title('Adaptive equalization')
ax_cdf.set_ylabel('Fraction of total intensity')
ax_cdf.set_yticks(np.linspace(0, 1, 5))
print(vid_orig[j].shape)
frame_downsample_crop = crop_center(vid_orig[j], 112, 112)
frame = crop_center(vid[j], 112, 112)
axes[2, 0].imshow(frame_downsample_crop.astype('uint8'))
axes[2, 0].set_title('Dowmsampled')
frame_scaled_joint = linear_scaling(
frame, vid, video_linear_scaling=True,
joint_channels=True).astype('uint8')
axes[2, 1].imshow(frame_scaled_joint.astype('uint8'))
axes[2, 1].set_title('Joint Scaling')
frame_scaled_separate = linear_scaling(
frame, vid, video_linear_scaling=True,
joint_channels=False).astype('uint8')
axes[2, 2].imshow(frame_scaled_separate.astype('uint8'))
axes[2, 2].set_title('Separate Scaling')
for j in range(frame.shape[2]):
axes[3, j].imshow(frame[:, :, j], cmap=plt.get_cmap('jet'))
axes[3, j].set_title('Channel{}'.format(j))
# prevent overlap of y-axis labels
fig.tight_layout()
plt.savefig('{}/vis_{}.png'.format(vis_dir, i))
plt.close()
if write_video:
fourcc = cv2.VideoWriter_fourcc(*'XVID') # Be sure to use lower case
output = "{}/hist_eq.avi".format(vis_dir)
out = cv2.VideoWriter(output, fourcc, 10.0, (width, height), False)
vid = np.multiply(np.asarray(video_histeq), 255).astype('uint8')
print(vid.shape)
print(output)
for i in range(vid.shape[0]):
frame = vid[i]
#frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
#frame = frame.reshape(112, 112, 3)
# print(frame)
out.write(frame)
out.release()
cv2.destroyAllWindows()
def equalize_histogram(img):
return (equalize_hist(img) * 255)
def track_digits(self, old_candidates, new_frame, show_idx):
# Prepare the new frame
frame_o = img_as_float(new_frame)
gray_o = rgb2grey(frame_o)
gray = equalize_hist(gray_o)
new_candidates = []
i = 0
roi_img = self.preprocess_roi(rescale(gray, 0.5))
# Try to locate each candidate
for c in old_candidates:
# Extract the area where the candidate can now be
i += 1
h, w = c.image.shape
if h < 10 or w < 10:
new_candidates.append(c)
c.lost_for += 1
continue
rx, ry, rw, rh = self.get_roi(
c.rect, (new_frame.shape[1], new_frame.shape[0]))
rx = int(rx / 2)
ry = int(ry / 2)
rw = int(rw / 2)
rh = int(rh / 2)
roi = roi_img[ry:ry + rh, rx:rx + rw]
if min(roi.shape) <= 0:
new_candidates.append(c)
c.lost_for += 1
continue
if self.show_debug_images and i - 1 == show_idx:
cv2.imshow('roi', img_as_ubyte(roi))
# Get digit rects in roi
rects = self.rotrects_from_image(
roi, 2, True, frame_o.shape[0] * frame_o.shape[1])
cands = []
for rect in rects:
transl_rect = ((rect[0][0] + 2 * rx, rect[0][1] + 2 * ry),
rect[1], rect[2])
cands.append(transl_rect)
if len(cands) == 0:
# If there are no candidates we lost the digit
c.lost_for += 1
new_candidates.append(c)
else:
# If there are several candidates we need to choose the best one
# Calculate metrics
filt_cands = []
for r in cands:
# Dimensions difference
dw = abs(max(r[1]) - max(c.dimensions))
dh = abs(min(r[1]) - min(c.dimensions))
ds = math.sqrt(dw * dw + dh * dh)
# Eucledian distance
dx = abs(r[0][0] - c.rect[0][0])
dy = abs(r[0][1] - c.rect[0][1])
dp = math.sqrt(dx * dx + dy * dy)
# Angular difference, unused
# da = abs(r[2] - c.rect[2])
# Filter based on heuristics
if ds < MAX_SIZE_DIFF_FACTOR * np.average(
c.dimensions) and dp < MAX_POS_DIFF:
filt_cands.append((r, dp))
# If there are no candidates now we lost the digit
best_score = -1
if not filt_cands:
new_candidates.append(c)
c.lost_for += 1
continue
# Try to find the best one
max_dp = max([dp for _, dp in filt_cands]) + 1
for r, dp in filt_cands:
cropped = self.crop_rotrect(r, gray_o)
if cropped is None or min(cropped.shape) <= 5:
continue
# Crop, calculate structural similarity
cropped = resize(cropped, (h, w))
ssim = compare_ssim(cropped, c.orig_image)
# Create an aggregated score
score = (1.0 - pow(dp / max_dp, POS_WEIGHT_ALPHA)) * ssim
if score > best_score:
best_score = score
best_rect = r
best_image = cropped
# Choose the candidate with the best score if there is one
if best_score < 0:
new_candidates.append(c)
c.lost_for += 1
else:
cand = DigitCandidate(best_rect, best_image)
cand.dimensions = c.dimensions
cand.conf = c.conf
cand.guess = c.guess
new_candidates.append(cand)
return new_candidates
def equalize(image_array):
image_equalized = ex.equalize_hist(image_array)
