def classification_score():
"""
Test the classificarion score on the basic CNN
"""
basic = BasicCNN()
model = basic.get_model()
data_training, labels = get_dataset_classification_only(1000)
evaluation = basic.evaluate_model(np.array(data_training))
evaluation = [Image(i, np.argmax(j[0]), 0, 0, 0, 0) for i, j in enumerate(evaluation)]
labels = [Image(i, j, 0, 0, 0, 0) for i, j in enumerate(labels)]
metric = calculate_metric_classification(evaluation, labels)
return metric
def create_randomImages(self, number):
images = []
for i in range(number):
classification = random.randint(0, 2)
if classification == 0:
images.append(Image(i, 0, 0, 0, 0, 0))
elif classification == 1:
images.append(
Image(i, 1,
random.random() * 24,
random.random() * 24, 0, 0))
else:
images.append(Image(i, 1, random.random() * 24, random.random() * 24, \
random.random() * 24, random.random() * 24))
return images
def generate_img_8a(C=400):
'''Image "Węzeł z pięcioma prądami"'''
image = Image((int(C * 2), int(C * 2)), line_width=6, vector_arrow_size=30)
image.draw_point((C, C), width=20)
out_param_list = [
(0, [190, 0, 0]),
(40, [190, 0, 0]),
(320, [190, 0, 0]),
]
in_param_list = [
(140, [0, 20, 190]),
(180, [0, 20, 190]),
(220, [0, 20, 190]),
]
for rotation, arrow_color in out_param_list:
vector = Vector.from_rotation((C, C), rotation, int(C * 0.5))
vector2 = Vector.from_rotation((C, C), rotation, int(C * 0.5))
vector2.scale(1.3)
line = Line.from_vector(vector2)
image.draw_line(line)
image.draw_vector(vector, arrow_color=arrow_color)
for rotation, arrow_color in in_param_list:
vector = Vector.from_rotation((C, C), rotation, int(C * 0.5))
vector = Vector.from_reversed(vector)
vector.scale(1 - (1 / 1.3))
vector2 = Vector.from_rotation((C, C), rotation, int(C * 0.5))
vector2.scale(1.3)
line = Line.from_vector(vector2)
image.draw_line(line)
image.draw_vector(vector, arrow_color=arrow_color)
image.save('img/img_8a.png')
def main():
args = parse_args()
proxy = setup_proxy(args.proxy_url, args.proxy_auth)
if not args.QR_text == "":
args.file = "./img/QRcode.png"
Image.create_QR_image(args.QR_text, args.QR_scale)
image = Image(args.file, args.round_sensitive, args.image_brightness)
bot = Bot(image, args.fingerprint, args.start_x, args.start_y, args.mode_defensive, args.colors_ignored, args.colors_not_overwrite, args.min_range, args.max_range, proxy,
args.draw_strategy, args.xreversed, args.yreversed)
bot.init()
def run():
try:
bot.run()
except NeedUserInteraction as exception:
alert(str(exception))
try:
if raw_input(I18n.get('token_resolved')).strip() == 'y':
run()
except NameError as e:
if input(I18n.get('token_resolved')).strip() == 'y':
run()
run()
def __preview_art(self, name: str, path: str, contrast: bool,
negative: bool, sharpen: bool, emboss: bool,
grayscale: str) -> None:
"""
Qt slot for image previewing.
Args:
name: str
Image's name.
path: str
Path to image file.
contrast: bool
Image's contrast flag.
negative: bool
Image's negative flag.
sharpen: bool
Image's sharpen flag.
emboss: bool
Image's emboss flag.
grayscale: str
Image's grayscale level.
Returns:
None.
"""
self.on_preview_art(
self,
Image(name, path, contrast, negative, sharpen, emboss, grayscale))
def create_1_images(self, number):
images = []
for i in range(number):
images.append(
Image(i, 1,
random.random() * 24,
random.random() * 24, 0, 0))
return images
def get_image(self):
"""Returns the source image if we are still in still phase, otherwise returns
a rotated and scaled image."""
