# detects and analyzes shapes in images using parallel processing.
## pip install multiprocshapefinder
### Tested against Python 3.11 / Windows 10
### Advantages:
#### Parallel Processing:
The module utilizes parallel processing (cpus parameter) to speed up the shape detection process,
which can be advantageous for analyzing multiple images simultaneously.
#### Flexible Input:
It accepts images in various formats, providing flexibility in the source of the images.
#### Caching:
The option to use caching (usecache) can save time on the same images.
#### Visualization:
The module provides a function (draw_results) for visualizing the detected shapes on the original images,
making it easier for users to interpret and verify the results.
#### Configurability:
The module exposes various parameters, such as Canny edge detection thresholds and contour approximation factors,
allowing users to fine-tune the shape detection process based on their requirements.
```python
# Importing functions from the multiprocshapefinder module
from multiprocshapefinder import find_all_shapes, draw_results
# List of image URLs to be processed
images = [
r"https://raw.githubusercontent.com/hansalemaos/screenshots/main/findshapes_1.png",
] # accepts almost all formats (url/path/buffer/base64/np/PIL) - https://github.com/hansalemaos/a_cv_imwrite_imread_plus
# Calling find_all_shapes function to detect and analyze shapes in the given images
df = find_all_shapes(
images,
threshold1=10,
threshold2=90,
approxPolyDPvar=0.01,
cpus=5, # Number of CPU cores to use for parallel processing
chunks=1, # Number of chunks to split the image processing into
print_stderr=True, # Print error messages to stderr
print_stdout=False, # Do not print standard output messages
usecache=True, # Use caching for intermediate results
)
# Printing the resulting DataFrame containing shape information
print(df)
# Calling draw_results function to visualize the detected shapes on the original image
draw_results(
df,
images[0], # Using the first image in the list - filter the DF (df.loc) if len(images)>1
min_area=5, # Minimum area threshold for shapes to be considered
shapes=("rectangle", "triangle", "circle", "pentagon", "hexagon", "oval"), # Shapes to be visualized
cv2show=True, # Display the result using OpenCV
)
```
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"description": "\r\n# detects and analyzes shapes in images using parallel processing. \r\n\r\n## pip install multiprocshapefinder\r\n\r\n### Tested against Python 3.11 / Windows 10\r\n\r\n\r\n### Advantages:\r\n\r\n#### Parallel Processing: \r\n\r\nThe module utilizes parallel processing (cpus parameter) to speed up the shape detection process, \r\nwhich can be advantageous for analyzing multiple images simultaneously.\r\n\r\n#### Flexible Input: \r\n\r\nIt accepts images in various formats, providing flexibility in the source of the images.\r\n\r\n#### Caching: \r\n\r\nThe option to use caching (usecache) can save time on the same images.\r\n\r\n#### Visualization: \r\n\r\nThe module provides a function (draw_results) for visualizing the detected shapes on the original images,\r\nmaking it easier for users to interpret and verify the results.\r\n\r\n#### Configurability: \r\n\r\nThe module exposes various parameters, such as Canny edge detection thresholds and contour approximation factors, \r\nallowing users to fine-tune the shape detection process based on their requirements.\r\n\r\n\r\n```python\r\n\r\n# Importing functions from the multiprocshapefinder module\r\nfrom multiprocshapefinder import find_all_shapes, draw_results\r\n\r\n# List of image URLs to be processed\r\nimages = [\r\n r\"https://raw.githubusercontent.com/hansalemaos/screenshots/main/findshapes_1.png\",\r\n] # accepts almost all formats (url/path/buffer/base64/np/PIL) - https://github.com/hansalemaos/a_cv_imwrite_imread_plus\r\n\r\n# Calling find_all_shapes function to detect and analyze shapes in the given images\r\ndf = find_all_shapes(\r\n images,\r\n threshold1=10,\r\n threshold2=90,\r\n approxPolyDPvar=0.01,\r\n cpus=5, # Number of CPU cores to use for parallel processing\r\n chunks=1, # Number of chunks to split the image processing into\r\n print_stderr=True, # Print error messages to stderr\r\n print_stdout=False, # Do not print standard output messages\r\n usecache=True, # Use caching for intermediate results\r\n)\r\n\r\n# Printing the resulting DataFrame containing shape information\r\nprint(df)\r\n\r\n# Calling draw_results function to visualize the detected shapes on the original image\r\ndraw_results(\r\n df,\r\n images[0], # Using the first image in the list - filter the DF (df.loc) if len(images)>1\r\n min_area=5, # Minimum area threshold for shapes to be considered\r\n shapes=(\"rectangle\", \"triangle\", \"circle\", \"pentagon\", \"hexagon\", \"oval\"), # Shapes to be visualized\r\n cv2show=True, # Display the result using OpenCV\r\n)\r\n\r\n\r\n```\r\n",
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