return image_equalized
# Fit CP tensor decomposition (two times).
rank = 10
repeats = 10
all_models = []
all_obj = []
for repeat in tqdm(range(repeats)):
U = tt.cp_als(all_data_long, rank=rank, verbose=False)
all_models.append(U)
all_obj.append(U.obj)
U = all_models[np.argmin(all_obj)]
## We should be able to see differences in tastes by using distance matrices on trial factors
trial_factors = U.factors.factors[-1]
trial_distances = dist_mat(trial_factors, trial_factors)
plt.figure()
plt.imshow(exposure.equalize_hist(trial_distances))
# pca on trial factors and plot by taste
trial_labels = np.sort([0, 1, 2, 3, 4, 5] * 32)
reduced_trials_pca = pca(n_components=2).fit_transform(trial_factors)
plt.scatter(reduced_trials_pca[:, 0], reduced_trials_pca[:, 1], c=trial_labels)
# tsne on trial factors and plot by taste
X_embedded = tsne(n_components=2, perplexity=40).fit_transform(trial_factors)
plt.figure()
plt.scatter(X_embedded[:, 0], X_embedded[:, 1], c=trial_labels)
# Compare the low-dimensional factors from the two fits.
fig, _, _ = tt.plot_factors(U.factors)
def cache(self, path):
As = [
os.path.join(path, p) for p in os.listdir(path)
if p.startswith('A') and p.endswith(self.ext)
]
Bs = [
os.path.join(path, p) for p in os.listdir(path)
if p.startswith('B') and p.endswith(self.ext)
]
LRs = [
os.path.join(path, p) for p in os.listdir(path)
if p.startswith('LR') and p.endswith(self.ext)
]
if os.path.exists(os.path.join('mask_A' + self.ext)):
m = np.array(Image.open(os.path.join('mask_A' + self.ext)))
m = np.expand_dims(m, axis=2) if m.ndim == 2 else m
maskA = m < m.max() / 2
else:
maskA = None
if os.path.exists(os.path.join('mask_B' + self.ext)):
m = np.array(Image.open(os.path.join('mask_B' + self.ext)))
m = np.expand_dims(m, axis=2) if m.ndim == 2 else m
maskB = m < m.max() / 2
else:
maskB = None
ImgAs, PathAs, ImgBs, PathBs, ImgLRs, PathLRs = [], [], [], [], [], []
for p in As:
try:
img = np.array(Image.open(p))
img = np.expand_dims(img, axis=2) if img.ndim == 2 else img
if maskA:
img[maskA] = img.min()
ImgAs.append(img)
PathAs.append(p)
except KeyboardInterrupt:
raise
except Exception as e:
print('error when reading file ', p)
assert len(ImgAs) > 0, 'no file found for "A"'
for p in Bs:
try:
img = np.array(Image.open(p))
img = np.expand_dims(img, axis=2) if img.ndim == 2 else img
if maskB:
img[maskB] = img.min()
ImgBs.append(img)
PathBs.append(p)
except KeyboardInterrupt:
raise
except Exception as e:
print('error when reading file ', p)
for p in LRs:
try:
img = np.array(Image.open(p))
assert img.ndim == 2
if self.drift_correction:
import imreg_dft as ird
from skimage import exposure
b = ImgBs[0][:, :, 0]
b = exposure.equalize_hist(b)
b = scipy.ndimage.filters.gaussian_filter(b, sigma=(6, 6))
b = scipy.misc.imresize(b, img.shape[:2])
ts = ird.translation(b, img)
tvec = ts["tvec"].round(4)
# the Transformed IMaGe.
img = ird.transform_img(img, tvec=tvec)
img = scipy.misc.imresize(img, ImgBs[0].shape[:2])
img = np.expand_dims(img, axis=2)
ImgLRs.append(img)
PathLRs.append(p)
except KeyboardInterrupt:
raise
except Exception as e:
print('error when reading file ', p)
import traceback, sys
traceback.print_exc(file=sys.stdout)
self.__cache[path] = {
'A': ImgAs,
'B': ImgBs,
'LR': ImgLRs,
'path': path,
'pathA': PathAs,
'pathB': PathBs,
'pathLR': PathLRs
}
return True
from matplotlib import pyplot as plt
import cv2
import numpy as np
from skimage import exposure
image = cv2.imread("../fig/Fig1.jpg")
# image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
plt.figure()
plt.title("Original Image")
plt.imshow(image)
# Histogram
hist1 = np.histogram(image, bins=2) # Calculate histogram with numpy
hist2 = exposure.histogram(image, nbins=2) # Calculate histogram with skimage
plt.figure("Grayscale Histogram")
arr = image.flatten()
n, bins, patches = plt.hist(arr,
bins=256,
normed=1,
facecolor='black',
edgecolor='black')
# Histogram Equalization
img1 = exposure.equalize_hist(image)
arr1 = img1.flatten()
plt.figure("Image after Histogram Equalization")
plt.imshow(img1, plt.cm.gray) # Image after Histogram Equalization
plt.figure("Histogram Equalization")
plt.hist(arr1, bins=256, normed=1, edgecolor='None', facecolor='red')
plt.show()
def get_section_stats(bd, section_rows, section_cols, parameter_object,
section_counter):
"""
Split section into chunks and process features at each scale
Args:
bd (ndarray): The section array.
section_rows (int)
section_cols (int)
parameter_object (class object)
Returns:
List of computed features for each scale, for each statistic.
