Today, I worked with art artificial intelligence, to create tool for my game development.
I used python and PyQt6 and this tool help me to remove border, resize, split, rename and save images as PNG file type for Godot game engine.



Is a blog about python programming language. You can see my work with python programming language, tutorials and news.
import subprocess
import sys
import os
import shutil
import importlib.util
import re
import concurrent.futures
from typing import List, Tuple, Set
class ModuleManager:
def __init__(self):
self.modules: Set[str] = set()
self.pip_path = self._get_pip_path()
def _get_pip_path(self) -> str:
possible_path = os.path.join(sys.exec_prefix, "Scripts", "pip.exe")
return shutil.which("pip") or (possible_path if os.path.exists(possible_path) else None)
def extract_imports_from_file(self, file_path: str) -> List[Tuple[str, str]]:
imports = []
try:
with open(file_path, 'r', encoding='utf-8') as file:
for line in file:
# Detect 'import module'
import_match = re.match(r'^\s*import\s+([a-zA-Z0-9_]+)(\s+as\s+.*)?$', line)
if import_match:
module = import_match.group(1)
imports.append((module, line.strip()))
continue
# Detect 'from module import ...'
from_match = re.match(r'^\s*from\s+([a-zA-Z0-9_]+)\s+import\s+.*$', line)
if from_match:
module = from_match.group(1)
imports.append((module, line.strip()))
except FileNotFoundError:
print(f"❌ Fișierul {file_path} nu a fost găsit.")
except Exception as e:
print(f"❌ Eroare la citirea fișierului {file_path}: {e}")
return imports
def scan_directory_for_py_files(self, directory: str = '.') -> List[str]:
py_files = []
for root, _, files in os.walk(directory):
for file in files:
if file.endswith('.py'):
py_files.append(os.path.join(root, file))
return py_files
def collect_unique_modules(self, directory: str = '.') -> None:
py_files = self.scan_directory_for_py_files(directory)
all_imports = []
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_file = {executor.submit(self.extract_imports_from_file, file_path): file_path for file_path in py_files}
for future in concurrent.futures.as_completed(future_to_file):
imports = future.result()
all_imports.extend(imports)
for module, _ in all_imports:
self.modules.add(module)
def is_module_installed(self, module: str) -> bool:
return importlib.util.find_spec(module) is not None
def run_pip_install(self, module: str) -> bool:
if not self.pip_path:
print(f"❌ Nu am găsit pip pentru {module}.")
return False
try:
subprocess.check_call([self.pip_path, "install", module])
print(f"✅ Pachetul {module} a fost instalat cu succes.")
return True
except subprocess.CalledProcessError as e:
print(f"❌ Eroare la instalarea pachetului {module}: {e}")
return False
def check_and_install_modules(self) -> None:
def process_module(module):
print(f"\n🔎 Verific dacă {module} este instalat...")
if self.is_module_installed(module):
print(f"✅ {module} este deja instalat.")
else:
print(f"📦 Instalez {module}...")
self.run_pip_install(module)
# Re-verifică după instalare
if self.is_module_installed(module):
print(f"✅ {module} funcționează acum.")
else:
print(f"❌ {module} nu funcționează după instalare.")
with concurrent.futures.ThreadPoolExecutor() as executor:
executor.map(process_module, self.modules)
def main():
print("🔍 Verific pip...")
manager = ModuleManager()
if manager.pip_path:
print(f"✅ Pip este disponibil la: {manager.pip_path}")
else:
print("⚠️ Pip nu este disponibil.")
return
directory = sys.argv[1] if len(sys.argv) > 1 else '.'
print(f"\n📜 Scanez directorul {directory} pentru fișiere .py...")
manager.collect_unique_modules(directory)
if not manager.modules:
print("⚠️ Nu s-au găsit module în importuri.")
return
print(f"\nModule unice detectate: {', '.join(manager.modules)}")
manager.check_and_install_modules()
if __name__ == "__main__":
main()
python catafest_build_package_001.py
🔍 Verificare module standard...
[✓] Modul standard 'json' este disponibil.
[✓] Modul standard 'subprocess' este disponibil.
[✓] Modul standard 'platform' este disponibil.
[✓] Modul standard 'datetime' este disponibil.
[✓] Modul standard 'os' este disponibil.
[✓] Modul standard 'sys' este disponibil.
📦 Verificare și instalare module pip...
[✓] Modulul 'PyQt6' este deja instalat.
[✓] Modulul 'build' este deja instalat.
* Creating isolated environment: venv+pip...
* Installing packages in isolated environment:
- setuptools
- wheel
...
