def GeneratePlot(outfile, Filename, xpixel, ypixel, nyanFlag=0, nyanAudio=0):
video = imageio.get_reader(Filename, 'ffmpeg')
colorArr = []
maxLen = int(len(video))
gradient = np.linspace(0,1,256)
gradient = np.vstack((gradient, gradient))
index = range(maxLen)
RGB = [[],[],[]] #0 - Red, 1 - Green, 2 - Blue
progress = ""
if(nyanAudio):
progress = NyanBar(audiofile="./NyanCat.mp3")
elif(nyanFlag):
progress = NyanBar()
try:
print "Starting Collection"
for i in index:
try:
frame = video.get_data(i)
pass
except KeyboardInterrupt:
print "\nEnding Collection"
break
tempArr = frame[ypixel,xpixel] #Get the Frame at X=xpixel and Y=ypixel
for j in range(3):
RGB[j].append(float(tempArr[j])/256.0) #Normalizing the RGB values to 256 to get RGB value less than 1 for Matplotlib cmap
colorArr.append([x[i] for x in RGB]) #Get the RGB values of the most recent entry and appent to colorArr
if(nyanFlag):
progress.update(int(float(i)/float(maxLen) * 100))
#sys.stdout.write("\rFrame %i of %i" % (i, maxLen))
#sys.stdout.flush()
if(nyanFlag):
progress.finish()
#RGB Plots for Rates
plt.plot(RGB[0], 'r')
plt.plot(RGB[1], 'g')
plt.plot(RGB[2], 'b')
plt.savefig(AppendTitleToFilename(outfile, "RGB"))
plt.clf()
#Making Colorbar
cmap1 = LinearSegmentedColormap.from_list("cmap", colorArr, N=256, gamma=1.0) #Need normalized RGB to 256 therefore from 0 to 1
gradient = np.linspace(0,1,256)
gradient = np.vstack((gradient, gradient))
plt.imshow(gradient, aspect='auto', cmap=cmap1)
plt.savefig(outfile)
plt.clf()
return
except KeyboardInterrupt:
print "\nEnding Plot"
def __init__(self, img_paths, outfile_name=("data/align/fundus_photo_aligner_%s.csv" % str(uuid.uuid4())[:8])):
path = '/'.join(img_paths[0].split('/')[:-1])
img_names = [img_path.split('/')[-1] for img_path in img_paths]
ext = img_names[0].split('.')[-1]
ids = set([img_path.split('_')[0] for img_path in img_names])
assert((len(ids) * 2) == len(img_names))
grouped_img_names = [["%s_left" % id, "%s_right" % id] for id in ids]
self.labels = {}
print ''.join([" %i%%|" % (j*10) for j in xrange(1,10)] + [' 100%|'])
pb = NyanBar(tasks=len(grouped_img_names))
with open(outfile_name, 'w') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['image','horizontal_flip','vertical_flip'])
for img_names in grouped_img_names:
img_paths = ["%s/%s.%s" % (path, name, ext) for name in img_names]
fundus_photos = [FundusPhoto(img_path) for img_path in img_paths]
for i, fundus_photo in enumerate(fundus_photos):
fundus_photo.refine_center()
direct = fundus_photo.has_notch()
lens_flip_vector = np.array([0,0] if direct else [1,1])
left_right_flip_vector = np.array([0,1] if i == 0 else [0,0])
final_flip_vector = (lens_flip_vector + left_right_flip_vector) % 2
writerow = [img_names[i]] + final_flip_vector.tolist()
writer.writerow(writerow)
self.labels[img_names[i]] = direct
pb.task_done()
pb.finish()
print("Done! created: %s" % outfile_name)
def benchmark_put_with_many_filters():
hosts = ["bloom1", "bloom2", "bloom3", "bloom4"]
client = BloomRouter(hosts, "g{}".format(random.randint(1, 100000), filter_count=16))
progress = NyanBar(tasks=testsize)
for i in range(testsize):
progress.task_done()
keys = [str(uuid.uuid4()) for _ in range(num_keys)]
client.add(keys)
progress.finish()
def put_then_get_with_one_filter():
hosts = ["bloom1", "bloom2", "bloom3", "bloom4"]
client = BloomRouter(hosts, "f{}".format(random.randint(1, 100000)), filter_count=1)
progress = NyanBar(tasks=testsize)
for i in range(testsize):
progress.task_done()
keys = [str(uuid.uuid4()) for _ in range(num_keys)]
client.add(keys)
assert client.get(keys)
progress.finish()
def package_data(directory, datasets, image_shape, outfile_path):
"""
Outputs a *.npz file of image data in 3 sets
:type directory: string
:param directory: directory of image data
:type datasets: tuple of dictionaries, string -> Any
:param datasets: dictionaries should be in the order
training, validation, test. The key is the image
filename and the value is the true label
:type image_shape: tuple of ints
:type outfile_path: string
"""
assert(len(datasets) == 3)
xsize = numpy.prod(image_shape)
