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khushal
2024-11-12 11:56:14 +05:30
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ai/image_classification/__init__.py
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ai/image_classification/async_blur.py
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"""
AUTHOR:
Khushal P Soonderji
DATE:
Monday, 2nd Sept., 2024
OBJECTIVE:
To provide an easy way to assess images for blurriness.
Tried and implemented using HuggingFace behaviour_models.
REFERENCES:
N/A
DOWNLOADS:
N/A
"""
# *****************************************************************************************************************
# ***** ****
# *** IMPORT ***
# ***** ****
# *****************************************************************************************************************
# To make sibling directories accessible for imports:
import sys
sys.path.append(".")
sys.path.append("..")
# System-level activities:
import io
# For using the AI model:
import torch
from transformers import pipeline
# To download images from URLs:
import requests
# To read images:
from PIL import Image
# Common:
from shared import variables
# *****************************************************************************************************************
# ***** ****
# *** MACROS / ONE-TIME INIT ***
# ***** ****
# *****************************************************************************************************************
# --- Nothing Yet
# *****************************************************************************************************************
# ***** ****
# *** VARIABLES ***
# ***** ****
# *****************************************************************************************************************
# --- Nothing Yet
# *****************************************************************************************************************
# ***** ****
# *** CLASSES ***
# ***** ****
# *****************************************************************************************************************
class AssessImageBlur:
def __init__(
self,
model,
blur_label = "Blur",
clarity_label = "Normal",
blur_threshold = 0.25,
clarity_threshold = 0.65
):
"""
This class assesses the input image and tells if it is blurry or clear.
:param model: The model to use. Either the name of the HuggingFace repo, or the directory where the model is
stored.
:param blur_threshold: The max allowed blurriness (0 to 1 range).
:param clarity_threshold: The minimum needed clarity (0 to 1 range).
"""
# Note down the config:
self.__model = model
self.__device = "gpu" if torch.cuda.is_available() else "cpu"
self.__blur_label = blur_label
self.__clarity_label = clarity_label
self.__blur_threshold = blur_threshold
self.__clarity_threshold = clarity_threshold
# Initialize the classifier:
self.__classifier = pipeline(
task = "image-classification",
model = self.__model,
device = self.__device
)
@staticmethod
async def read_image(source):
"""
Reads an image in whichever format it is provided and returns it as a PIL object.
:param source: The image as either a path or a URL or a io.BytesIO object.
:return: The image opened as a PIL object.
"""
# If the input image is already a PIL image:
if isinstance(source, Image.Image): return source
# If a buffer is provided:
elif isinstance(source, io.BytesIO):
source.seek(0)
return Image.open(source)
# If a string is provided (local path or URL):
elif isinstance(source, str):
if source.startswith("http://") or source.startswith("https://"):
response = await variables.http_client.get(source)
return Image.open(io.BytesIO(response.content))
else: return Image.open(source)
async def classify(self, image):
"""
To get the prediction of the model from the given input image.
:param image: The image (as a PIL object or file path or a URL).
:return: The dictionary of classes with their respective predictions.
"""
image = await self.read_image(image)
predictions = self.__classifier(image)
classes = {p["label"]: p["score"] for p in predictions}
return classes
async def is_ok(
self,
image,
blur_threshold = None,
clarity_threshold = None
):
"""
Checks if the image given to it can be used, or should be rejected.
:param image: The image (as a PIL object or file path or a URL).
:param blur_threshold: A custom threshold to test against. If not provided, the default will be taken that was
provided when the instance was created.
:param clarity_threshold: A custom threshold to test against. If not provided, the default will be taken that
was provided when the instance was created.
:return: True if the image is okay, else False.
