def get_styles(self):
if self.styles:
return self.styles
style_path = os.path.join(self.file_path, "word/styles.xml")
with open(style_path, encoding="UTF-8") as f:
styles = f.read()
styles = BeautifulSoup(styles, "xml")
self.styles = Styles(styles)
return self.styles
def _write_styles_file(self):
# Write the style xml file.
xf_formats = self.workbook.xf_formats
palette = self.workbook.palette
font_count = self.workbook.font_count
num_format_count = self.workbook.num_format_count
border_count = self.workbook.border_count
fill_count = self.workbook.fill_count
custom_colors = self.workbook.custom_colors
dxf_formats = self.workbook.dxf_formats
styles = Styles()
styles._set_style_properties([
xf_formats,
palette,
font_count,
num_format_count,
border_count,
fill_count,
custom_colors,
dxf_formats])
styles._set_xml_writer(self._filename('xl/styles.xml'))
styles._assemble_xml_file()
def add_output_box(self):
text = tk.Text(self, Styles('text'))
text.grid(row=3, column=0, sticky='WS')
return text
def add_search_term(self):
label = tk.Label(self, Styles('label', {'text': 'Search Term'}))
label.grid(row=0, column=2, sticky='W')
entry_search_term = tk.Entry(self, Styles('entry'))
entry_search_term.grid(row=1, column=2)
return entry_search_term
def add_web_address(self):
label = tk.Label(self, Styles('label', {'text': 'Web Address'}))
label.grid(row=0, column=0, sticky='W')
entry_web_address = tk.Entry(self, Styles('entry'))
entry_web_address.grid(row=1, column=0)
return entry_web_address
def __init__(self, root, data, devices):
"""Method initiates GUI and all it's main components"""
super().__init__(root)
self.root = root
self.root.geometry('500x400')
self.styles = Styles()
# make GUI
self.main_frame = Frame(self.root, height=400, width=500)
self.main_frame.pack_propagate(0)
self.main_frame.pack()
self.left_frame = Frame(self.main_frame,
relief='groove',
bd=3,
height=400,
width=100)
self.right_frame = Frame(self.main_frame,
relief='groove',
bd=3,
height=400,
width=500)
self.right_top_frame = Frame(self.right_frame,
relief='groove',
bd=1,
height=200,
width=350)
self.right_bottom_frame = Frame(self.right_frame,
relief='groove',
bd=1,
height=200,
width=350)
self.left_frame.pack(anchor='n', side=LEFT, fill='x')
self.right_frame.pack(anchor='n', side=RIGHT)
self.right_top_frame.pack()
self.right_bottom_frame.pack()
self.button_add_device = Button(self.left_frame,
text='Add Device',
relief='groove',
command=self.add_device).pack(
side=TOP, expand=True, fill='x')
# data export
self.data = data
self.start_recording_button = Button(
self.right_bottom_frame,
text='Start Recording',
relief='groove',
command=self.start_recording).pack(side=LEFT)
self.stop_recording_button = Button(
self.right_bottom_frame,
text='Stop Recording',
relief='groove',
command=self.stop_recording).pack()
# IoT devices - menu
self.devices = devices
self.iot_dev_name_var = StringVar()
self.iot_dev_name_var.set(
self.devices.list_of_devices[0].serial_number)
self.iot_dev_name_var.trace(
'w', lambda *pargs: self.callback_iot_dev_name_var())
self.radio_buttons_init()
# other objects
self.ani = None
self.main_frame.pack_propagate(0)
self.root.resizable(0, 0)
def recompute_and_populate():
"""
- load pickled vectorizer
- transform docs
- compute cosine similarity for all vector pairs
- data is retrieved at rev_rollup_ct = 1 (beer level)
"""
vec_pkl = "src/vocab/review_vectorizer.p"
was_pkl, vec = load_vec(vec_pkl)
# load data for styles with feature sets
# overridden until full feature table is populated
styles = Styles()
top_sy = [159, 84, 157, 56, 58, 9, 128, 97, 116, 140]
print 'Comparing the top %s styles: %s' % (len(top_sy), ', '.join(
str(s) for s in top_sy))
X = styles.beer_reviews_rollup(top_sy,
limit=0,
rev_rollup_ct=1,
shuffle=False)
if was_pkl:
print "Loaded pickled vectorizer."
print "Feature count: %s" % len(vec.get_feature_names())
print "Transforming reviews"
trans_pool = Pool(min(10, len(top_sy)))
res_t = trans_pool.map(__asyncable_transform,
[(vec, sy, X[X['style_id'] == sy])
for sy in top_sy])
# as style keyed dict
res_t = {r[0]: {'beer_ids': r[1], 'X_t': r[2]} for r in res_t}
else:
# exit program
return 0
print 'Truncating similarity table'
bs = BeerSimilarity()
# bs.remove_all()
dim1 = sum(v['X_t'].shape[0] for k, v in res_t.iteritems())
dim2 = sum(len(v['X_t'].data) for k, v in res_t.iteritems())
print 'Computing similarities and saving to db %s' % dim1
print 'Nonzero elements %s' % dim2
# set style RU
# will account for symmetry in the database
# ru_sids = [ (top_sy[i], top_sy[j]) for i in xrange(len(top_sy)) for j in xrange(i,len(top_sy)) ]
ru_sids = [(top_sy[i], top_sy[i]) for i in xrange(len(top_sy))]
pool_inp = []
for ruc in ru_sids:
X_t_ref = res_t[ruc[0]]['X_t']
b_id_ref = res_t[ruc[0]]['beer_ids']
X_t_comp = res_t[ruc[1]]['X_t']
b_id_comp = res_t[ruc[1]]['beer_ids']
pool_inp.append((bs, b_id_ref, X_t_ref, b_id_comp, X_t_comp, 100))
p = Pool(min(10, len(top_sy)))
b_id_res = p.map(__asyncable_similarity, pool_inp)
for res in b_id_res:
if res[1] is not None:
print '%s %s' % (', '.join(str(r) for r in res[0]), res[1])
# Description:
# Modified to support streaming out with webcams, and not just raw JPEGs.
# Most of the code credits to Miguel Grinberg, except that I made a small tweak. Thanks!
# Credits: http://blog.miguelgrinberg.com/post/video-streaming-with-flask
#
# Usage:
# 1. Install Python dependencies: cv2, flask. (wish that pip install works like a charm)
# 2. Run "python main.py".
# 3. Navigate the browser to the local webpage.
from flask import Flask, render_template, Response, request
from camera import VideoCamera
from styles import Styles
import json
app = Flask(__name__)
styles = Styles()
def generate_video(camera):
while True:
frame = camera.get_frame()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
def init_styles():
styles.set_eyebrows({"r": 0, "g": 0, "b": 255, "opacity": 128})
styles.set_eyeliner({"r": 0, "g": 255, "b": 0, "opacity": 110})
styles.set_lips({"r": 255, "g": 0, "b": 0, "opacity": 128})