if self.still:
return self.image
else:
surface = pygame.transform.rotozoom(self.image.get_surface(),
self.angle, self.scale)
offset_x, offset_y = self.image.get_offset()
return Image(surface, (offset_x, offset_y))
def main():
args = parse_args()
proxy = setup_proxy(args.proxy_url, args.proxy_auth)
image = Image(args.file)
bot = Bot(image, args.fingerprint, args.start_x, args.start_y, args.mode_defensive, args.colors_ignored, proxy, args.draw_strategy)
bot.run()
def generate_img_2b(C=400):
'''Image "Linie pola ekektr wokół 2 punktowych ładunków jednoimiennych"'''
image = Image((C * 4, C * 2), line_width=3)
# left
image.draw_circle(Circle((C, C), int(C * 0.4)), fill=[190, 0, 0], width=10)
image.draw_line(Line((C, int(C + (C * 0.1))), (C, int(C - (C * 0.1)))),
fill=[190, 0, 0])
image.draw_line(Line((int(C + (C * 0.1)), C), (int(C - (C * 0.1)), C)),
fill=[190, 0, 0])
# right
image.draw_circle(Circle((3 * C, C), int(C * 0.4)),
fill=[190, 0, 0],
width=10)
image.draw_line(Line((3 * C, int(C + (C * 0.1))),
(3 * C, int(C - (C * 0.1)))),
fill=[190, 0, 0])
image.draw_line(Line((int(3 * C + (C * 0.1)), C),
(int(3 * C - (C * 0.1)), C)),
fill=[190, 0, 0])
# vertical
image.draw_line(Line((2 * C, 0), (2 * C, 2 * C)), width=1)
# vectors
image.draw_vector(
Vector((int(C * 1.5), int(C * 1.1)), (int(C * 0.25), int(C * 0.3))))
image.draw_vector(
Vector((int(C * 1.5), int(C * 0.9)), (int(C * 0.25), -int(C * 0.3))))
image.draw_vector(Vector((int(C * 1.8), int(C * 1.5)), (0, int(C * 0.4))))
image.draw_vector(Vector((int(C * 1.8), int(C * 0.5)), (0, -int(C * 0.4))))
image.draw_vector(
Vector((int(C * 2.5), int(C * 1.1)), (-int(C * 0.25), int(C * 0.3))))
image.draw_vector(
Vector((int(C * 2.5), int(C * 0.9)), (-int(C * 0.25), -int(C * 0.3))))
image.draw_vector(Vector((int(C * 2.2), int(C * 1.5)), (0, int(C * 0.4))))
image.draw_vector(Vector((int(C * 2.2), int(C * 0.5)), (0, -int(C * 0.4))))
#
image.draw_vector(
Vector((int(C * 1.4), int(C * 1.25)), (int(C * 0.20), int(C * 0.40))))
image.draw_vector(
Vector((int(C * 1.4), int(C * 0.75)), (int(C * 0.20), -int(C * 0.40))))
image.draw_vector(
Vector((int(C * 2.6), int(C * 1.25)), (-int(C * 0.20), int(C * 0.40))))
image.draw_vector(
Vector((int(C * 2.6), int(C * 0.75)),
(-int(C * 0.20), -int(C * 0.40))))
#
image.draw_vector(
Vector((int(C * 1.2), int(C * 1.4)), (int(C * 0.1), int(C * 0.5))))
image.draw_vector(
Vector((int(C * 1.2), int(C * 0.6)), (int(C * 0.1), -int(C * 0.5))))
image.draw_vector(
Vector((int(C * 2.8), int(C * 1.4)), (-int(C * 0.1), int(C * 0.5))))
image.draw_vector(
Vector((int(C * 2.8), int(C * 0.6)), (-int(C * 0.1), -int(C * 0.5))))
image.save('img/img_2b.png')
def factory():
from src.factory import ArtFactory
from src.image import Image
factory = ArtFactory()
for i in range(10):
factory += Image(
f"Lenna{i}", "",
False, False, False, False,
""
)
return factory
def generate_img_1b(C=400):
'''Image "Linie pola ekektr wokół punktowego ładunku ujemnego"'''
image = Image((C * 2, C * 2), line_width=3)
image.draw_circle(Circle((C, C), int(C * 0.4)), fill=[0, 0, 190], width=10)
image.draw_line(Line((int(C + (C * 0.1)), C), (int(C - (C * 0.1)), C)),
fill=[0, 0, 190])
for deg in range(0, 360, 360 // 12):
prevector = Vector.from_rotation((C, C), deg, int(C * 0.95))
vector = Vector.from_reversed(prevector)
vector.scale(0.45)
vector.round()
image.draw_vector(vector)
image.save('img/img_1b.png')
def generate_img_5a(C=400):
'''Image "Symbol ogólny kondensatora"'''
image = Image((int(C * 2), int(C * 2)), line_width=6)
image.draw_line(
Line((int(C * 5 / 6), int(C * 0.5)), (int(C * 5 / 6), int(C * 1.5))))
image.draw_line(
Line((int(C * 7 / 6), int(C * 0.5)), (int(C * 7 / 6), int(C * 1.5))))
image.draw_line(
Line((int(C * 5 / 6), int(C * 1)), (int(C * 1 / 4), int(C * 1))))
image.draw_line(
Line((int(C * 7 / 6), int(C * 1)), (int(C * 7 / 4), int(C * 1))))
image.draw_point((int(C * 1 / 4), int(C * 1)), width=20)
image.draw_point((int(C * 7 / 4), int(C * 1)), width=20)
image.save('img/img_5a.png')
def run(self):
while True:
try:
quote = quotes_generator.generate()[0]
quote = self.clean_quote(quote)
self.logger.info('Quote: %s' % quote)
if len(quote) > self.max_quote_length:
self.logger.warning('Quote too long!')