import torch
import torch.nn as nn
import numpy as np
data = np.array([
[1800.5, 1810.0, 1795.0, 1000, 1805.2],
[1805.2, 1815.0, 1800.0, 1200, 1812.8],
[1812.8, 1820.0, 1808.0, 1100, 1810.5],
[1810.5, 1818.0, 1805.0, 1300, 1825.0],
[1825.0, 1830.0, 1815.0, 1400, 1820.3],
[1820.3, 1828.0, 1810.0, 1250, 1835.7]
])
X, y = torch.tensor(data[:, :4], dtype=torch.float32), torch.tensor(data[:, 4], dtype=torch.float32)
model = nn.Sequential(nn.Linear(4, 6), nn.ReLU(), nn.Linear(6, 4), nn.ReLU(), nn.Linear(4, 1))
optimizer = torch.optim.Adam(model.parameters())
loss_fn = nn.MSELoss()
for _ in range(3000):
optimizer.zero_grad()
y_pred = model(X).squeeze()
loss = loss_fn(y_pred, y)
loss.backward()
optimizer.step()
prediction = model(torch.tensor([[1830.0, 1840.0, 1825.0, 1150]], dtype=torch.float32))
print("Predicted XAU/USD closing price:", round(prediction.item(), 2))
python torch_001.py
Predicted XAU/USD closing price: 1819.57
import tensorflow as tf
import numpy as np
data = np.array([
[1800.5, 1810.0, 1795.0, 1000, 1805.2],
[1805.2, 1815.0, 1800.0, 1200, 1812.8],
[1812.8, 1820.0, 1808.0, 1100, 1810.5],
[1810.5, 1818.0, 1805.0, 1300, 1825.0],
[1825.0, 1830.0, 1815.0, 1400, 1820.3],
[1820.3, 1828.0, 1810.0, 1250, 1835.7]
])
X, y = data[:, :4], data[:, 4]
model = tf.keras.Sequential([
tf.keras.layers.Dense(6, activation='relu', input_shape=(4,)),
tf.keras.layers.Dense(4, activation='relu'),
tf.keras.layers.Dense(1)
])
model.compile(optimizer='adam', loss='mse')
model.fit(X, y, epochs=3000, verbose=0)
prediction = model.predict(np.array([[1830.0, 1840.0, 1825.0, 1150]]))
print("Predicted XAU/USD closing price:", round(prediction[0][0], 2))
python tf_001.py
2025-08-30 21:11:13.966066: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
C:\Python313\Lib\site-packages\google\protobuf\runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/attr_value.proto. Please update the gencode to avoid compatibility violations in the next runtime release.
...
Predicted XAU/USD closing price: 2.9
from sklearn.neural_network import MLPRegressor
import numpy as np
data = np.array([
[1800.5, 1810.0, 1795.0, 1000, 1805.2],
[1805.2, 1815.0, 1800.0, 1200, 1812.8],
[1812.8, 1820.0, 1808.0, 1100, 1810.5],
[1810.5, 1818.0, 1805.0, 1300, 1825.0],
[1825.0, 1830.0, 1815.0, 1400, 1820.3],
[1820.3, 1828.0, 1810.0, 1250, 1835.7]
])
X, y = data[:, :4], data[:, 4]
model = MLPRegressor(hidden_layer_sizes=(6, 4), max_iter=3000)
model.fit(X, y)
prediction = model.predict([[1830.0, 1840.0, 1825.0, 1150]])
print("Predicted XAU/USD closing price:", round(prediction[0], 2))
import os
import subprocess
from PyQt6.QtWidgets import (
QApplication, QWidget, QVBoxLayout, QPushButton,
QListWidget, QMessageBox
)
# fisier download : yt-dlp.exe -vU https://www.youtube.com/watch?v=xxxxxx -f bestvideo*+bestaudio/best
FOLDER_PATH = r"D:\Software"
class FFmpegMerger(QWidget):
def __init__(self):
super().__init__()
self.setWindowTitle("Combinare Video + Audio cu FFmpeg")
self.resize(600, 400)
self.layout = QVBoxLayout()
self.file_list = QListWidget()
self.process_button = QPushButton("Prelucrează în MP4")
self.layout.addWidget(self.file_list)
self.layout.addWidget(self.process_button)
self.setLayout(self.layout)
self.process_button.clicked.connect(self.process_files)
self.populate_file_list()
def populate_file_list(self):
files = os.listdir(FOLDER_PATH)
video_files = [f for f in files if f.endswith(".f401.mp4")]
audio_files = [f for f in files if f.endswith(".f251-9.webm")]
base_names = set(f.split(".f401.mp4")[0] for f in video_files)
candidates = []
for base in base_names:
audio_name = f"{base}.f251-9.webm"
output_name = f"{base}.mp4"
if audio_name in audio_files and output_name not in files:
candidates.append(base)
for name in candidates:
self.file_list.addItem(name)
def process_files(self):
for i in range(self.file_list.count()):
base = self.file_list.item(i).text()
video_path = os.path.join(FOLDER_PATH, f"{base}.f401.mp4")
audio_path = os.path.join(FOLDER_PATH, f"{base}.f251-9.webm")
output_path = os.path.join(FOLDER_PATH, f"{base}.mp4")
cmd = [
"ffmpeg",
"-i", video_path,
"-i", audio_path,
"-c:v", "copy",
"-c:a", "aac",
"-strict", "experimental",
output_path
]
try:
subprocess.run(cmd, check=True)
except subprocess.CalledProcessError as e:
QMessageBox.critical(self, "Eroare", f"Eroare la procesarea {base}: {e}")
return
QMessageBox.information(self, "Succes", "Toate fișierele au fost prelucrate cu succes!")
