Today, I tested a simple tool for news with the gnews python package:
Showing posts with label python packages. Show all posts
Showing posts with label python packages. Show all posts
Saturday, October 4, 2025
Wednesday, October 1, 2025
Friday, September 26, 2025
Python Qt6 : tool for game development with PNG images.
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.
Wednesday, September 24, 2025
Python 3.10.7 : Krita and python - part 002.
A simple source code to export PNG file for Godot game engine as Texture2D .
from krita import *
from PyQt5.QtWidgets import QAction, QMessageBox
import os
class ExportGodotPNG(Extension):
def __init__(self, parent):
super().__init__(parent)
def setup(self):
pass
def export_png(self):
# Get the active document
doc = Krita.instance().activeDocument()
if not doc:
QMessageBox.warning(None, "Error", "No document open! Please open a document and try again.")
return
# Create an InfoObject for PNG export
info = InfoObject()
info.setProperty("alpha", True) # Keep alpha channel for transparency
info.setProperty("compression", 0) # No compression for maximum quality
info.setProperty("interlaced", False) # Disable interlacing
info.setProperty("forceSRGB", True) # Force sRGB for Godot compatibility
# Build the output file path
if doc.fileName():
base_path = os.path.splitext(doc.fileName())[0]
else:
base_path = os.path.join(os.path.expanduser("~"), "export_godot")
output_file = base_path + "_godot.png"
# Export the document as PNG
try:
doc.exportImage(output_file, info)
# Show success message with brief usage info
QMessageBox.information(None, "Success",
f"Successfully exported as PNG for Godot: {output_file}\n\n"
"This PNG has no compression, alpha channel support, and sRGB for Godot compatibility. "
"To use in Godot, import the PNG and adjust texture settings as needed."
)
except Exception as e:
QMessageBox.critical(None, "Error", f"Export failed: {str(e)}")
def createActions(self, window):
# Create only the export action in Tools > Scripts
action_export = window.createAction("export_godot_png", "Export Godot PNG", "tools/scripts")
action_export.triggered.connect(self.export_png)
# Register the plugin
Krita.instance().addExtension(ExportGodotPNG(Krita.instance()))Monday, September 8, 2025
Python 3.13.0 : Script for python modules then installs them - updated with fix.
This script scans a folder full of .py files Python scripts, identifies all the external modules they import, filters out built-in ones, writes the installable ones to a requirements.txt file, and then installs them using pip—in parallel threads for speed.
I use the copilot and some comments are into my language, but I tested and works well:
NOTE: I updated with detection python modules based "from" and another issue: check if python module is instaled and step over that python module ...
This script will try to install many python modules, I can update to be better with these issues:
...some modules are default , some scripts are from another area, see Blender 3D with bpy python modules, some packages comes with same modules, this can be soleved with defined lists with unique items.
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()Saturday, August 30, 2025
Python Qt6 : ... management of installations and build python package.
Yesterday I created a small project for managing Python packages and building a new package based on added modules. I only tested the local installations of various Python versions and the creation of a new package, but it worked.
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
...
Python 3.13.0 : Predicted XAU/USD with torch.
Testing the torch python package
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))The result is :
python torch_001.py
Predicted XAU/USD closing price: 1819.57Python 3.13.0 : Predicted XAU/USD with tensorflow.
This is the source code :
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))The result is :
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: 2.9Wednesday, August 27, 2025
Python 3.13.0 : Predicted XAU/USD with MLPRegressor.
Testing the MLPRegressor from sklearn python package:
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))The answer is: Predicted XAU/USD closing price: 1836.68
Thursday, August 21, 2025
Python Qt6 : Use python with ffmpeg tool ...
If you download a video from youtube with high resolution using a tool as yt-dlp then you can get two files with video and audio content:
... one with be with .f401.mp4 and another with .f251-9.webm and using the ffmpeg tool you can create one .mp4 file with both audio and video content.
Let's see a source code with python and PyQt6 module to search into D:\Software folder and create the mp4 file.
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()
Sunday, August 17, 2025
News : Django Hijack
Log in and work on behalf of other users without having to know their credentials.
With Django Hijack, admins can impersonate and work on behalf of other users without having to know their credentials.
Comes with two versions available: v2.x and v3.x. The last v3.x provides a security first design, easy integration, customisation, out-of-the-box Django admin support and dark-mode. It is a complete rewrite and all former APIs are broken.