continue
quote_vector = self.similarity.get_vector(text=quote)
keywords = self.similarity.get_keywords(
text=quote, num=config.NUMBER_OF_KEYWORDS)
self.logger.info('Keywords: %s' % ','.join(keywords))
photos = self.unsplash.get_photos(
query=','.join(keywords),
num=config.UNSPLASH_PHOTOS_TO_ANALYSE)
self.logger.info('Unsplash found photos: %s' % len(photos))
if not photos:
self.logger.warning('No Unsplash photos found!')
continue
photo = self.get_best_photo(photos=photos,
quote_vector=quote_vector)
self.logger.info(photo)
image = Image(url=photo[1])
image.draw_text(text=quote)
image_file_path = image.save(
file_path=config.QUOTES_IMAGES_PATH)
self.reddit.post(text='<...>%s<...> %s, %s' %
(quote, '\n', photo[3]),
image_path=image_file_path)
self.logger.info('Posted to reddit!')
time.sleep(config.GENERATION_TIMEOUT)
except KeyboardInterrupt:
self.logger.info('Stoping!')
break
except:
self.logger.exception(traceback.format_exc())
time.sleep(60)
def generate_img_5b(C=400):
'''Image "Symbol kondensatora regulowanego"'''
image = Image((int(C * 2), int(C * 2)), line_width=6)
image.draw_line(
Line((int(C * 5 / 6), int(C * 0.5)), (int(C * 5 / 6), int(C * 1.5))))
image.draw_line(
Line((int(C * 7 / 6), int(C * 0.5)), (int(C * 7 / 6), int(C * 1.5))))
image.draw_line(
Line((int(C * 5 / 6), int(C * 1)), (int(C * 1 / 4), int(C * 1))))
image.draw_line(
Line((int(C * 7 / 6), int(C * 1)), (int(C * 7 / 4), int(C * 1))))
image.draw_point((int(C * 1 / 4), int(C * 1)), width=20)
image.draw_point((int(C * 7 / 4), int(C * 1)), width=20)
image.draw_vector(Vector((int(C * 0.5), int(C * 1.5)),
(int(C * 1), -int(C * 1))),
width=8)
image.save('img/img_5b.png')
def generate_img_2a(C=400):
'''Image "Linie pola ekektr wokół 2 punktowych ładunków różnoimiennych"'''
image = Image((C * 4, C * 2), line_width=3)
# pos
image.draw_circle(Circle((C, C), int(C * 0.4)), fill=[190, 0, 0], width=10)
image.draw_line(Line((C, int(C + (C * 0.1))), (C, int(C - (C * 0.1)))),
fill=[190, 0, 0])
image.draw_line(Line((int(C + (C * 0.1)), C), (int(C - (C * 0.1)), C)),
fill=[190, 0, 0])
# neg
image.draw_circle(Circle((3 * C, C), int(C * 0.4)),
fill=[0, 0, 190],
width=10)
image.draw_line(Line((int(3 * C + (C * 0.1)), C),
(int(3 * C - (C * 0.1)), C)),
fill=[0, 0, 190])
# straight vectors
image.draw_vector(Vector((int(C * 1.5), C), (int(C * 0.4), 0)))
image.draw_vector(Vector((int(C * 1.5), int(C * 1.2)), (int(C * 0.4), 0)))
image.draw_vector(Vector((int(C * 1.5), int(C * 0.8)), (int(C * 0.4), 0)))
image.draw_vector(Vector((int(C * 2.1), C), (int(C * 0.4), 0)))
image.draw_vector(Vector((int(C * 2.1), int(C * 1.2)), (int(C * 0.4), 0)))
image.draw_vector(Vector((int(C * 2.1), int(C * 0.8)), (int(C * 0.4), 0)))
# slightly curved vectors
image.draw_vector(
Vector((int(C * 1.5), int(C * 1.4)), (int(C * 0.4), int(C * 0.05))))
image.draw_vector(
Vector((int(C * 2.1), int(C * 1.45)), (int(C * 0.4), int(C * -0.05))))
image.draw_vector(
Vector((int(C * 1.5), int(C * 0.6)), (int(C * 0.4), int(C * -0.05))))
image.draw_vector(
Vector((int(C * 2.1), int(C * 0.55)), (int(C * 0.4), int(C * 0.05))))
# curved 3-part vectors
image.draw_vector(
Vector((int(C * 1.1), int(C * 1.5)), (int(C * 0.4), int(C * 0.1))))
image.draw_vector(Vector((int(C * 1.8), int(C * 1.65)), (int(C * 0.4), 0)))
image.draw_vector(
Vector((int(C * 2.5), int(C * 1.6)), (int(C * 0.4), int(C * -0.1))))
image.draw_vector(
Vector((int(C * 1.1), int(C * 0.5)), (int(C * 0.4), int(C * -0.1))))
image.draw_vector(Vector((int(C * 1.8), int(C * 0.35)), (int(C * 0.4), 0)))
image.draw_vector(
Vector((int(C * 2.5), int(C * 0.4)), (int(C * 0.4), int(C * 0.1))))
image.save('img/img_2a.png')
def get_csv_training():
"""
Get the annotations for
all images contained in a particular
csv file
:return: list of all the annotations (id, c ???, x coordinate of the
first spot, y coordinate of the first sport and then the coordinates
of the second spot).