if __name__ == "__main__":
app = QApplication([])
window = FFmpegMerger()
window.show()
app.exec()
pip install geoai-py
Successfully installed Flask-Caching-2.3.1 MarkupSafe-3.0.2 PySocks-1.7.1 PyYAML-6.0.2 absl-py-2.3.1 aenum-3.1.16 affine-2.4.0 aiohappyeyeballs-2.6.1 aiohttp-3.12.14 aiosignal-1.4.0 albucore-0.0.24 albumentations-2.0.8 aniso8601-10.0.1 annotated-types-0.7.0 antlr4-python3-runtime-4.9.3 anyio-4.9.0 anywidget-0.9.18 argon2-cffi-25.1.0 argon2-cffi-bindings-21.2.0 arrow-1.3.0 asttokens-3.0.0 beautifulsoup4-4.13.4 bitsandbytes-0.46.1 bleach-6.2.0 blinker-1.9.0 bqplot-0.12.45 branca-0.8.1 buildingregulariser-0.2.2 cachelib-0.13.0 cachetools-6.1.0 cffi-1.17.1 click-8.2.1 click-plugins-1.1.1.2 cligj-0.7.2 color-operations-0.2.0 comm-0.2.2 contextily-1.6.2 contourpy-1.3.2 cycler-0.12.1 datasets-4.0.0 decorator-5.2.1 defusedxml-0.7.1 dill-0.3.8 docstring-parser-0.17.0 duckdb-1.3.2 einops-0.8.1 eval-type-backport-0.2.2 ever-beta-0.5.1 executing-2.2.0 fastjsonschema-2.21.1 filelock-3.18.0 fiona-1.10.1 flask-3.1.1 flask-cors-6.0.1 flask-restx-1.3.0 folium-0.20.0 fonttools-4.59.0 fqdn-1.5.1 frozenlist-1.7.0 fsspec-2025.3.0 gdown-5.2.0 geoai-py-0.9.0 geographiclib-2.0 geojson-3.2.0 geopandas-1.1.1 geopy-2.4.1 gitdb-4.0.12 gitpython-3.1.44 grpcio-1.73.1 h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 huggingface_hub-0.33.4 hydra-core-1.3.2 importlib-resources-6.5.2 ipyevents-2.0.2 ipyfilechooser-0.6.0 ipyleaflet-0.20.0 ipython-9.4.0 ipython-pygments-lexers-1.1.1 ipytree-0.2.2 ipyvue-1.11.2 ipyvuetify-1.11.3 ipywidgets-8.1.7 isoduration-20.11.0 itsdangerous-2.2.0 jedi-0.19.2 jinja2-3.1.6 joblib-1.5.1 jsonargparse-4.40.0 jsonnet-0.21.0 jsonpointer-3.0.0 jupyter-client-8.6.3 jupyter-core-5.8.1 jupyter-events-0.12.0 jupyter-leaflet-0.20.0 jupyter-server-2.16.0 jupyter-server-proxy-4.4.0 jupyter-server-terminals-0.5.3 jupyterlab-pygments-0.3.0 jupyterlab_widgets-3.0.15 kiwisolver-1.4.8 kornia-0.8.1 kornia_rs-0.1.9 leafmap-0.48.6 lightly-1.5.21 lightly_utils-0.0.2 lightning-2.5.2 lightning-utilities-0.14.3 localtileserver-0.10.6 mapclassify-2.10.0 maplibre-0.3.4 markdown-3.8.2 markdown-it-py-3.0.0 matplotlib-3.10.3 matplotlib-inline-0.1.7 mdurl-0.1.2 mercantile-1.2.1 mistune-3.1.3 morecantile-6.2.0 multidict-6.6.3 multiprocess-0.70.16 narwhals-1.48.0 nbclient-0.10.2 nbconvert-7.16.6 nbformat-5.10.4 numexpr-2.11.0 omegaconf-2.3.0 opencv-python-headless-4.12.0.88 overrides-7.7.0 overturemaps-0.15.0 pandas-2.3.1 pandocfilters-1.5.1 parso-0.8.4 planetary-computer-1.0.0 plotly-6.2.0 prettytable-3.16.0 prometheus-client-0.22.1 prompt_toolkit-3.0.51 propcache-0.3.2 psygnal-0.14.0 pure-eval-0.2.3 pyarrow-21.0.0 pycparser-2.22 pydantic-2.11.7 pydantic-core-2.33.2 pygments-2.19.2 pyogrio-0.11.0 pyparsing-3.2.3 pyproj-3.7.1 pystac-1.13.0 pystac-client-0.9.0 python-box-7.3.2 python-dateutil-2.9.0.post0 python-dotenv-1.1.1 python-json-logger-3.3.0 pytorch_lightning-2.5.2 pytz-2025.2 pywin32-311 pywinpty-2.0.15 pyzmq-27.0.0 rasterio-1.4.3 regex-2024.11.6 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 rich-14.0.0 rio-cogeo-5.4.2 rio-tiler-7.8.1 rioxarray-0.19.0 rtree-1.4.0 safetensors-0.5.3 scikit-learn-1.7.1 scooby-0.10.1 segmentation-models-pytorch-0.5.0 send2trash-1.8.3 sentry-sdk-2.33.0 server-thread-0.3.0 shapely-2.1.1 simpervisor-1.0.0 simsimd-6.5.0 six-1.17.0 smmap-5.0.2 sniffio-1.3.1 soupsieve-2.7 stack_data-0.6.3 stringzilla-3.12.5 tensorboard-2.20.0 tensorboard-data-server-0.7.2 tensorboardX-2.6.4 terminado-0.18.1 threadpoolctl-3.6.0 timm-1.0.17 tinycss2-1.4.0 tokenizers-0.21.2 torch-2.7.1 torchange-0.0.1 torchgeo-0.7.1 torchinfo-1.8.0 torchmetrics-1.7.4 torchvision-0.22.1 tornado-6.5.1 traitlets-5.14.3 traittypes-0.2.1 transformers-4.53.2 types-python-dateutil-2.9.0.20250708 typeshed-client-2.8.2 typing-inspection-0.4.1 tzdata-2025.2 uri-template-1.3.0 uvicorn-0.35.0 wandb-0.21.0 wcwidth-0.2.13 webcolors-24.11.1 webencodings-0.5.1 websocket-client-1.8.0 werkzeug-3.1.3 whitebox-2.3.6 whiteboxgui-2.3.0 widgetsnbextension-4.0.14 xarray-2025.7.1 xxhash-3.5.0 xyzservices-2025.4.0 yarl-1.20.1
>>> import geoai
>>> dir(geoai)
['AgricultureFieldDelineator', 'Any', 'AutoConfig', 'AutoModelForMaskGeneration',
'AutoModelForMaskedImageModeling', 'AutoProcessor', 'BoundingBox', 'BuildingFootprintExtractor',
'CLIPSegForImageSegmentation', 'CLIPSegProcessor', 'CLIPSegmentation', 'CarDetector',
'ChangeDetection', 'CustomDataset', 'DetectionResult', 'Dict', 'ET', 'GroundedSAM', 'Image',