"""
if parameter_object.trigger in ['pantex', 'lac']:
out_d_range = (0, 31)
else:
out_d_range = (0, 255)
# Scale the data to an 8-bit range.
if (bd.dtype != 'uint8') and (parameter_object.trigger
not in parameter_object.spectral_indices):
bd = np.uint8(
rescale_intensity(bd,
in_range=(parameter_object.image_min,
parameter_object.image_max),
out_range=out_d_range))
# Apply histogram equalization.
if parameter_object.trigger != 'dmp':
if parameter_object.equalize:
bd = equalize_hist(bd, nbins=256)
elif parameter_object.equalize_adapt:
bd = equalize_adapthist(bd,
kernel_size=(int(section_rows / 128),
int(section_cols / 128)),
clip_limit=.05,
nbins=256)
if parameter_object.equalize or parameter_object.equalize_adapt:
bd = np.uint8(
rescale_intensity(bd, in_range=(0., 1.), out_range=(0, 255)))
# Remove image noise.
if parameter_object.smooth > 0:
bd = np.uint8(
cv2.bilateralFilter(bd, parameter_object.smooth, .1, .1))
# elif parameter_object.trigger == 'lbp':
#
# if parameter_object.visualize:
# bdOrig = bd.copy()
#
# elif parameter_object.trigger == 'hough':
#
# # for display (testing) purposes only
# if parameter_object.visualize:
# bdOrig = bd.copy()
#
# # test canny and hough lines
# if parameter_object.visualize:
#
# # for display purposes only
# bdOrig = bd.copy()
#
# test_plot(bd, bdOrig, parameter_object.trigger, parameter_object)
# Get the row and column section chunk indices.
# chunk_indices = get_chunk_indices(section_rows,
# section_cols,
# parameter_object.block,
# parameter_object.chunk_size,
# parameter_object.scales[-1])
func_dict = dict(
dmp=dict(name='Differential Morphological Profiles', args=dict()),
evi2=dict(name='Two-band Enhanced Vegetation Index', args=dict()),
fourier=dict(name='Fourier transfrom', args=dict()),
gabor=dict(name='Gabor filters', args=dict()),
gndvi=dict(name='Green Normalized Difference Vegetation Index',
args=dict()),
grad=dict(name='Gradient magnitude', args=dict()),
hog=dict(name='Histogram of Oriented Gradients', args=dict()),
lac=dict(name='Lacunarity', args=dict(lac_r=parameter_object.lac_r)),
lbp=dict(name='Local Binary Patterns', args=dict()),
lbpm=dict(name='Local Binary Patterns moments', args=dict()),
lsr=dict(name='Line support regions', args=dict()),
mean=dict(name='Mean', args=dict()),
ndvi=dict(name='Normalized Difference Vegetation Index', args=dict()),
pantex=dict(name='PanTex', args=dict(weight=parameter_object.weight)),
orb=dict(name='Oriented FAST and Rotated BRIEF key points',
args=dict()),
saliency=dict(name='Image saliency', args=dict()),
seg=dict(name='Segmentation', args=dict()),
sfs=dict(name='Structural Feature Sets',
args=dict(sfs_threshold=parameter_object.sfs_threshold,
sfs_skip=parameter_object.sfs_skip)))
for idx in parameter_object.spectral_indices:
if idx not in func_dict:
func_dict[idx] = {'name': idx, 'args': {}}
logger.info(' Processing {} for section {:,d} of {:,d} ...'.format(
func_dict[parameter_object.trigger]['name'], section_counter,
parameter_object.n_sects))
other_args = func_dict[parameter_object.trigger]['args']
if parameter_object.trigger in parameter_object.spectral_indices:
trigger = 'mean'
else:
trigger = parameter_object.trigger
return call_func(bd, parameter_object.block, parameter_object.scales,
parameter_object.scales[-1], trigger, **other_args)
def test_equalize_float():
img = skimage.img_as_float(test_img)
img_eq = exposure.equalize_hist(img)
cdf, bin_edges = exposure.cumulative_distribution(img_eq)
check_cdf_slope(cdf)
def test_equalize_uint8_approx():
"""Check integer bins used for uint8 images."""
img_eq0 = exposure.equalize_hist(test_img_int)
img_eq1 = exposure.equalize_hist(test_img_int, nbins=3)
np.testing.assert_allclose(img_eq0, img_eq1)
img_cdf, bins = exposure.cumulative_distribution(img, bins)
ax_cdf.plot(bins, img_cdf, 'r')
ax_cdf.set_yticks([])
return ax_img, ax_hist, ax_cdf
# Load an example image
img = data.moon()
# Contrast stretching
p2, p98 = np.percentile(img, (2, 98))
img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))
# Equalization
img_eq = exposure.equalize_hist(img)
# Adaptive Equalization
img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)
# Display results
fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(8, 5))
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
ax_img.set_title('Low contrast image')
y_min, y_max = ax_hist.get_ylim()
ax_hist.set_ylabel('Number of pixels')
ax_hist.set_yticks(np.linspace(0, y_max, 5))
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])
from tabulate import tabulate
import matplotlib.pyplot as plt
import time
from pylab import savefig
text = '----------REPORT----------'
#######################################################################################################################
# Import data #
#######################################################################################################################
print '--Importing data \n'
data = pickle.load(open("data/groundTruth.pkl", "rb"))
img = data['image']
img = exposure.equalize_hist(img)
mask = data['mask']
#######################################################################################################################
# Building training and test sets #
#######################################################################################################################
print '--Building sets \n'
h, w = img.shape
test_size = 0.85
text += '\n\n---Parameters--'
text += '\n-test_size = %s' % (test_size)
img_train = img[:, :w * (1 - test_size)]
mask_train = mask[:, :w * (1 - test_size)]
