def on_status(self, status):
text = get_status_text(status, self.screen_name)
if text is None:
logging.info("Status %d is not relevant", status.id)
return
fp = io.BytesIO()
generate_image(text, fp)
self.api.update_with_media(
"filename.png",
file=fp,
in_reply_to_status_id=status.id,
auto_populate_reply_metadata=True,
)
def shengcheng_image():
strr = request.values.get('message')
print strr
url = generate_image(strr)
return json.dumps({
"code": 0,
"images": url,
})
def find_points(width, height, min_x, max_x, min_y, max_y):
"""
Calls the calculate() function on each point.
Paramters:
width, height: dimensions of the image/complex plane
min_x, min_y: the minimum x and yi values for the plane
max_x, max_y: the maximum x and yi values for the plane
"""
num = 0 # TODO: Fix arduino job status indicator
print("True total points: ",
width * height) # TODO: Remove unnecessary prints
results_txt = open('results.txt', 'w+')
write_serial(b'1')
totalJobs = width * height
write_serial(bytes(str(totalJobs), encoding="utf-8"))
x_step = (max_x - min_x) / width
y_step = (max_y - min_y) / height
current_point = [min_x, min_y]
x_point = 1
y_point = 1
while y_point <= height: # TODO: Fix precision issues (Perturbation or store in array)
row = []
while x_point <= width:
point_result = calculate(*current_point)
row.append(f"{point_result},{current_point[0]},{current_point[1]}")
current_point[0] += x_step
x_point += 1
num += 1
# TODO: Rewrite arduino progress indicator
for point in row:
results_txt.write(f"{point}\n")
current_point[1] += y_step
current_point[0] = min_x
y_point += 1
x_point = 1
print("Total calculated points: ", num)
results_txt.close()
write_serial(b'9')
write_serial(b'x')
generate.generate_image(int(width), int(height))
def generate_all_images():
global IMAGES
last_progress = time.time()
print("Generating")
words = [x.strip() for x in open("frees.txt", "r").readlines()]
for i, word in enumerate(words):
if time.time() - last_progress > 5:
last_progress = time.time()
print(i, "/", len(words))
png_data = generate.image_to_png(generate.generate_image(word))
IMAGES.append(png_data)
print("Generated all images")
#coding:utf-8
from generate import generate_image
a = generate_image(100)
a.check_font()
a.generate()
a.reset_config(text_mode='chinese',
picnum=100,
is_sample_from_corpus=False,
dict_path='dic_num.txt')
a.generate()
#a=generate_image(0)
#a.check_font()
# divide by 2 or else loss is weighted 2x towards disc vs gen
disc_loss = (real_loss + fake_loss) / 2
total_disc_loss += disc_loss.data
optimizer_d.zero_grad()
disc_loss.backward()
optimizer_d.step()
progress_bar.set_description(
f"epoch: {epoch} || disc loss: {total_disc_loss/batch_num} || gen loss: {total_gen_loss/batch_num}"
)
denom = opts.numImages // opts.batchSize
avg_gen_loss = total_gen_loss / denom
avg_disc_loss = total_disc_loss / denom
losses[0].append(avg_gen_loss)
losses[1].append(avg_disc_loss)
torch.save(gen.state_dict(), './generator.pt')
torch.save(disc.state_dict(), './discriminator.pt')
generate_image(fixed_noise, epoch=epoch)
# display loss graph
plt.plot(losses[0], label="gen")
plt.plot(losses[1], label="disc")
plt.xlabel('epochs')
plt.ylabel('loss')
plt.legend(loc='upper left')
plt.show()