'Iterable', 'List', 'Map', 'MapLibre', 'MultiPolygon', 'NonGeoDataset', 'ObjectDetector',
'Optional', 'OrderedDict', 'ParkingSplotDetector', 'Path', 'Polygon', 'RandomRotation',
'ShipDetector', 'SolarPanelDetector', 'Tuple', 'Union', 'Window', '__author__',
'__builtins__', '__cached__', '__doc__', '__email__', '__file__', '__loader__', '__name__',
'__package__', '__path__', '__spec__', '__version__', 'adaptive_regularization',
'add_geometric_properties', 'analyze_vector_attributes', 'batch_vector_to_raster', 'bbox_to_xy',
'box', 'boxes_to_vector', 'calc_stats', 'change_detection', 'classify', 'classify_image',
'classify_images', 'clip_raster_by_bbox', 'coords_to_xy', 'create_overview_image',
'create_split_map', 'create_vector_data', 'csv', 'cv2', 'dataclass', 'deeplabv3_resnet50',
'dict_to_image', 'dict_to_rioxarray', 'download', 'download_file', 'download_model_from_hf',
'download_naip', 'download_overture_buildings', 'download_pc_stac_item', 'edit_vector_data',
'export_geotiff_tiles', 'export_geotiff_tiles_batch', 'export_tiles_to_geojson',
'export_training_data', 'extract', 'extract_building_stats', 'fasterrcnn_resnet50_fpn_v2',
'fcn_resnet50', 'features', 'geoai', 'geojson_to_coords', 'geojson_to_xy', 'get_device',
'get_instance_segmentation_model', 'get_model_config', 'get_model_input_channels',
'get_overture_data', 'get_raster_info', 'get_raster_info_gdal', 'get_raster_resolution',
'get_raster_stats', 'get_vector_info', 'get_vector_info_ogr', 'glob', 'gpd', 'hf',
'hf_hub_download', 'hybrid_regularization', 'image_segmentation', 'inspect_pth_file',
'install_package', 'instance_segmentation', 'instance_segmentation_batch',
'instance_segmentation_inference_on_geotiff', 'json', 'leafmap', 'logging', 'maplibregl',
'mapping', 'mask_generation', 'maskrcnn_resnet50_fpn', 'masks_to_vector', 'math',
'mosaic_geotiffs', 'ndimage', 'np', 'object_detection', 'object_detection_batch', 'orthogonalize',
'os', 'pc_collection_list', 'pc_item_asset_list', 'pc_stac_download', 'pc_stac_search', 'pd',
'pipeline', 'plot_batch', 'plot_images', 'plot_masks', 'plot_performance_metrics',
'plot_prediction_comparison', 'plt', 'print_raster_info', 'print_vector_info', 'raster_to_vector',
'raster_to_vector_batch', 'rasterio', 'read_pc_item_asset', 'read_raster', 'read_vector',
'region_groups', 'regularization', 'regularize', 'requests', 'rotate', 'rowcol_to_xy', 'rxr',
'segment', 'semantic_segmentation', 'semantic_segmentation_batch', 'set_proj_lib_path', 'shape',
'show', 'stack_bands', 'subprocess', 'sys', 'temp_file_path', 'time', 'torch', 'torchgeo', 'tqdm',
'train', 'train_MaskRCNN_model', 'train_classifier', 'train_instance_segmentation_model',
'train_segmentation_model', 'transform_bounds', 'try_common_architectures', 'utils',
'vector_to_geojson', 'vector_to_raster', 'view_image', 'view_pc_item', 'view_pc_items',
'view_raster', 'view_vector', 'view_vector_interactive', 'visualize_vector_by_attribute',
'warnings', 'write_colormap', 'xr']
# Generate star polygon vertices
points_cw = []
points_ccw = []
for i in range(steps):
t = 2 * math.pi * i / steps
r = outer_radius if i % 2 == 0 else inner_radius
x_cw = center[0] + r * math.cos(t)
y_cw = center[1] + r * math.sin(t)
x_ccw = center[0] + r * math.cos(-t + math.pi / max(lobes, 1))
y_ccw = center[1] + r * math.sin(-t + math.pi / max(lobes, 1))
points_cw.append((x_cw, y_cw))
points_ccw.append((x_ccw, y_ccw))
import os
import unittest
from PIL import Image, ImageDraw, ImageFont, ImageQt
import shutil
from PyQt6.QtWidgets import QApplication, QMainWindow, QPushButton, QFileDialog, QLabel, QVBoxLayout, QWidget, QComboBox
from PyQt6.QtGui import QPixmap
from PyQt6.QtCore import Qt
import sys
def create_test_image(path, size, number):
img = Image.new('RGBA', size, (255, 255, 255, 255))
draw = ImageDraw.Draw(img)
# Simplified to just draw the number with a basic color background
draw.rectangle((0, 0, size[0], size[1]), fill=(0, 100 * number % 255, 0, 255))
try:
font = ImageFont.load_default()
except:
font = None
draw.text((size[0]//2-5, size[1]//2-5), str(number), fill=(255, 255, 255, 255), font=font)
img.save(path, 'PNG')
def merge_sprites(folder_path, output_horizontal, output_vertical):
images = [Image.open(os.path.join(folder_path, f)) for f in os.listdir(folder_path) if f.endswith(('.png', '.jpg', '.jpeg'))]
if not images:
return None, None
width, height = images[0].size