# 25 XP
# Import the required Scikit-image module for contrast.
# Instructions 2/4
# 25 XP
# Show the histogram from the original x-ray image chest_xray_image, using the hist() function.
# Instructions 3/4
# 25 XP
# Use histogram equalization on chest_xray_image to obtain the improved image and load it as xray_image_eq.
# Instructions 4/4
# 25 XP
# Show the resulting improved image xray_image_eq.
# Import the required module (Instruction 1)
from skimage import exposure
# Show original x-ray image and its histogram (Instruction 2)
show_image(chest_xray_image, 'Original x-ray')
plt.title('Histogram of image')
plt.hist(chest_xray_image.ravel(), bins=256)
plt.show()
# Use histogram equalization to improve the contrast (Instruction 3)
xray_image_eq = exposure.equalize_hist(chest_xray_image)
# Show the resulting image (Instruction 4)
show_image(xray_image_eq, 'Resulting image')
def dicom_to_array(img_rows, img_cols):
TrainData_dict = scipy.io.loadmat('../data/train.mat')
TrainMask_dict = scipy.io.loadmat('../data/trainseg.mat')
ValidateData_dict = scipy.io.loadmat('../data/validate.mat')
ValidateMask_dict = scipy.io.loadmat('../data/validateseg.mat')
TestData_dict = scipy.io.loadmat('../data/test.mat')
TestMask_dict = scipy.io.loadmat('../data/testseg.mat')
TrainData = TrainData_dict['image_all']
TrainMask = TrainMask_dict['seg_all']
ValidateData = ValidateData_dict['image_all']
ValidateMask = ValidateMask_dict['seg_all']
TestData = TestData_dict['image_all']
TestMask = TestMask_dict['seg_all']
d1 = TrainData.shape[0]
d2 = ValidateData.shape[0]
d3 = TestData.shape[0]
imgs1 = np.zeros( [d1, img_rows, img_cols])
imgs1_seg = np.zeros( [d1, img_rows, img_cols])
imgs2 = np.zeros( [d2, img_rows, img_cols])
imgs2_seg = np.zeros( [d2, img_rows, img_cols])
imgs3 = np.zeros( [d3, img_rows, img_cols])
imgs3_seg = np.zeros( [d3, img_rows, img_cols])
for kk in range(0,d1): #for train
img = TrainData[kk,:,:]
TrainData[kk,:,:] = equalize_hist( img.astype(int) )
imgs1[kk,:,:] = resize( TrainData[kk,:,:], (img_rows, img_cols), preserve_range=True)
imgs1_seg[kk,:,:] = resize( TrainMask[kk,:,:], (img_rows, img_cols), preserve_range=True)
for kk in range(0,d2): #for validate
img = ValidateData[kk,:,:]
ValidateData[kk,:,:] = equalize_hist( img.astype(int) )
imgs2[kk,:,:] = resize( ValidateData[kk,:,:], (img_rows, img_cols), preserve_range=True)
imgs2_seg[kk,:,:] = resize( ValidateMask[kk,:,:], (img_rows, img_cols), preserve_range=True)
for kk in range(0,d3): #for test
img = TestData[kk,:,:]
TestData[kk,:,:] = equalize_hist( img.astype(int) )
imgs3[kk,:,:] = resize( TestData[kk,:,:], (img_rows, img_cols), preserve_range=True)
imgs3_seg[kk,:,:] = resize( TestMask[kk,:,:], (img_rows, img_cols), preserve_range=True)
TrainData = np.concatenate(imgs1, axis=0).reshape(-1, img_rows, img_cols, 1)
TrainMask = np.concatenate(imgs1_seg, axis=0).reshape(-1, img_rows, img_cols, 1)
ValidateData = np.concatenate(imgs2, axis=0).reshape(-1, img_rows, img_cols, 1)
ValidateMask = np.concatenate(imgs2_seg, axis=0).reshape(-1, img_rows, img_cols, 1)
TestData = np.concatenate(imgs3, axis=0).reshape(-1, img_rows, img_cols, 1)
TestMask = np.concatenate(imgs3_seg, axis=0).reshape(-1, img_rows, img_cols, 1)
np.save('../data/train.npy', TrainData)
np.save('../data/train_masks.npy', TrainMask)
np.save('../data/validate.npy', ValidateData)
np.save('../data/validate_masks.npy', ValidateMask)
np.save('../data/test.npy', TestData)
np.save('../data/test_masks.npy', TestMask)
def test_equalize_ubyte():
img = util.img_as_ubyte(test_img)
img_eq = exposure.equalize_hist(img)
cdf, bin_edges = exposure.cumulative_distribution(img_eq)
check_cdf_slope(cdf)
inverse_thresh = np.invert(thresh_planes)
fig = plt.figure(figsize=(6, 6))
plt.imshow(inverse_thresh, "gray")
plt.show()
# %%
# Contrast stretching
p2 = np.percentile(image, 2)
p98 = np.percentile(image, 98)
image_ct = exposure.rescale_intensity(image, in_range=(p2, p98))
# Histogram Equalization
image_eq = exposure.equalize_hist(image)
# Show the images
fig = plt.figure(figsize=(20, 12))
# Subplot for original image
a = fig.add_subplot(3, 3, 1)
imgplot = plt.imshow(image)
a.set_title('Original')
# Subplot for contrast stretched image
a = fig.add_subplot(3, 3, 2)
imgplot = plt.imshow(image_ct)
a.set_title('Contrast Stretched')
# Subplot for equalized image
from matplotlib import pyplot as plt
from skimage import exposure
from utils import show_image, show_histogram
chest_xray_img = plt.imread('images/chest_xray.png')
show_image(chest_xray_img, "Chest x-ray")
show_histogram(chest_xray_img.ravel())
chest_xray_img_eq = exposure.equalize_hist(chest_xray_img)
show_image(chest_xray_img_eq, 'Resulting image')
show_plane(c, gamma_high[32], title=f'Gamma = {gamma_high_val}')
plot_hist(d, data)
plot_hist(e, gamma_low)
plot_hist(f, gamma_high)