x_datasets = [numpy.zeros((len(dataset), xsize), dtype=numpy.uint8) for dataset in datasets]
y_datasets = [numpy.array(dataset.values(), dtype=numpy.uint8) for dataset in datasets]
print "| " + ("⚐ ⚑ " * 19) + "-|"
pb = NyanBar(tasks=sum([len(dataset) for dataset in datasets]))
for j, dataset in enumerate(datasets):
for i, image_name in enumerate(dataset.keys()):
pb.task_done()
im = Image.open(directory + image_name)
x_datasets[j][i, :] = numpy.array(im.getdata(), dtype=numpy.uint8).reshape(xsize)
pb.finish()
print '... saving data'
# cPickle too slow (takes many minutes for tens of thousands of images over 100x100x3)
saveme = [x_datasets[0], y_datasets[0], x_datasets[1], y_datasets[1], x_datasets[2], y_datasets[2]]
numpy.savez(open(outfile_path, 'wb'), *saveme)
print 'done'
if len(sys.argv) == 2: # single image
predict_single_image(sys.argv[1])
elif len(sys.argv) == 4: # batch of single images
label_csv = sys.argv[1]
inpath = sys.argv[2]
outpath = sys.argv[3]
true_labels = []
predicted_labels = []
with open(label_csv, 'rU') as csvfile:
reader = csv.reader(csvfile, dialect=csv.excel_tab, delimiter=',')
next(reader, None) # skip header
print ''.join([" %i%%|" % (j*10) for j in xrange(1,10)] + [' 100%|'])
pb = NyanBar(tasks=(sum(1 for row in open(label_csv, 'rU'))-1))
for row in reader:
imgname = row[0]
true_label = 1 if (row[1] == 'tr' or row[1] == 'br' or row[1] == '1') else 0
title_prefix = "True Label: %s" % true_label
predicted_label = predict_single_image(inpath + imgname, title_prefix=title_prefix, save_path=(outpath + imgname), true_label=true_label)
true_labels.append(true_label)
predicted_labels.append(predicted_label)
pb.task_done()
pb.finish()
accuracy, precision = binary_accuracy_precision(true_labels, predicted_labels)
print("Accuracy: %.3f. Precision: %.3f." % (accuracy, precision))
elif len(sys.argv) == 3: # batch of doubled images
true_label_csv = sys.argv[1]
default=False,
action='store_true',
help='show a Nyan Cat progress bar')
return arg_parser.parse_args()
def lint_file(linter, file):
linter.set_file(file)
if args.fix:
linter.format()
else:
linter.lint()
@atexit.register
def clean_up():
if nyan_progress:
nyan_progress.finish()
args = parse_args()
nyan_progress = None
if args.nyan:
nyan_mp3_file = os.path.dirname(__file__) + '/nyan.mp3'
nyan_progress = NyanBar(audiofile=nyan_mp3_file)
lintthesql(args)
evaluation_results = []
for ei, e in enumerate(emails_dir):
evaluation = ""
emails = []
for d in os.listdir(e):
if ("spam" in d or "ham" in d) and os.path.isfile(os.path.join(e, d)):
emails.append(os.path.join(e, d))
else:
print "The test filename must either contain word *spam* or *ham* indicating its class!"
print "The filter won't be tested on `%s` file." % os.path.join(
e, d)
if show_progress and progress_bar == "nyan":
print "Test [%d/%d]" % (ei + 1, len(emails_dir))
progress = NyanBar(tasks=100)
tp, tn, fp, fn = 0, 0, 0, 0
evaluation_start_time = time.time()
for ii, i in enumerate(emails):
if show_progress and progress_bar == "classic":
print "[%d/%d] %.2d%% (%s)" % (ei + 1, len(emails_dir),
100. * ii / len(emails), i)
elif show_progress and progress_bar == "nyan":
progress.update(100. * ii / len(emails))
current_email = execute % os.path.abspath(i)
if "spam" in os.path.split(i)[1]:
ground_truth = "spam"
elif "ham" in os.path.split(i)[1]:
ground_truth = "ham"
else:
sys.exit(
from nyanbar import NyanBar
import time
progress = NyanBar(audiofile="NyanCat-original.mp3")
for n in range(100):
time.sleep(.3)
progress.update(n)
progress.finish()
# Number of hours to run the experiment
nHours = 18
# Sampling every x minutes
intervals = 5
nMins = nHours * 60
timer = 0
leds = 1
# Initialize the leds with the first one only
pfd.leds[0].turn_on()
pfd.leds[0].set_high()
progress = NyanBar()
while timer <= nMins:
# Update progress bar
progress.update((timer / nMins) * 100)
# Light up the transilluminator
pfd.relays[0].value = 1
# Set camera values
camera.awb_gains = 1, 0.5
camera.brightness = 55
camera.saturation = 30
camera.exposure_compensation = 25
camera.contrast = 50
camera.shutter_speed = 220000