# Horizontal merge
total_width = width * len(images)
horizontal_image = Image.new('RGBA', (total_width, height))
for i, img in enumerate(images):
horizontal_image.paste(img, (i * width, 0))
horizontal_image.save(output_horizontal, 'PNG')
# Vertical merge
total_height = height * len(images)
vertical_image = Image.new('RGBA', (width, total_height))
for i, img in enumerate(images):
vertical_image.paste(img, (0, i * height))
vertical_image.save(output_vertical, 'PNG')
return horizontal_image, vertical_image
class TestSpriteMerger(unittest.TestCase):
def setUp(self):
self.test_folder = 'test_images'
self.size = (50, 20)
os.makedirs(self.test_folder, exist_ok=True)
for i in range(3):
create_test_image(os.path.join(self.test_folder, f'test_{i+1}.png'), self.size, i+1)
def test_merge_horizontal(self):
output_h = 'test_merged_horizontal.png'
output_v = 'test_merged_vertical.png'
h_img, _ = merge_sprites(self.test_folder, output_h, output_v)
self.assertIsNotNone(h_img, "Horizontal merge failed")
self.assertEqual(h_img.size, (self.size[0] * 3, self.size[1]))
def test_merge_vertical(self):
output_h = 'test_merged_horizontal.png'
output_v = 'test_merged_vertical.png'
_, v_img = merge_sprites(self.test_folder, output_h, output_v)
self.assertIsNotNone(v_img, "Vertical merge failed")
self.assertEqual(v_img.size, (self.size[0], self.size[1] * 3))
def tearDown(self):
if os.path.exists(self.test_folder):
shutil.rmtree(self.test_folder)
for f in ['test_merged_horizontal.png', 'test_merged_vertical.png']:
if os.path.exists(f):
os.remove(f)
class SpriteMergerApp(QMainWindow):
def __init__(self):
super().__init__()
self.setWindowTitle("Sprite Merger")
self.setGeometry(100, 100, 800, 600)
self.folder_path = ""
layout = QVBoxLayout()
self.select_button = QPushButton("Select Folder")
self.select_button.clicked.connect(self.select_folder)
layout.addWidget(self.select_button)
self.merge_type = QComboBox()
self.merge_type.addItems(["Horizontal", "Vertical"])
layout.addWidget(self.merge_type)
self.process_button = QPushButton("Process Selected Folder")
self.process_button.clicked.connect(self.process_folder)
layout.addWidget(self.process_button)
self.test_button = QPushButton("Run Unit Test")
self.test_button.clicked.connect(self.run_unit_test)
layout.addWidget(self.test_button)
self.result_label = QLabel("No image processed")
layout.addWidget(self.result_label)
self.image_label = QLabel()
layout.addWidget(self.image_label)
container = QWidget()
container.setLayout(layout)
self.setCentralWidget(container)
def select_folder(self):
self.folder_path = QFileDialog.getExistingDirectory(self, "Select Sprite Folder")
self.result_label.setText(f"Selected: {self.folder_path}")
def process_folder(self):
if not self.folder_path:
self.result_label.setText("Please select a folder first")
return
h_img, v_img = merge_sprites(self.folder_path, 'merged_horizontal.png', 'merged_vertical.png')
selected_type = self.merge_type.currentText()
img = h_img if selected_type == "Horizontal" else v_img
if img:
pixmap = QPixmap.fromImage(ImageQt.ImageQt(img))
self.image_label.setPixmap(pixmap.scaled(700, 500, aspectRatioMode=Qt.AspectRatioMode.KeepAspectRatio))
self.result_label.setText(f"{selected_type} merge completed")
else:
self.result_label.setText(f"{selected_type} merge failed")
def run_unit_test(self):
suite = unittest.TestLoader().loadTestsFromTestCase(TestSpriteMerger)
result = unittest.TextTestRunner().run(suite)
test_folder = 'test_images'
os.makedirs(test_folder, exist_ok=True)
for i in range(3):
create_test_image(os.path.join(test_folder, f'test_{i+1}.png'), (50, 20), i+1)
h_img, v_img = merge_sprites(test_folder, 'test_merged_horizontal.png', 'test_merged_vertical.png')
selected_type = self.merge_type.currentText()
img = h_img if selected_type == "Horizontal" else v_img
if img:
pixmap = QPixmap.fromImage(ImageQt.ImageQt(img))
self.image_label.setPixmap(pixmap.scaled(700, 500, aspectRatioMode=Qt.AspectRatioMode.KeepAspectRatio))
self.result_label.setText(f"Unit tests: {result.testsRun} run, {len(result.failures)} failed, showing {selected_type.lower()} merge")
else:
self.result_label.setText(f"Unit tests: {result.testsRun} run, {len(result.failures)} failed, merge failed")
if __name__ == '__main__':
app = QApplication(sys.argv)
window = SpriteMergerApp()
window.show()
sys.exit(app.exec())
pip install --upgrade keras
Collecting keras
Downloading keras-3.10.0-py3-none-any.whl.metadata (6.0 kB)
...