"""
image = await self.read_image(image)
blur_threshold = blur_threshold or self.__blur_threshold
clarity_threshold = clarity_threshold or self.__clarity_threshold
classes = await self.classify(image)
if (
classes[self.__blur_label] <= blur_threshold and
classes[self.__clarity_label] >= clarity_threshold
): return True
return False
# *****************************************************************************************************************
# ***** ****
# *** MAIN PROGRAM ***
# ***** ****
# *****************************************************************************************************************
if __name__ == "__main__":
import asyncio
async def main():
image = r"/home/developer/Downloads/low-res-check.png"
assessor = AssessImageBlur(model = r"/home/developer/PycharmProjects/utils/data/ai/behaviour_models/hugging_face/image_classification/BlurOrBokeh")
usable = await assessor.is_ok(image)
classes = await assessor.classify(image)
print("IS OKAY:", usable)
print("CLASSES:", classes)
asyncio.run(main())
ai/image_classification/async_nsfw.py
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"""
AUTHOR:
Khushal P Soonderji
DATE:
Monday, 2nd Sept., 2024
OBJECTIVE:
To provide an easy way to assess images for adult content.
Tried and implemented using HuggingFace behaviour_models.
REFERENCES:
N/A
DOWNLOADS:
N/A
"""
# *****************************************************************************************************************
# ***** ****
# *** IMPORT ***
# ***** ****
# *****************************************************************************************************************
# To make sibling directories accessible for imports:
import sys
sys.path.append(".")
sys.path.append("..")
# System-level activities:
import io
# For using the AI model:
import torch
from transformers import pipeline
# To download images from URLs:
import requests
# To read images:
from PIL import Image
# Common:
from shared import variables
# *****************************************************************************************************************
# ***** ****
# *** MACROS / ONE-TIME INIT ***
# ***** ****
# *****************************************************************************************************************
# --- Nothing Yet
# *****************************************************************************************************************
# ***** ****
# *** VARIABLES ***
# ***** ****
# *****************************************************************************************************************
# --- Nothing Yet
# *****************************************************************************************************************
# ***** ****
# *** CLASSES ***
# ***** ****
# *****************************************************************************************************************
class AssessImageNSFW:
def __init__(
self,
model,
nsfw_label = "nsfw",
nsfw_threshold = 0.25,
):
"""
This class assesses the input image and tells if it is blurry or clear.
:param model: The model to use. Either the name of the HuggingFace repo, or the directory where the model is
stored.
:param nsfw_threshold: The max allowed blurriness (0 to 1 range).
"""
# Note down the config:
self.__model = model
self.__device = "gpu" if torch.cuda.is_available() else "cpu"
self.__nsfw_label = nsfw_label
self.__nsfw_threshold = nsfw_threshold
# Initialize the classifier:
self.__classifier = pipeline(
task = "image-classification",
model = self.__model,
device = self.__device
)
@staticmethod
async def read_image(source):
"""
Reads an image in whichever format it is provided and returns it as a PIL object.
:param source: The image as either a path or a URL or a io.BytesIO object.
:return: The image opened as a PIL object.
"""
# If the input image is already a PIL image:
if isinstance(source, Image.Image): return source
# If a buffer is provided:
elif isinstance(source, io.BytesIO):
source.seek(0)
return Image.open(source)
# If a string is provided (local path or URL):
elif isinstance(source, str):
if source.startswith("http://") or source.startswith("https://"):
response = await variables.http_client.get(source)
return Image.open(io.BytesIO(response.content))
else: return Image.open(source)
async def classify(self, image):
"""
To get the prediction of the model from the given input image.
:param image: The image (as a PIL object or file path or a URL).
:return: The dictionary of classes with their respective predictions.
"""
image = await self.read_image(image)
predictions = self.__classifier(image)
classes = {p["label"]: p["score"] for p in predictions}
return classes
async def is_ok(
self,
image,
nsfw_threshold = None
):
"""
Checks if the image given to it can be used, or should be rejected.
:param image: The image (as a PIL object or file path or a URL).
:param nsfw_threshold: A custom threshold to test against. If not provided, the default will be taken that was
provided when the instance was created.
:return: True if the image is okay, else False.
"""
image = await self.read_image(image)
nsfw_threshold = nsfw_threshold or self.__nsfw_threshold
classes = await self.classify(image)
return True if classes[self.__nsfw_label] <= nsfw_threshold else False
# *****************************************************************************************************************
# ***** ****
# *** MAIN PROGRAM ***
# ***** ****
# *****************************************************************************************************************
if __name__ == "__main__":
import asyncio
async def main():
assessor = AssessImageNSFW(model = r"/path/to/model")
usable = await assessor.is_ok(r"/path/to/image/img.jpg")
classes = await assessor.classify(r"https://...")
asyncio.run(main())