repo_id stringlengths 6 101 | size int64 367 5.14M | file_path stringlengths 1 269 | content stringlengths 367 5.14M |
|---|---|---|---|
Yawan-Chaudhari/Cold_Email_Generator | 5,754 | myenv/Lib/site-packages/kubernetes/client/models/v1_bound_object_reference.py | # coding: utf-8
"""
Kubernetes
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: release-1.31
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import si... |
Yawan-Chaudhari/Cold_Email_Generator | 6,856 | myenv/Lib/site-packages/kubernetes/client/models/v1_daemon_set_list.py | # coding: utf-8
"""
Kubernetes
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: release-1.31
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import si... |
Yawan-Chaudhari/Cold_Email_Generator | 3,668 | myenv/Lib/site-packages/kubernetes/client/models/v1beta1_ip_address_spec.py | # coding: utf-8
"""
Kubernetes
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: release-1.31
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import si... |
Yawan-Chaudhari/Cold_Email_Generator | 6,904 | myenv/Lib/site-packages/kubernetes/client/models/v1_component_status.py | # coding: utf-8
"""
Kubernetes
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: release-1.31
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import si... |
Yawan-Chaudhari/Cold_Email_Generator | 7,067 | myenv/Lib/site-packages/kubernetes/client/models/v1_http_get_action.py | # coding: utf-8
"""
Kubernetes
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: release-1.31
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import si... |
Yawan-Chaudhari/Cold_Email_Generator | 5,140 | myenv/Lib/site-packages/kubernetes/client/models/v1_env_from_source.py | # coding: utf-8
"""
Kubernetes
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: release-1.31
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import si... |
Yawan-Chaudhari/Cold_Email_Generator | 9,571 | myenv/Lib/site-packages/kubernetes/client/models/v1_resource_policy_rule.py | # coding: utf-8
"""
Kubernetes
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: release-1.31
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import si... |
Yawan-Chaudhari/Cold_Email_Generator | 7,257 | myenv/Lib/site-packages/kubernetes/client/models/v1_flow_schema_condition.py | # coding: utf-8
"""
Kubernetes
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: release-1.31
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import si... |
Yawan-Chaudhari/Cold_Email_Generator | 33,074 | myenv/Lib/site-packages/kubernetes/client/models/v1_container.py | # coding: utf-8
"""
Kubernetes
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: release-1.31
Generated by: https://openapi-generator.tech
"""
import pprint
import re # noqa: F401
import si... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Deep-Learning | 48,436 | main.ipynb | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "code",
"execution_count... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Deep-Learning | 2,174 | utilities/plot.py | import os
import random
import matplotlib.pyplot as plt
import cv2
import pandas as pd
def plot_random_images(original_dir, preprocessed_dir, num_images=2, seed=42):
# Get list of filenames in both directories
random.seed(seed)
original_filenames = set(os.listdir(original_dir))
preprocessed_fil... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Deep-Learning | 1,075 | utilities/evaluate.py | import pandas as pd
from sklearn.metrics import accuracy_score
def evaluate_models(pipeline_ORB, pipeline_SIFT, df):
"""
Evaluate the accuracy of two models (ORB and SIFT) on a test dataset and return a DataFrame of the accuracies.
Parameters:
pipeline_ORB (Pipeline): The ORB model pipeline.
pipel... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Deep-Learning | 5,756 | utilities/modify.py | import os
import cv2
import numpy as np
def apply_rotations_and_save(input_dir=os.path.join('data', 'preprocessed'), output_dir=os.path.join('data', 'rotate'), rotation_angles=[45, 90, 135]):
# Traverse each image in the input directory
for root, _, files in os.walk(input_dir):
for filename in files:
... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Deep-Learning | 4,912 | utilities/utils.py | import os # OS module for file operations
import shutil # Shutil for file operations
import cv2
import numpy as np
import pandas as pd
# Load Images from File
def load_images_from_directory(directory, target_size=(256, 128)):
data = []
for root, _, files in os.walk(directory):
for filen... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Deep-Learning | 2,438 | utilities/process.py | import os
import pandas as pd
from sklearn.metrics import accuracy_score
from utilities import utils
from utilities import evaluate
def process_modify_directory(directory, pipeline_sift, pipeline_orb, encoder):
results = []
type = os.path.basename(directory)
for subdir in os.listdir(directory):
s... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Local-Feature-Extraction-Techniques | 3,650 | README.md | # Arabic Handwritten Text Identification Using Local Feature Extraction Techniques
This repository contains the implementation of various techniques for identifying Arabic handwritten text using local feature extraction methods such as ORB and SIFT. The project includes functionalities for evaluating models, processin... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Local-Feature-Extraction-Techniques | 263,442 | main.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Feature Extraction and Transformation Pipeline\n",
"\n",
"This notebook demonstrates the process of feature extraction, clustering, histogram creation, and IDF computation using custom transformers and pipelines."
]
},
... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Local-Feature-Extraction-Techniques | 2,618 | classes/feature_extractor.py | import cv2
from sklearn.base import BaseEstimator, TransformerMixin
class FeatureExtractor(BaseEstimator, TransformerMixin):
def __init__(self, method='ORB'):
self.method = method
if method == 'ORB':
self.detector = cv2.ORB_create(
nfeatures=750,
... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Local-Feature-Extraction-Techniques | 3,820 | classes/clusterer.py | import time
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.cluster import KMeans
from sklearn.pipeline import FunctionTransformer, Pipeline
from classes.feature_extractor import FeatureExtractor
class Clusterer(BaseEsti... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Local-Feature-Extraction-Techniques | 2,174 | utilities/plot.py | import os
import random
import matplotlib.pyplot as plt
import cv2
import pandas as pd
def plot_random_images(original_dir, preprocessed_dir, num_images=2, seed=42):
# Get list of filenames in both directories
random.seed(seed)
original_filenames = set(os.listdir(original_dir))
preprocessed_fil... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Local-Feature-Extraction-Techniques | 1,075 | utilities/evaluate.py | import pandas as pd
from sklearn.metrics import accuracy_score
def evaluate_models(pipeline_ORB, pipeline_SIFT, df):
"""
Evaluate the accuracy of two models (ORB and SIFT) on a test dataset and return a DataFrame of the accuracies.
Parameters:
pipeline_ORB (Pipeline): The ORB model pipeline.
pipel... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Local-Feature-Extraction-Techniques | 5,756 | utilities/modify.py | import os
import cv2
import numpy as np
def apply_rotations_and_save(input_dir=os.path.join('data', 'preprocessed'), output_dir=os.path.join('data', 'rotate'), rotation_angles=[45, 90, 135]):
# Traverse each image in the input directory
for root, _, files in os.walk(input_dir):
for filename in files:
... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Local-Feature-Extraction-Techniques | 4,912 | utilities/utils.py | import os # OS module for file operations
import shutil # Shutil for file operations
import cv2
import numpy as np
import pandas as pd
# Load Images from File
def load_images_from_directory(directory, target_size=(256, 128)):
data = []
for root, _, files in os.walk(directory):
for filen... |
yazan6546/Arabic-Handwritten-Text-Identification-Using-Local-Feature-Extraction-Techniques | 2,423 | utilities/process.py | import os
import pandas as pd
from sklearn.metrics import accuracy_score
from utilities import utils
import evaluate
def process_modify_directory(directory, pipeline_sift, pipeline_orb, encoder):
results = []
type = os.path.basename(directory)
for subdir in os.listdir(directory):
subdir_path = os... |