Successfully installed absl-py-2.3.0 h5py-3.14.0 keras-3.10.0 ml-dtypes-0.5.1 namex-0.1.0 optree-0.16.0
pip install tensorflow
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)
ERROR: No matching distribution found for tensorflow
pip install torch
Collecting torch
...
Successfully installed filelock-3.18.0 fsspec-2025.5.1 mpmath-1.3.0 sympy-1.14.0 torch-2.7.1
import sys
import os
from datetime import datetime
from PyQt6.QtWidgets import QApplication, QMainWindow, QWidget, QVBoxLayout, QPushButton, QFileDialog, QLineEdit, QCheckBox, QLabel, QMessageBox
from PyQt6.QtCore import Qt
from PIL import Image
class ResizeApp(QMainWindow):
def __init__(self):
super().__init__()
self.setWindowTitle("Image Resizer")
self.setGeometry(100, 100, 400, 200)
layout = QVBoxLayout()
central_widget = QWidget()
central_widget.setLayout(layout)
self.setCentralWidget(central_widget)
self.folder_button = QPushButton("Select Folder")
self.folder_button.clicked.connect(self.select_folder)
layout.addWidget(self.folder_button)
self.width_edit = QLineEdit("800")
self.width_edit.setPlaceholderText("Width (px)")
layout.addWidget(QLabel("Width:"))
layout.addWidget(self.width_edit)
self.height_edit = QLineEdit("600")
self.height_edit.setPlaceholderText("Height (px)")
layout.addWidget(QLabel("Height:"))
layout.addWidget(self.height_edit)
self.aspect_ratio = QCheckBox("Maintain Aspect Ratio")
self.aspect_ratio.setChecked(True)
layout.addWidget(self.aspect_ratio)
self.resize_button = QPushButton("Resize Images")
self.resize_button.clicked.connect(self.resize_images)
layout.addWidget(self.resize_button)
self.folder_path = ""
def select_folder(self):
self.folder_path = QFileDialog.getExistingDirectory(self, "Select Image Folder")
if self.folder_path:
self.folder_button.setText(f"Selected: {os.path.basename(self.folder_path)}")
def resize_images(self):
if not self.folder_path:
QMessageBox.warning(self, "Error", "Please select a folder.")
return
try:
width = int(self.width_edit.text())
height = int(self.height_edit.text())
except ValueError:
QMessageBox.warning(self, "Error", "Please enter valid width and height.")
return
if width <= 0 or height <= 0:
QMessageBox.warning(self, "Error", "Width and height must be positive.")
return
date_str = datetime.now().strftime("%d%m%y_%H%M")
aspect_str = "asp_on" if self.aspect_ratio.isChecked() else "asp_off"
output_folder = os.path.join(self.folder_path, f"resized_{date_str}_{height}_{aspect_str}")
os.makedirs(output_folder, exist_ok=True)
for file_name in os.listdir(self.folder_path):
if file_name.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif')):
image_path = os.path.join(self.folder_path, file_name)
try:
with Image.open(image_path) as img:
if self.aspect_ratio.isChecked():
img.thumbnail((width, height), Image.Resampling.LANCZOS)
else:
img = img.resize((width, height), Image.Resampling.LANCZOS)
output_path = os.path.join(output_folder, f"resized_{date_str}_{height}_{aspect_str}_{file_name}")
img.save(output_path)
except Exception as e:
QMessageBox.warning(self, "Error", f"Failed to process {file_name}: {str(e)}")
QMessageBox.information(self, "Success", f"Images resized and saved to {output_folder}!")