See the webpage project.
Monday, July 21, 2025
News : The geoai-py - part 001.
A powerful Python package for integrating Artificial Intelligence with geospatial data analysis and visualization
GeoAI bridges the gap between AI and geospatial analysis, providing tools for processing, analyzing, and visualizing geospatial data using advanced machine learning techniques. Whether you're working with satellite imagery, LiDAR point clouds, or vector data, GeoAI offers intuitive interfaces to apply cutting-edge AI models.
Today , I tested this python package named geoai-py. I used the pip tool:
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-1.20.1You can find some example on the Open Geospatial Solutions - the official youtube channel
Let's see my testing python example:
>>> 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']Sunday, July 13, 2025
Python Qt6 : simple celtic knots tool with SVG file format.
Today, I try to create SVG file with an celtic knot design tool.
I used random values from -360 up to 360 for for Twist 1, Twist 2, and Twist 3 sliders.
The basic function is this, is created by artificial intelligence and not works very well.
# 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))See one random example with this tool:
Saturday, July 12, 2025
Python Qt6 : simple merge sprites images with unittest feature.
Today, I created one python tool script with the artificial intelligence to merge sprites images.
I used the artificial intelligence to add unittest to create default images with PIL to test the result.
You can select your folder , select the align of merge features or test with unittest button.
This is the result and works well:
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())Sunday, July 6, 2025
Python 3.13.5 : keras and torch and tensorflow on python version 3.13.5 !
I am not very satisfied with the security of the system in this area, I have noticed intrusions when I was at the Vodafone provider and now I am on mobile and they persist. I should improve my software and hardware protection and restart the system, but I have the impression that it won't help much considering the hacking capabilities that exist.
For now, I'm sticking with this solution, because it's quite good for simple tests with the Python language.
Today I will show you that the involvement of developers is somewhat lagging behind, and this can be seen from the analysis of the development progression across versions of the Python language, as it is not easy to implement in newer versions. Some packages, although important, are not available in newer versions.Today I will show you that the involvement of developers is somewhat lagging behind, and this can be seen from the analysis of the development progression across versions of the Python language, as it is not easy to implement in newer versions. Some packages, although important, are not available in newer versions. Let's see these python packages:
Keras is a deep learning API designed for human beings, not machines. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability. When you choose Keras, your codebase is smaller, more readable, easier to iterate on.
... read more on the official website.
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-0.16.0TensorFlow does not support Python 3.13 as of July 2025; it supports Python 3.9–3.11. To use Keras with TensorFlow, downgrade to Python 3.11, and this install will comes with:
pip install tensorflow
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)
ERROR: No matching distribution found for tensorflowYou can use the torch python package:
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... during the test with these packages in running it seems that I have errors. I believe that those who develop Python didn't really have a strategy regarding the protection of installed packages because I've found all sorts of junk packages that can be installed with pip or from GitHub.
Saturday, July 5, 2025
Python 3.13.5 : the groq tested with basic example !
About groq you can find on the official Grok website .
Today I tested the groq A.P.I. with the basic example :
import os
from groq import Groq
client = Groq(
api_key="gsk_..."
)
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "explain what groq is",
}
],
model="llama-3.3-70b-versatile",
)
print(chat_completion.choices[0].message.content)The result of : "explain what groq is" is that :
Groq is a relatively new and innovative technology company that focuses on developing high-performance, artificial intelligence (AI) computing hardware and software solutions. Founded in 2016 by Jonathan Ross, a former Google engineer, Groq aims to provide faster, more efficient, and scalable AI computing capabilities to various industries.
Groq's primary product is a tensor processing unit (TPU) chip designed specifically for machine learning (ML) and deep learning (DL) workloads. This TPU is optimized for high-performance, low-latency, and low-power consumption, making it suitable for applications that require rapid processing of vast amounts of data, such as:
1. **Computer Vision**: Image and video recognition, object detection, and tracking.
2. **Natural Language Processing (NLP)**: Text analysis, sentiment analysis, and language translation.
3. **Speech Recognition**: Voice command processing and speech-to-text applications.
Groq's innovative architecture and design philosophy are centered around the following key aspects:
1. **High-Bandwidth Memory**: Groq's TPU features high-bandwidth memory, which enables fast data transfer and processing.