# sphinx_gallery_thumbnail_number = 4
#####################################################################
# `Histogram
# equalization <https://en.wikipedia.org/wiki/Histogram_equalization>`_
# improves contrast in an image by redistributing pixel intensities. The most
# common pixel intensities get spread out, increasing contrast in low-contrast
# areas. One downside of this approach is that it may enhance background
# noise.
equalized_data = exposure.equalize_hist(data)
display(equalized_data)
#####################################################################
# As before, if we have a Jupyter kernel running, we can explore the above
# slices interactively.
explore_slices(equalized_data)
#####################################################################
# Let us now plot the image histogram before and after histogram equalization.
# Below, we plot the respective cumulative distribution functions (CDF).
_, ((a, b), (c, d)) = plt.subplots(nrows=2, ncols=2, figsize=(16, 8))
def plot_visualization_frame(eval_vis_dir, frame, name):
def crop_center(img, cropx, cropy):
y, x, _ = img.shape
startx = x // 2 - (cropx // 2)
starty = y // 2 - (cropy // 2)
return img[starty:starty + cropy, startx:startx + cropx, :]
def plot_img_and_hist(image, axes, bins=256):
#Plot an image along with its histogram and cumulative histogram.
image = img_as_float(image)
ax_img, ax_hist = axes
ax_cdf = ax_hist.twinx()
# Display image
ax_img.imshow(image, cmap=plt.cm.gray)
ax_img.set_axis_off()
ax_img.set_adjustable('box-forced')
# Display histogram
ax_hist.hist(image.ravel(), bins=bins, histtype='step', color='black')
ax_hist.ticklabel_format(axis='y',
style='scientific',
scilimits=(0, 0))
ax_hist.set_xlabel('Pixel intensity')
ax_hist.set_xlim(0, 1)
ax_hist.set_yticks([])
# Display cumulative distribution
img_cdf, bins = exposure.cumulative_distribution(image, bins)
ax_cdf.plot(bins, img_cdf, 'r')
ax_cdf.set_yticks([])
return ax_img, ax_hist, ax_cdf
frame = crop_center(frame, 112, 112)
frame = frame[:, :, 0]
max, min = np.max(frame), np.min(frame)
frame = ((frame - min) / (max - min) * 255).astype('uint8')
# Contrast stretching
p2, p98 = np.percentile(frame, (2, 98))
img_rescale = exposure.rescale_intensity(frame, in_range=(p2, p98))
# Equalization
img_eq = exposure.equalize_hist(frame)
# Display results
fig = plt.figure(figsize=(12, 8))
axes = np.zeros((2, 3), dtype=np.object)
axes[0, 0] = fig.add_subplot(2, 3, 1)
for i in range(1, 3):
axes[0, i] = fig.add_subplot(2,
3,
1 + i,
sharex=axes[0, 0],
sharey=axes[0, 0])
for i in range(3, 6):
axes[i // 3, i % 3] = fig.add_subplot(2, 3, 1 + i)
ax_img, ax_hist, ax_cdf = plot_img_and_hist(frame, axes[0:2, 0])
ax_img.set_title('Feature map')
y_min, y_max = ax_hist.get_ylim()
ax_hist.set_ylabel('Number of pixels')
ax_hist.set_yticks(np.linspace(0, y_max, 5))
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[0:2, 1])
ax_img.set_title('Contrast stretching')
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[0:2, 2])
ax_img.set_title('Histogram equalization')
ax_cdf.set_ylabel('Fraction of total intensity')
ax_cdf.set_yticks(np.linspace(0, 1, 5))
fig.tight_layout()
plt.savefig(os.path.join(eval_vis_dir, '{}.png'.format(name)))
plt.close()
def tile_alt_imshow(img_arrays,
heat_maps=None,
labels=None,
titles=None,
label_choice=1,
width=30,
height=30,
save_it=None,
h_slot=None,
w_slot=None,
CAM=None,
cmap='seismic',
alpha=0.3,
vmin=None,
vmax=None,
colorbar=False,
prob_array=None,
force_single_channel=False):
plot_heat_map = None
#scaled_img_arrays = rescale_img(img_arrays)
scaled_img_arrays = img_arrays
if len(img_arrays.shape) == 4:
img_n, img_h, img_w, _ = img_arrays.shape
else:
img_n, img_h, img_w = img_arrays.shape
if h_slot is None:
h_slot = int(math.ceil(np.sqrt(img_n)))
if w_slot is None:
w_slot = int(math.ceil(np.sqrt(img_n)))
fig, axes = plt.subplots(h_slot, w_slot, figsize=(width, height))
fig.subplots_adjust(hspace=0.1, wspace=0)
for ax, i in zip(axes.flatten(), range(img_n)):
#img = rescale_img(scaled_img_arrays[i])