if __name__ == '__main__':
app = QApplication(sys.argv)
window = ResizeApp()
window.show()
sys.exit(app.exec())
import sys
import os
import re
import shutil
import zipfile
from PyQt6.QtWidgets import (QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout,
QPushButton, QListWidget, QFileDialog, QMessageBox, QDialog, QLabel)
from PyQt6.QtCore import Qt
class CriteriaDialog(QDialog):
def __init__(self, folder, parent=None):
super().__init__(parent)
self.setWindowTitle(f"Processing Folder: {folder}")
self.layout = QVBoxLayout(self)
self.layout.addWidget(QLabel(f"Selected folder: {folder}"))
self.ok_button = QPushButton("OK")
self.ok_button.clicked.connect(self.accept)
self.layout.addWidget(self.ok_button)
class DuplicateFinder(QMainWindow):
def __init__(self):
super().__init__()
self.setWindowTitle("Duplicate File Finder")
self.setGeometry(100, 100, 600, 400)
self.central_widget = QWidget()
self.setCentralWidget(self.central_widget)
self.layout = QVBoxLayout(self.central_widget)
self.file_list = QListWidget()
self.file_list.setSelectionMode(QListWidget.SelectionMode.MultiSelection)
self.layout.addWidget(self.file_list)
button_layout = QHBoxLayout()
self.select_button = QPushButton("Select Folder")
self.select_button.clicked.connect(self.select_folder)
button_layout.addWidget(self.select_button)
self.copy_button = QPushButton("Copy To")
self.copy_button.clicked.connect(self.copy_files)
button_layout.addWidget(self.copy_button)
self.zip_button = QPushButton("Zip All")
self.zip_button.clicked.connect(self.zip_files)
button_layout.addWidget(self.zip_button)
self.delete_button = QPushButton("Delete All")
self.delete_button.clicked.connect(self.delete_files)
button_layout.addWidget(self.delete_button)
self.layout.addLayout(button_layout)
self.files = []
self.selected_folder = ""
def select_folder(self):
folder = QFileDialog.getExistingDirectory(self, "Select Folder")
if folder:
self.selected_folder = folder
dialog = CriteriaDialog(folder, self)
if dialog.exec():
self.files = []
self.file_list.clear()
self.scan_folder(folder)
self.find_duplicates()
def scan_folder(self, folder):
for root, _, files in os.walk(folder):
for file in files:
file_path = os.path.join(root, file)
self.files.append({
'path': file_path,
'name': os.path.basename(file_path),
'size': os.path.getsize(file_path),
'extension': os.path.splitext(file_path)[1]
})
def find_duplicates(self):
duplicates = self.find_by_similar_name()
self.display_duplicates(duplicates)
def find_by_similar_name(self):
duplicates = []
name_groups = {}
pattern = r'(.+?)(?:[\s_-]*\d{3,}|[\s_-]*\d{1,2}|_)?(?:\.\w+)?$'
for file in self.files:
match = re.match(pattern, file['name'])
if match:
base_name = match.group(1)
if base_name in name_groups:
name_groups[base_name].append(file)
else:
name_groups[base_name] = [file]
for base_name, files in name_groups.items():
if len(files) > 1:
duplicates.extend(files)
return duplicates
def display_duplicates(self, duplicates):
self.file_list.clear()
for file in duplicates:
self.file_list.addItem(file['path'])
def copy_files(self):
if not self.file_list.selectedItems():
QMessageBox.warning(self, "Warning", "No files selected!")
return
dest_folder = QFileDialog.getExistingDirectory(self, "Select Destination Folder")
if dest_folder:
for item in self.file_list.selectedItems():
file_path = item.text()
dest_path = os.path.join(dest_folder, os.path.basename(file_path))
shutil.copy2(file_path, dest_path)
QMessageBox.information(self, "Success", "Files copied!")
def zip_files(self):
if not self.file_list.selectedItems():
QMessageBox.warning(self, "Warning", "No files selected!")
return
zip_path = QFileDialog.getSaveFileName(self, "Save Zip File", "", "Zip Files (*.zip)")[0]
if zip_path:
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
for item in self.file_list.selectedItems():
file_path = item.text()
zipf.write(file_path, os.path.basename(file_path))
QMessageBox.information(self, "Success", "Files zipped!")
def delete_files(self):
if not self.file_list.selectedItems():
QMessageBox.warning(self, "Warning", "No files selected!")
return
reply = QMessageBox.question(self, "Confirm", "Delete selected files?",
QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No)
if reply == QMessageBox.StandardButton.Yes:
for item in self.file_list.selectedItems():
os.remove(item.text())
self.file_list.clear()
QMessageBox.information(self, "Success", "Files deleted!")
if __name__ == '__main__':
app = QApplication(sys.argv)
window = DuplicateFinder()
window.show()
sys.exit(app.exec())
cd uv_projects
uv_projects>uv init hello-world
Initialized project `hello-world` at `D:\PythonProjects\uv_projects\hello-world`
uv_projects>cd hello-world
uv_projects\hello-world>uv run main.py
Using CPython 3.13.5 interpreter at: C:\Python3135\python.exe
Creating virtual environment at: .venv
Hello from hello-world!
import sys
from PyQt6.QtWidgets import QApplication, QLabel, QWidget, QVBoxLayout, QMenu
# diff PyQt6 versus old PyQt
from PyQt6.QtGui import QAction
from PyQt6.QtGui import QPixmap, QIcon
from PyQt6.QtCore import QTimer, Qt
class AnimatedWindow(QWidget):
def __init__(self):
super().__init__()
# set window
self.setWindowFlags(Qt.WindowType.FramelessWindowHint | Qt.WindowType.WindowStaysOnTopHint)
self.setAttribute(Qt.WidgetAttribute.WA_TranslucentBackground)
# build layout
layout = QVBoxLayout()
self.label = QLabel(self)
layout.addWidget(self.label)
self.setLayout(layout)
# load sprite sheet from Camera_256px.png file name
sprite_sheet = QPixmap("Camera_256px.png")
self.frame_width = sprite_sheet.width() // 11 # because I have 11 sprite on image
self.sprites = [sprite_sheet.copy(i * self.frame_width, 0, self.frame_width, sprite_sheet.height()) for i in range(11)]
self.current_frame = 0
#
self.timer = QTimer()
self.timer.timeout.connect(self.update_animation)
self.timer.start(150) # Schimbă frame-ul la fiecare 150 ms
#
self.resize(self.frame_width, sprite_sheet.height())
# left down on screen
screen = QApplication.primaryScreen().geometry()
self.move(10, screen.height() - self.height() - 10)
def update_animation(self):
"""Actualizează sprite-ul în QLabel"""
self.label.setPixmap(self.sprites[self.current_frame])
self.current_frame = (self.current_frame + 1) % len(self.sprites)
if __name__ == "__main__":
app = QApplication(sys.argv)
window = AnimatedWindow()
window.show()
sys.exit(app.exec())
python -m pip install -U nuitka
Collecting nuitka
Downloading Nuitka-2.7.tar.gz (3.9 MB)
...