2. **Scalable Architecture**: The TPU is designed to scale horizontally, allowing for easy expansion to meet growing computational demands.
3. **Low-Latency**: Groq's architecture minimizes latency, ensuring fast response times and real-time processing capabilities.
4. **Software-Defined**: The TPU is software-defined, allowing for flexibility and customization to support a wide range of AI applications and frameworks.
Groq's technology has the potential to accelerate AI adoption in various industries, including:
1. **Autonomous Vehicles**: Enhanced computer vision and sensor processing for safer and more efficient autonomous driving.
2. **Healthcare**: Faster medical image analysis and diagnosis, as well as improved personalized medicine and treatment planning.
3. **Financial Services**: Enhanced risk analysis, portfolio optimization, and fraud detection using AI-powered systems.
While Groq is still a relatively new company, its innovative approach to AI computing has garnered significant attention and interest from the tech industry, investors, and potential customers. As AI continues to transform various aspects of our lives, Groq's technology is poised to play a significant role in shaping the future of artificial intelligence and machine learning.
Wednesday, July 2, 2025
Python Qt6 : ... simple resize image files.
I like the combination of Python development and the inclusion of the PyQt6 module. It is very fast and stable and allows me to create all sorts of tools to use.
Today I will show you another handy script that allows you to read all the image files from a folder and, depending on the selections: height, length, and/or aspect ratio, resize them and then place them in a folder created specifically for the resulting images.
Here is how the script looks, I clearly used artificial intelligence and it didn't take more than a few minutes, my evaluation, testing, and rearranging the interface took longer ...
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())Tuesday, July 1, 2025
Python 3.13.5 : use the jupyterlab, notebook and voila - part 001.
JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design invites extensions to expand and enrich functionality.
Let's test with these python modules: jupyterlab, notebook and voila.
First, I install with pip tool the jupyterlab python module:
pip install jupyterlab
Collecting jupyterlab
...
Successfully installed anyio-4.9.0 argon2-cffi-25.1.0 argon2-cffi-bindings-21.2.0 arrow-1.3.0 asttokens-3.0.0 async-lru-2.0.5
attrs-25.3.0 babel-2.17.0 bleach-6.2.0 cffi-1.17.1 comm-0.2.2 debugpy-1.8.14 defusedxml-0.7.1 executing-2.2.0
fastjsonschema-2.21.1 fqdn-1.5.1 h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 ipykernel-6.29.5 ipython-9.4.0
ipython-pygments-lexers-1.1.1 isoduration-20.11.0 jedi-0.19.2 json5-0.12.0 jsonpointer-3.0.0 jsonschema-4.24.0
jsonschema-specifications-2025.4.1 jupyter-client-8.6.3 jupyter-core-5.8.1 jupyter-events-0.12.0 jupyter-lsp-2.2.5
jupyter-server-2.16.0 jupyter-server-terminals-0.5.3 jupyterlab-4.4.4 jupyterlab-pygments-0.3.0 jupyterlab-server-2.27.3
matplotlib-inline-0.1.7 mistune-3.1.3 nbclient-0.10.2 nbconvert-7.16.6 nbformat-5.10.4 nest-asyncio-1.6.0 notebook-shim-0.2.4
overrides-7.7.0 pandocfilters-1.5.1 parso-0.8.4 platformdirs-4.3.8 prometheus-client-0.22.1 prompt_toolkit-3.0.51
psutil-7.0.0 pure-eval-0.2.3 pycparser-2.22 python-json-logger-3.3.0 pywin32-310 pywinpty-2.0.15 pyyaml-6.0.2 pyzmq-27.0.0
referencing-0.36.2 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 rpds-py-0.26.0 send2trash-1.8.3 sniffio-1.3.1
stack_data-0.6.3 terminado-0.18.1 tinycss2-1.4.0 tornado-6.5.1 traitlets-5.14.3 types-python-dateutil-2.9.0.20250516
uri-template-1.3.0 wcwidth-0.2.13 webcolors-24.11.1 webencodings-0.5.1 websocket-client-1.8.0Next, the install of the notebook python module
pip install notebook
Collecting notebook
...
Installing collected packages: notebook
Successfully installed notebook-7.4.4This python package named voila will help us to use online graphic user interfaces:
pip install voila
Collecting voila
...