img = scaled_img_arrays[i]
#p2, p98 = np.percentile(img, (2, 98))
#img = exposure.rescale_intensity(img, in_range=(p2, p98))
img = exposure.equalize_hist(img)
if labels is not None:
c_labels = resize_label_array(labels[i], (img_h, img_w))
img *= np.expand_dims(c_labels[..., label_choice], axis=2)
if force_single_channel:
cxr = ax.imshow(img[..., 0], cmap='gray')
else:
cxr = ax.imshow(img, 'gray')
if prob_array is not None:
c_prob = prob_array[i].round(2)
c_text = 'Pelvis: {0:.2f}\nHip: {1:.2f}\nFail: {2:.2f}\nCXR: {3:.2f}'.format(
c_prob[0], c_prob[1], c_prob[2], c_prob[3])
ax.text(10, 30, c_text, bbox={'facecolor': 'white', 'pad': 2})
if heat_maps is not None:
resized_map = transform.resize(heat_maps[i], (224, 224),
mode='reflect',
anti_aliasing=True)
plot_heat_map = ax.imshow(resized_map,
cmap=cmap,
alpha=alpha,
vmin=vmin,
vmax=vmax)
if CAM is not None:
resized_map = transform.resize(CAM[i], (224, 224),
mode='reflect',
anti_aliasing=True)
resized_map = rescale_img(resized_map)
plot_heat_map = ax.imshow(resized_map,
cmap=cmap,
alpha=alpha,
vmin=vmin,
vmax=vmax)
if titles is not None:
ax.set_title(titles[i], color='red')
ax.axis('off')
if colorbar:
fig.colorbar(plot_heat_map)
if save_it is not None:
plt.savefig(save_it, dpi=300, format='png')
else:
plt.show()
def order_location_plot(outpath, obj_base_name, flat_base_name, flat_img,
obj_img, orders):
pl.figure('orders', facecolor='white', figsize=(8, 5))
pl.cla()
pl.suptitle('order location and identification', fontsize=14)
pl.set_cmap('Blues_r')
pl.rcParams['ytick.labelsize'] = 8
obj_plot = pl.subplot(1, 2, 1)
try:
obj_plot.imshow(exposure.equalize_hist(obj_img))
except BaseException:
obj_plot.imshow(obj_img)
obj_plot.set_title('object ' + obj_base_name)
obj_plot.set_ylim([1023, 0])
obj_plot.set_xlim([0, 1023])
flat_plot = pl.subplot(1, 2, 2)
try:
flat_plot.imshow(exposure.equalize_hist(flat_img))
except BaseException:
flat_plot.imshow(flat_img)
flat_plot.set_title('flat ' + flat_base_name)
flat_plot.set_ylim([1023, 0])
flat_plot.set_xlim([0, 1023])
for order in orders:
obj_plot.plot(np.arange(1024),
order.flatOrder.topEdgeTrace,
'k-',
linewidth=1.0)
obj_plot.plot(np.arange(1024),
order.flatOrder.botEdgeTrace,
'k-',
linewidth=1.0)
obj_plot.plot(np.arange(1024),
order.flatOrder.smoothedSpatialTrace,
'y-',
linewidth=1.0)
obj_plot.text(10,
order.flatOrder.topEdgeTrace[0] - 10,
str(order.flatOrder.orderNum),
fontsize=10)
flat_plot.plot(np.arange(1024),
order.flatOrder.topEdgeTrace,
'k-',
linewidth=1.0)
flat_plot.plot(np.arange(1024),
order.flatOrder.botEdgeTrace,
'k-',
linewidth=1.0)
flat_plot.plot(np.arange(1024),
order.flatOrder.smoothedSpatialTrace,
'y-',
linewidth=1.0)
flat_plot.text(10,
order.flatOrder.topEdgeTrace[0] - 10,
str(order.flatOrder.orderNum),
fontsize=10)
pl.tight_layout()
pl.savefig(constructFileName(outpath, obj_base_name, None, 'traces.png'))
pl.close()
def hist_equalize(img):
return exposure.equalize_hist(img)
def time_equalize_hist(self):
# Run 10x to average out performance
# note that this is not needed as asv does this kind of averaging by
# default, but this loop remains here to maintain benchmark continuity
for i in range(10):
result = exposure.equalize_hist(self.image)
xmin, xmax = dtype_range[image.dtype.type]
ax_hist.set_xlim(xmin, xmax)
# Display cumulative distribution
img_cdf, bins = exposure.cumulative_distribution(image, bins)
ax_cdf.plot(bins, img_cdf, 'r')
return ax_img, ax_hist, ax_cdf
# Load an example image
img = img_as_ubyte(data.moon())
# Global equalize
img_rescale = exposure.equalize_hist(img)
# Equalization
selem = disk(30)
img_eq = rank.equalize(img, selem=selem)
# Display results
fig = plt.figure(figsize=(8, 5))
axes = np.zeros((2, 3), dtype=np.object)
axes[0, 0] = plt.subplot(2, 3, 1)
axes[0, 1] = plt.subplot(2, 3, 2, sharex=axes[0, 0], sharey=axes[0, 0])
axes[0, 2] = plt.subplot(2, 3, 3, sharex=axes[0, 0], sharey=axes[0, 0])
axes[1, 0] = plt.subplot(2, 3, 4)
axes[1, 1] = plt.subplot(2, 3, 5)
axes[1, 2] = plt.subplot(2, 3, 6)