"""
list_images = []
with open('DataChallenge/descriptions_training.csv', 'r') as csvfile:
spamreader = csv.reader(csvfile, delimiter=' ', quotechar='|')
for row in spamreader:
row = row[0].split(",")
_id, c, xf, yf, xs, ys = row
i = Image(_id, c, xf, yf, xs, ys)
list_images.append(i)
return list_images
def main():
args = parse_args()
proxy = setup_proxy(args.proxy_url, args.proxy_auth)
image = Image(args.file, args.round_sensitive, args.image_brightness)
bot = Bot(image, args.fingerprint, args.start_x, args.start_y, args.mode_defensive, args.colors_ignored, proxy, args.draw_strategy)
bot.init()
def run():
try:
bot.run()
except NeedUserInteraction as exception:
alert(exception.message)
if raw_input(I18n.get('token_resolved')) == 'y':
run()
run()
def generate_img_5c(C=400):
'''Image "Symbol kondensatora elektrolitycznego"'''
image = Image((int(C * 2), int(C * 2)), line_width=6)
image.draw_line(
Line((int(C * 5 / 6), int(C * 0.5)), (int(C * 5 / 6), int(C * 1.5))))
image.draw_line(
Line((int(C * 7 / 6), int(C * 0.5)), (int(C * 7 / 6), int(C * 1.5))))
image.draw_line(
Line((int(C * 8 / 6), int(C * 0.5)), (int(C * 8 / 6), int(C * 1.5))))
image.draw_line(
Line((int(C * 7 / 6), int(C * 0.5)), (int(C * 8 / 6), int(C * 0.5))))
image.draw_line(
Line((int(C * 7 / 6), int(C * 1.5)), (int(C * 8 / 6), int(C * 1.5))))
image.draw_line(
Line((int(C * 5 / 6), int(C * 1)), (int(C * 1 / 4), int(C * 1))))
image.draw_line(
Line((int(C * 8 / 6), int(C * 1)), (int(C * 7 / 4), int(C * 1))))
image.draw_point((int(C * 1 / 4), int(C * 1)), width=20, fill=[0, 0, 190])
image.draw_point((int(C * 7 / 4), int(C * 1)), width=20, fill=[190, 0, 0])
image.save('img/img_5c.png')
def render_isometric(
self,
scene: Base,
origin: Vector3 = (0, 0, -5),
direction: Vector3 = (0, 0, 1),
width: int = 6,
height: int = 6,
):
origin = Vec3(origin)
direction = Vec3(direction)
raymarcher = Raymarcher(scene)
image = Image(width, height)
raymarcher.render(image, lambda p: (p + origin, direction), aa=4)
file = StringIO()
image.dump_bw(file=file, black=".", white="#", threshold=.3)
file.seek(0)
#image.dump()
return file.read()
def generate_img_3a(C=400):
'''Image "Pole elektryczne pomiędzy odładami kondensatora płaskiego"'''
image = Image((C * 2, C * 2), line_width=3)
image.draw_line(Line((int(C*2/3), int(C*0.5)),(int(C*2/3), int(C*1.5))),\
fill=[190,0,0], width=10)
image.draw_line(Line((int(C * 2 / 3), C), (int(C * 1 / 3), C)),
fill=[190, 0, 0],
width=3)
image.draw_point((int(C * 1 / 3), int(C)), fill=[190, 0, 0], width=10)
image.draw_line(Line((int(C*4/3), int(C*0.5)),(int(C*4/3), int(C*1.5))),\
fill=[0,0,190], width=10)
image.draw_line(Line((int(C * 4 / 3), C), (int(C * 5 / 3), C)),
fill=[0, 0, 190],
width=3)
image.draw_point((int(C * 5 / 3), int(C)), fill=[0, 0, 190], width=10)
len = 2 / 3 - 2 * (0.75 - 2 / 3)
image.draw_vector(Vector((int(C * 0.75), int(C * 0.6)), (int(C * len), 0)))
image.draw_vector(Vector((int(C * 0.75), int(C * 0.8)), (int(C * len), 0)))
image.draw_vector(Vector((int(C * 0.75), int(C * 1)), (int(C * len), 0)))