Successfully built nuitka
Installing collected packages: zstandard, ordered-set, nuitka
Successfully installed nuitka-2.7 ordered-set-4.1.0 zstandard-0.23.0
[notice] A new release of pip is available: 25.0.1 -> 25.1.1
[notice] To update, run: python.exe -m pip install --upgrade pip
python.exe -m pip install --upgrade pip
...
Successfully installed pip-25.1.1
python -m nuitka --version
2.7
Commercial: None
Python: 3.13.0rc1 (tags/v3.13.0rc1:e4a3e78, Jul 31 2024, 20:58:38) [MSC v.1940 64 bit (AMD64)]
Flavor: CPython Official
GIL: yes
Executable: C:\Python313\python.exe
OS: Windows
Arch: x86_64
WindowsRelease: 10
Nuitka-Scons:WARNING: Windows SDK must be installed in Visual Studio for it to be usable
Nuitka-Scons:WARNING: with Nuitka. Use the Visual Studio installer for adding it.
Version C compiler: ~\AppData\Local\Nuitka\Nuitka\Cache\downloads\gcc\x86_64\14.2.0posix-19.1.1-12.0.0-msvcrt-r2\mingw64\bin\gcc.exe (gcc 14.2.0).
python -m nuitka --version
2.7
Commercial: None
Python: 3.13.3 (tags/v3.13.3:6280bb5, Apr 8 2025, 14:47:33) [MSC v.1943 64 bit (AMD64)]
Flavor: CPython Official
GIL: yes
Executable: C:\Python313\python.exe
OS: Windows
Arch: x86_64
WindowsRelease: 10
Nuitka-Scons:WARNING: Windows SDK must be installed in Visual Studio for it to be usable
Nuitka-Scons:WARNING: with Nuitka. Use the Visual Studio installer for adding it.
Version C compiler: ~\AppData\Local\Nuitka\Nuitka\Cache\downloads\gcc\x86_64\14.2.0posix-19.1.1-12.0.0-msvcrt-r2\mingw64\bin\gcc.exe (gcc 14.2.0).
python -m pip install -U nuitka
Collecting nuitka
...
Installing collected packages: zstandard, ordered-set, nuitka
Successfully installed nuitka-2.7 ordered-set-4.1.0 zstandard-0.23.0
[notice] A new release of pip is available: 24.2 -> 25.1.1
[notice] To update, run: python.exe -m pip install --upgrade pip
python.exe -m pip install --upgrade pip
Requirement already satisfied: pip in c:\python_3_13_0\lib\site-packages (24.2)
...
Successfully installed pip-25.1.1
py -3.12 -m pip install -U nuitka
Collecting nuitka
Using cached Nuitka-2.7.tar.gz (3.9 MB)
Installing build dependencies ... done
...
Installing collected packages: zstandard, ordered-set, nuitka
Successfully installed nuitka-2.7 ordered-set-4.1.0 zstandard-0.23.0
[notice] A new release of pip is available: 24.2 -> 25.1.1
[notice] To update, run: C:\Python312\python.exe -m pip install --upgrade pip
py -0
-V:3.13 * Python 3.13 (64-bit)
-V:3.12 Python 3.12 (64-bit)
py -3.12 -m nuitka --mingw64 hello.py
Nuitka-Options: Used command line options:
Nuitka-Options: --mingw64 hello.py
Nuitka-Options:WARNING: You did not specify to follow or include anything but main
Nuitka-Options:WARNING: program. Check options and make sure that is intended.
Nuitka: Starting Python compilation with:
Nuitka: Version '2.7' on Python 3.12 (flavor 'CPython Official')
Nuitka: commercial grade 'not installed'.
Nuitka: Completed Python level compilation and optimization.
Nuitka: Generating source code for C backend compiler.
Nuitka: Running data composer tool for optimal constant value handling.
Nuitka: Running C compilation via Scons.
Nuitka-Scons: Backend C compiler: gcc (gcc 14.2.0).
Nuitka-Scons: Backend C linking with 6 files (no progress information available for
Nuitka-Scons: this stage).
Nuitka-Scons: Compiled 6 C files using ccache.
Nuitka-Scons: Cached C files (using ccache) with result 'cache miss': 6
Nuitka: Keeping build directory 'hello.build'.
Nuitka: Successfully created 'D:\PythonProjects\hello.exe'.
Nuitka: Execute it by launching 'hello.cmd', the batch file needs to set environment.
def talk(message):
return "Talk " + message
def main():
print(talk("Hello World"))
if __name__ == "__main__":
main()
hello.exe
Talk Hello World
system32>setx.exe FAL_KEY "e6fd708c-8065-4c73-ac2a-e3c73c6ff0fe:f70e0adb08362a3073993efa31b6acee"
SUCCESS: Specified value was saved.
import fal_client
response = fal_client.run("fal-ai/fast-sdxl", arguments={"prompt": "a cute cat, realistic, orange"})
print(response["images"][0]["url"])
python test_fal_client_001.py
https://v3.fal.media/files/rabbit/kY2MZG6LLkzjyIT8J3oiI.jpe