# We compare here how the global histogram equalization is applied locally.
#
# The equalized image [2]_ has a roughly linear cumulative distribution
# function for each pixel neighborhood. The local version [3]_ of the
# histogram equalization emphasizes every local gray-level variations.
#
# .. [2] http://en.wikipedia.org/wiki/Histogram_equalization
# .. [3] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization
from skimage import exposure
from skimage.filters import rank
noisy_image = img_as_ubyte(data.camera())
# equalize globally and locally
glob = exposure.equalize_hist(noisy_image) * 255
loc = rank.equalize(noisy_image, disk(20))
# extract histogram for each image
hist = np.histogram(noisy_image, bins=np.arange(0, 256))
glob_hist = np.histogram(glob, bins=np.arange(0, 256))
loc_hist = np.histogram(loc, bins=np.arange(0, 256))
fig, axes = plt.subplots(3, 2, figsize=(10, 10))
ax = axes.ravel()
ax[0].imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray)
ax[0].axis('off')
ax[1].plot(hist[1][:-1], hist[0], lw=2)
ax[1].set_title('Histogram of gray values')
def __getitem__(self, idx):
self.clone = False
self.orig_noise = False
self.binary_orig = False
self.binary_mask = False
self.binary_ones_pow = False
self.binary_orig_pow = False
file_name = self.file_names[idx]
if self.working_dir is not None:
file = os.path.join(self.working_dir, file_name)
else:
file = file_name
sample, _ = self.t_spectrogram(file)
sample_spec = sample.clone()
# Data augmentation
if self.augmentation:
sample_spec = self.t_amplitude(sample_spec)
sample_spec = self.t_pitchshift(sample_spec)
sample_spec = self.t_timestretch(sample_spec)
sample_orca_detect = sample_spec.clone()
sample_orca_detect = self.t_compr_a(sample_orca_detect)
sample_orca_detect = self.t_norm(sample_orca_detect)
sample_spec = self.sp.detect_strong_spectral_region(sample_orca_detect, sample_spec).unsqueeze(dim=0)
sample_spec_ncmpr = sample_spec.clone()
sample_spec = self.t_compr_f(sample_spec)
# input not compressed, but 0/1 normalized for binary cases
binary_input_not_cmpr_not_norm = sample_spec_ncmpr.clone()
binary_input = self.t_compr_a(binary_input_not_cmpr_not_norm)
binary_input = self.t_norm(binary_input)
# frequency compressed, to amplitude and normalized ground truth
ground_truth = sample_spec.clone()
ground_truth = self.t_compr_a(ground_truth)
ground_truth = self.t_norm(ground_truth)
# ARTF PART
distribution_idx = random.randint(0, 9)
if distribution_idx != 4:
sample_spec = self.t_compr_a(sample_spec)
if distribution_idx == 0:
if self.random:
gaus_stdv = float(random.randint(0, 25))
else:
gaus_stdv = self.gaus_stdv
distribution = torch.distributions.normal.Normal(torch.tensor(self.gaus_mean),
torch.tensor(gaus_stdv)).sample(
sample_shape=torch.Size([128, 256])).squeeze(dim=-1)
elif distribution_idx == 1:
if self.random:
df = float(random.randint(0, 30))
else:
df = self.df
distribution = torch.distributions.chi2.Chi2(torch.tensor(df)).sample(
sample_shape=torch.Size([128, 256])).squeeze(dim=-1)
elif distribution_idx == 2:
if self.random:
p_lambda = float(random.randint(0, 30))
else:
p_lambda = self.poisson_lambda
distribution = torch.distributions.poisson.Poisson(torch.tensor(p_lambda)).sample(
sample_shape=torch.Size([128, 256])).squeeze(dim=-1)
elif distribution_idx == 3:
if self.random:
e = round(random.uniform(0.05, 0.15), 2)
else:
e = self.exp_e
distribution = torch.distributions.exponential.Exponential(torch.tensor(e)).sample(
sample_shape=torch.Size([128, 256])).squeeze(dim=-1)
elif distribution_idx == 4:
if not self.random:
self.t_addnoise.min_snr = self.orig_noise_value
self.t_addnoise.max_snr = self.orig_noise_value
self.orig_noise = True
elif distribution_idx == 5:
# histogram equalization is always constant - no probabilistic effects!
self.clone = True
elif distribution_idx == 6:
self.binary_orig = True
elif distribution_idx == 7:
if self.random:
bin_pow = round(random.uniform(1.3, 2.7), 2)
else:
bin_pow = self.bin_pow
self.binary_ones_pow = True
elif distribution_idx == 8:
if self.random:
bin_pow = round(random.uniform(1.3, 2.7), 2)
else:
bin_pow = self.bin_pow
self.binary_orig_pow = True
elif distribution_idx == 9:
self.binary_mask = True
if self.orig_noise:
# Add original noise to the sample
sample_spec_n, _ = self.t_addnoise(sample_spec)
sample_spec_n = self.t_compr_a(sample_spec_n)
sample_spec_n = self.t_norm(sample_spec_n)
elif self.clone:
sample_spec_n = self.t_compr_a(sample_spec_ncmpr)
sample_spec_n = self.t_norm(sample_spec_n)
sample_spec_n = self.sp.search_maxima_spec(sample_spec_n, radius=2)
sample_spec_n = torch.tensor(exposure.equalize_hist(np.nan_to_num(sample_spec_n.squeeze(dim=0).numpy())),
dtype=torch.float)
sample_spec_n = self.t_compr_f(sample_spec_n.unsqueeze(dim=0))
elif self.binary_orig:
# amplitude and normalized
sample_spec_n = binary_input.clone()
binary_mask = self.sp.create_mask(trainable_spectrogram=sample_spec_n).unsqueeze(dim=0)