Successfully installed voila-0.5.8 websockets-15.0.1The voila package need to work with ipywidgets python package:
pip install ipywidgets
Collecting ipywidgets
...
Successfully installed ipywidgets-8.1.7 jupyterlab_widgets-3.0.15 widgetsnbextension-4.0.14Let's start the jupiter tool with this command:
jupyter notebookI used an default python example with a slider:
import ipywidgets as widgets
from IPython.display import display
slider = widgets.IntSlider(value=5, min=0, max=10)
display(slider)This command will start the web with the slider using the voila command:
voila test.ipynbThe result is this:
Friday, June 27, 2025
Python 3.13.5 : the manim python module - part 001.
I used this package few days ago and now I wrote about how can used it.
Update the new release of pip is available: 25.0.1 -> 25.1.1 , run this command:
python.exe -m pip install --upgrade pipThe documentation can be found on the official website.
pip install manim
Collecting manim
Downloading manim-0.19.0-py3-none-any.whl.metadata (11 kB)
...
Successfully installed Pillow-11.2.1 Pygments-2.19.1 audioop-lts-0.2.1 av-13.1.0 beautifulsoup4-4.13.4 click-8.2.1 cloup-3.0.7 colorama-0.4.6 decorator-5.2.1 glcontext-3.0.0 isosurfaces-0.1.2 manim-0.19.0 manimpango-0.6.0 mapbox-earcut-1.0.3 markdown-it-py-3.0.0 mdurl-0.1.2 moderngl-5.12.0 moderngl-window-3.1.1 networkx-3.5 numpy-2.3.0 pycairo-1.28.0 pydub-0.25.1 pyglet-2.1.6 pyglm-2.8.2 rich-14.0.0 scipy-1.15.3 screeninfo-0.8.1 skia-pathops-0.8.0.post2 soupsieve-2.7 srt-3.5.3 svgelements-1.9.6 tqdm-4.67.1 typing-extensions-4.14.0 watchdog-6.0.0Let's see how can be used to see the help area:
python.exe -m manim render --help
Manim Community v0.19.0
Usage: python -m manim render ...Let's use this source code:
from manim import *
class AdvancedAnimation(Scene):
def construct(self):
# Scene 1: Introduction
title = Text("Advanced Animation with Manim").scale(0.76)
self.play(FadeIn(title))
self.wait(2)
# Scene 2: Custom Animation
circle = Circle().set_fill(color=BLUE, opacity=0.5)
square = Square().set_fill(color=RED, opacity=0.5)
self.add(circle, square)
self.play(
Rotate(circle, angle=TAU),
Rotate(square, angle=-TAU),
run_time=2,
rate_func=linear
)
self.wait()
# Scene 3: Text Animation
text = Text("This is a custom text animation", font_size=40).to_edge(UP)
self.play(Write(text), run_time=2)
self.wait()
# Scene 4: Shapes Manipulation
triangle = Triangle().shift(RIGHT * 2)
self.play(GrowFromCenter(triangle), run_time=1.5)
self.wait()
# Scene 5: Transition to next scene
self.play(Uncreate(triangle), FadeOut(text))
# Scene 6: Final Animation
final_text = Text("This is the end of our animation", font_size=50).to_edge(DOWN)
self.play(FadeIn(final_text), run_time=1.5)
self.wait(2)
# Run the animation
AdvancedAnimation()Use this command to render:
python.exe -m manim render manim_test_001.py AdvancedAnimation -p
AdvancedAnimation -p
Manim Community v0.19.0
[06/27/25 19:52:43] INFO Animation 0 : Partial movie file scene_file_writer.py:588
written in
'D:\PythonProjects\manim_projects\med
ia\videos\manim_test_001\1080p60\part
ial_movie_files\AdvancedAnimation\397
7891868_355746014_223132457.mp4'
...
[06/27/25 19:53:56] INFO Previewed File at: file_ops.py:237
'D:\PythonProjects\manim_projects\media\videos
\manim_test_001\1080p60\AdvancedAnimation.mp4'The result comes with many files, see this 1080p60 video result:
Python Qt6 : ... simple processing file.
Today I will show you an simple python script with PyQt6.
This script ai build using the artificial inteligence and I tested will process your files by slections : copy to another folder , zip all selected and delete all selected.
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())
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