def main():
parser = argparse.ArgumentParser()
parser.add_argument('images_dir', type=str)
parser.add_argument('results_file', type=str)
args = parser.parse_args()
im_dir = Path('./numbers_letters/')
im_paths = sorted([
im_path for im_path in im_dir.iterdir()
if im_path.name.endswith('.jpeg')
])
images_dir = Path(args.images_dir)
results_file = Path(args.results_file)
images_paths = sorted([
image_path for image_path in images_dir.iterdir()
if image_path.name.endswith('.jpg')
])
results = {}
for image_path in images_paths:
image = cv2.imread(str(image_path))
if image is None:
print(f'Error loading image {image_path}')
continue
results[image_path.name] = perform_processing(image, im_paths)
with results_file.open('w') as output_file:
json.dump(results, output_file, indent=4)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('images_dir', type=str)
parser.add_argument('results_file', type=str)
args = parser.parse_args()
images_dir = Path(args.images_dir)
results_file = Path(args.results_file)
images_paths = sorted([image_path for image_path in images_dir.iterdir() if image_path.name.endswith('.jpg')])
results = {}
t1_start = perf_counter()
for image_path in images_paths:
image = cv2.imread(str(image_path))
if image is None:
print(f'Error loading image {image_path}')
continue
results[image_path.name] = perform_processing(image)
t1_stop = perf_counter()
print("Elapsed time during the whole program in seconds:",t1_stop - t1_start)
# print("liczba zdjec:", i)
with results_file.open('w') as output_file:
json.dump(results, output_file, indent=4)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('input_file', type=str)
parser.add_argument('results_file', type=str)
args = parser.parse_args()
input_file = Path(args.input_file)
results_file = Path(args.results_file)
with open(input_file) as f:
arguments = json.load(f)
start = pd.Timestamp(arguments['start']).tz_localize('UTC')
stop = pd.Timestamp(arguments['stop']).tz_localize('UTC')
df_temperature = pd.read_csv(arguments['file_temperature'],
index_col=0,
parse_dates=True)
df_target_temperature = pd.read_csv(arguments['file_target_temperature'],
index_col=0,
parse_dates=True)
df_valve = pd.read_csv(arguments['file_valve_level'],
index_col=0,
parse_dates=True)
df_combined = pd.concat([
df_temperature[df_temperature['serialNumber'] ==
arguments['serial_number']].rename(
columns={'value': 'temperature'}),
df_target_temperature.rename(columns={'value': 'target_temperature'}),
df_valve.rename(columns={'value': 'valve_level'})
])
df_combined_resampled = df_combined.resample(
pd.Timedelta(minutes=15), label='right').mean().fillna(method='ffill')
df_combined_resampled = df_combined_resampled.loc[start:stop]
df_combined_resampled['predicted_temperature'] = 0.0
df_combined_resampled['predicted_valve_level'] = 0.0
current = start - pd.DateOffset(minutes=15)
while current < stop:
predicted_temperature, predicted_valve_level = perform_processing(
df_temperature.loc[(current - pd.DateOffset(days=7)):current],
df_target_temperature.loc[(current -
pd.DateOffset(days=7)):current],
df_valve.loc[(current - pd.DateOffset(days=7)):current],
arguments['serial_number'])
current = current + pd.DateOffset(minutes=15)
df_combined_resampled.at[
current, 'predicted_temperature'] = predicted_temperature
df_combined_resampled.at[
current, 'predicted_valve_level'] = predicted_valve_level
df_combined_resampled.to_csv(results_file)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('images_dir', type=str)
parser.add_argument('results_file', type=str)
args = parser.parse_args()
images_dir = Path(args.images_dir)
results_file = Path(args.results_file)
images_paths = sorted([
image_path for image_path in images_dir.iterdir()
if image_path.name.endswith('.jpg')
])
results = {}
template = []
sign = []
#start = time.perf_counter()
templete_dir = Path("./szablon/")
template_paths = sorted([
image_path for image_path in templete_dir.iterdir()
if image_path.name.endswith('.jpg')
])
for template_path in template_paths:
temp = cv2.imread(str(template_path), 0)
l = len(str(template_path))
s = str(template_path)[l - 5:l - 4]
sign.append(s)
template.append(temp)
for image_path in images_paths:
image = cv2.imread(str(image_path))
if image is None:
print(f'Error loading image {image_path}')
continue
results[image_path.name] = perform_processing(image, template, sign)
# end = time.perf_counter()
# print("Czas: " + str(round(end-start,2)) + "s.")
with results_file.open('w') as output_file:
json.dump(results, output_file, indent=4)