image.draw_vector(Vector((int(C * 0.75), int(C * 1.2)), (int(C * len), 0)))
image.draw_vector(Vector((int(C * 0.75), int(C * 1.4)), (int(C * len), 0)))
image.save('img/img_3a.png')
def parse_current_images(current_image_points, index):
if not current_image_points:
image_out = Image(index - 1, 0, 0, 0, 0, 0)
elif len(current_image_points) == 1:
if is_spot_possible(current_image_points[0][1]/float(RESIZE_FACTOR), \
current_image_points[0][2]/float(RESIZE_FACTOR), \
current_image_points[0][3]/float(RESIZE_FACTOR), \
current_image_points[0][4]/float(RESIZE_FACTOR)):
spot = ((current_image_points[0][1] + current_image_points[0][2])/float(2 * RESIZE_FACTOR), \
(current_image_points[0][3] + current_image_points[0][4])/float(2 * RESIZE_FACTOR))
image_out = Image(index - 1, 1, spot[0], spot[1], 0, 0)
else:
image_out = Image(index - 1, 0, 0, 0, 0, 0)
else:
# Reverse sort the mostr likely images
current_image_points.sort(key=lambda x: -x[0])
spots = []
if is_spot_possible(current_image_points[0][1]/float(RESIZE_FACTOR), \
current_image_points[0][2]/float(RESIZE_FACTOR), \
current_image_points[0][3]/float(RESIZE_FACTOR), \
current_image_points[0][4]/float(RESIZE_FACTOR)):
spot1 = ((current_image_points[0][1] + current_image_points[0][2])/float(2 * RESIZE_FACTOR), \
(current_image_points[0][3] + current_image_points[0][4])/float(2 * RESIZE_FACTOR))
spots.append(spot1)
if is_spot_possible(current_image_points[1][1]/float(RESIZE_FACTOR), \
current_image_points[1][2]/float(RESIZE_FACTOR), \
current_image_points[1][3]/float(RESIZE_FACTOR), \
current_image_points[1][4]/float(RESIZE_FACTOR)):
spot2 = ((current_image_points[1][1] + current_image_points[1][2])/float(2 * RESIZE_FACTOR), \
(current_image_points[1][3] + current_image_points[1][4])/float(2 * RESIZE_FACTOR))
spots.append(spot2)
if len(spots) == 0:
image_out = Image(index - 1, 0, 0, 0, 0, 0)
elif len(spots) == 1:
image_out = Image(index - 1, 1, spots[0][0], spots[0][1], 0, 0)
else:
image_out = Image(index - 1, 2, spot1[0], spot1[1], spot2[0],
spot2[1])
return image_out
from src.image import Image
import sys
if __name__ == '__main__':
if len(sys.argv) != 2:
print('Argument error')
img = Image(sys.argv[1])
size = len(img.data) // 2
img.data = img.data[:size, :size]
img.save('img/penguin128.png')
def create_2_images(self, number):
images = []
for i in range(number):
images.append(Image(i, 2, random.random() * 24, random.random() * 24, \
random.random() * 24, random.random() * 24))
return images
def lenna():
from src.image import Image
return Image("Lenna", relative_path("tests/data/lenna.png"), False, False,
False, False, consts.uiConsts["DefaultGrayscaleLevel"])
def create_0_images(self, number):
images = []
for i in range(number):
images.append(Image(i, 0, 0, 0, 0, 0))
return images
def __init__(self, emotion, pixels, partition):
self.emotion = emotion
self.image = Image(pixels)
self.partition = partition
def import_key_points(self):
self.first_img = Image(self.first_img_name)
self.first_img.import_key_points()
self.second_img = Image(self.second_img_name)
self.second_img.import_key_points()