ground_truth = binary_input * binary_mask
ground_truth = self.t_compr_f(ground_truth)
sample_spec_n = self.t_compr_f(binary_input_not_cmpr_not_norm)
sample_spec_n = self.t_compr_a(sample_spec_n)
sample_spec_n = self.t_norm(sample_spec_n)
elif self.binary_ones_pow:
sample_spec_n = binary_input.clone()
binary_mask = self.sp.create_mask(trainable_spectrogram=sample_spec_n).unsqueeze(dim=0)
ground_truth = binary_input + binary_mask
ground_truth[ground_truth >= 1.0] = 1.0
ground_truth = ground_truth.pow(bin_pow)
ground_truth = self.t_compr_f(ground_truth)
sample_spec_n = self.t_compr_f(binary_input_not_cmpr_not_norm)
sample_spec_n = self.t_compr_a(sample_spec_n)
sample_spec_n = self.t_norm(sample_spec_n)
elif self.binary_orig_pow:
sample_spec_n = binary_input.clone()
binary_mask = self.sp.create_mask(trainable_spectrogram=sample_spec_n).unsqueeze(dim=0)
ground_truth = binary_input + binary_mask
ground_truth[ground_truth >= 1.0] = 0.0
ground_truth = ground_truth.pow(bin_pow)
ground_truth = ground_truth + (sample_spec_n * binary_mask)
ground_truth = self.t_compr_f(ground_truth)
sample_spec_n = self.t_compr_f(binary_input_not_cmpr_not_norm)
sample_spec_n = self.t_compr_a(sample_spec_n)
sample_spec_n = self.t_norm(sample_spec_n)
elif self.binary_mask:
sample_spec_n = binary_input.clone()
binary_mask = self.sp.create_mask(trainable_spectrogram=sample_spec_n).unsqueeze(dim=0)
ground_truth = binary_mask
ground_truth = self.t_compr_f(ground_truth)
sample_spec_n = self.t_compr_f(binary_input_not_cmpr_not_norm)
sample_spec_n = self.t_compr_a(sample_spec_n)
sample_spec_n = self.t_norm(sample_spec_n)
else:
sample_spec_n = sample_spec + distribution
sample_spec_n = self.t_norm(sample_spec_n)
label = self.load_label(file)
label["ground_truth"] = ground_truth
label["file_name"] = label["file_name"].replace(label["file_name"].rsplit("/", 1)[1], str(distribution_idx)+"_"+label["file_name"].rsplit("/", 1)[1])
return sample_spec_n, label
def composite_fig(data, bands, title, scalebar_color=None, fig_name=None, max_size=16, hist_str=None, \
v_min = None, v_max = None):
"""
Description:
Create a three band (one time) composite figure
-----
Input:
data: one time xarray.Dataset containing the three bands mentionned in bands.
bands: bands to be used in the composite (RGB order).
title: prefix of the figure title.
scalebar_color (OPTIONAL): scalebar color (https://matplotlib.org/examples/color/named_colors.html)
fig_name (OPTIONAL): file name (including extension) to save the figure (show only if not added to input).
max_size (OPTIONAL, default 16): maximum size of the figure (either horizontal or vertical).
hist_str: (OPTIONAL): histogram stretch type (['contr','eq','ad_eq']). Cannot be used with v_min, v_max options.
v_min (OPTIONAL, default minimum value): minimum value to display. Cannot be used with hist_str option.
v_max (OPTIONAL, default maximum value): maximum value to display. Cannot be used with hist_str option.
Output:
figure.
"""
# check options combination
assert not((hist_str is not None) and (v_min is not None or v_max is not None)) , \
'hist_str option cannot be used with v_min, vmax options'
assert not((v_min is not None) ^ (v_max is not None)), \
'v_min option requires v_max option, and inverserly'
if v_min is not None:
assert v_min < v_max, 'v_min value must be lower than v_max'
# Create a copy to unlink from original dataset
rgb = data.copy(deep=True)
height, width, orient, posit = fig_aspects(rgb.sizes, max_size)
rgb = np.stack([rgb[bands[0]], rgb[bands[1]], rgb[bands[2]]])
# perform stretch on each band
for b in range(3):
# https://scikit-image.org/docs/dev/auto_examples/color_exposure/plot_equalize.html
# Contrast stretching
if hist_str == 'contr':
p2, p98 = np.nanpercentile(rgb[b], (2, 98))
rgb[b] = exposure.rescale_intensity(rgb[b], in_range=(p2, p98))
# Equalization
if hist_str == 'eq':
rgb[b] = exposure.equalize_hist(rgb[b])
# Adaptive Equalization
if hist_str == 'ad_eq':
rgb[b] = exposure.equalize_adapthist(rgb[b], clip_limit=0.03)
rgb = np.stack(rgb, axis=-1)
# normalize between 0 and 1
if v_min is None:
rgb = (rgb - np.nanmin(rgb)) / (np.nanmax(rgb) - np.nanmin(rgb))
else:
rgb = (rgb - v_min) / (v_max - v_min)
# Start plotting the figure
plt.close('all')
fig, ax = plt.subplots()
fig.set_size_inches(width, height)
im = ax.imshow(rgb, vmin=0, vmax=1)
# add a scalebar if required
if scalebar_color:
ax.add_artist(create_scalebar(data, ax, scalebar_color))
# ticks moved 1 pixel inside to guarantee they are displayed
plt.yticks([rgb.shape[0] - 1, 1], xtrms_format(data.latitude.values))
plt.xticks([1, rgb.shape[1] - 1], xtrms_format(data.longitude.values))
plt.title(title, weight='bold', fontsize=16)
fig.tight_layout()
if fig_name:
plt.savefig(fig_name, dpi=150)
display(
HTML("""<a href="{}" target="_blank" >View and download {}</a>""".
format(fig_name, basename(fig_name))))
else:
plt.show()
plt.close()