Merge pull request #36 from Face-Tagger/release

Release Face Tagger v1.0.1

README.md

@@ -62,18 +62,18 @@ Face Tagger follows a systematic process to classify images:
 
 To utilize the Face Tagger library, follow these steps:
 
-1. **Define an image generator**:
+1. **Load image with functions defined in utils module**:
    The generator function allows you to loop through all images in a specified directory and create `ImageObject`
    instances for each one.
 
 ```python
-def image_generator(image_dir):
-    for filename in os.listdir(image_dir):
-        img = cv2.imread(os.path.join(image_dir, filename))
-        if img is not None:
-            yield ImageObject(filename, img)
+from utils import *
+
+image_objects = load_images_from_directory("path_to_images_directory")
 ```
 
+Replace `path_to_images_directory` with the path to your image directory.
+
 (Note: You don't necessarily need to use a generator for image_objects. You can also create ImageObject list from loaded
 images in an array and pass them.)
 
@@ -93,6 +93,8 @@ Here is the representation of the `ImageObject` class:
    and `min_similarity_face_count`.
 
 ```python
+from face_tagger import FaceTagger
+
 face_tagger = FaceTagger(
     use_gpu=False,
     image_resize_factor=1.0,
@@ -106,12 +108,10 @@ face_tagger = FaceTagger(
    generator function to classify the images.
 
 ```python
-result = face_tagger.classify_images_by_person(image_objects=image_generator("path_to_images_directory"))
+result = face_tagger.classify_images_by_person(image_objects=image_objects)
 print(result)
 ```
 
-Replace `path_to_images_directory` with the path to your image directory.
-
 ---
 
 With these steps, you can effectively utilize the Face Tagger library to classify images based on the individuals they

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "face_tagger"
-version = "1.0.0"
+version = "1.0.1"
 authors = [
   { name="Bae SungJoon", email="clcc001@naver.com" },
   { name="Yoon JongBeom", email="home99032@naver.com" },
@@ -22,4 +22,62 @@ classifiers = [
 
 [project.urls]
 "Homepage" = "https://github.com/Face-Tagger"
-"Bug Tracker" = "https://github.com/Face-Tagger/facetagger-lib/issues"
+"Bug Tracker" = "https://github.com/Face-Tagger/facetagger-lib/issues"
+
+[project.dependencies]
+absl-py = "1.4.0"
+astunparse = "1.6.3"
+cachetools = "5.3.1"
+certifi = "2023.7.22"
+charset-normalizer = "3.2.0"
+Cython = "0.29.36"
+facenet-pytorch = "2.5.3"
+filelock = "3.12.3"
+flatbuffers = "23.5.26"
+gast = "0.4.0"
+google-auth = "2.22.0"
+google-auth-oauthlib = "1.0.0"
+google-pasta = "0.2.0"
+grpcio = "1.57.0"
+h5py = "3.9.0"
+hdbscan = "0.8.33"
+idna = "3.4"
+Jinja2 = "3.1.2"
+joblib = "1.3.2"
+keras = "2.13.1"
+libclang = "16.0.6"
+Markdown = "3.4.4"
+MarkupSafe = "2.1.3"
+mpmath = "1.3.0"
+mtcnn = "0.1.1"
+networkx = "3.1"
+numpy = "1.24.3"
+oauthlib = "3.2.2"
+opencv-python = "4.8.0.74"
+opt-einsum = "3.3.0"
+packaging = "23.1"
+Pillow = "10.0.0"
+protobuf = "4.24.2"
+pyasn1 = "0.5.0"
+pyasn1-modules = "0.3.0"
+requests = "2.31.0"
+requests-oauthlib = "1.3.1"
+rsa = "4.9"
+scikit-learn = "1.3.0"
+scipy = "1.11.2"
+six = "1.16.0"
+sympy = "1.12"
+tensorboard = "2.13.0"
+tensorboard-data-server = "0.7.1"
+tensorflow = "2.13.0"
+tensorflow-estimator = "2.13.0"
+tensorflow-cpu = "2.13.0"
+tensorflow-io-gcs-filesystem = "0.31.0"
+termcolor = "2.3.0"
+threadpoolctl = "3.2.0"
+torch = "2.0.1"
+torchvision = "0.15.2"
+typing_extensions = "4.5.0"
+urllib3 = "1.26.16"
+Werkzeug = "2.3.7"
+wrapt = "1.15.0"

src/face_tagger/utils.py

@@ -1,7 +1,11 @@
+import os
+
 import cv2
 import numpy as np
 from PIL import Image
 
+from src.face_tagger.models import ImageObject
+
 
 def resize_image(image, width, height):
     """
@@ -33,3 +37,25 @@ def bytes_to_cvimage(byte_stream):
     """
 
     return cv2.imdecode(np.frombuffer(byte_stream, np.uint8), cv2.IMREAD_COLOR)
+
+
+def load_images_from_directory(images_path):
+    """
+    Generate image objects from the given directory.
+    :param images_path: Directory containing images.
+    :return: ImageObject generator.
+    """
+    for filename in os.listdir(images_path):
+        image_data = cv2.imread(os.path.join(images_path, filename))
+        if image_data is not None:
+            yield ImageObject(filename, image_data)
+
+
+def load_image(image_path):
+    """
+    Load image from the given path.
+    :param image_path: Path to the image.
+    :return: ImageObject.
+    """
+    image_data = cv2.imread(image_path)
+    return ImageObject(image_path, image_data)