Upload 11 files

.gitignore

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+test_video.mp4
+test.jpg
+0*.py
+__pycache__
+ISL2.keras
+ISL4.keras

Dockerfile

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+From python:3.10.10
+
+WORKDIR /app
+
+COPY requirements.txt ./requirements.txt
+
+RUN pip install -r requirements.txt
+
+EXPOSE 8501
+
+COPY . /app
+
+ENTRYPOINT ["streamlit","run"]
+
+CMD ["app.py"]

Models/yolo8n-signlanguagedetection.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:040ac545ec3e03a4ab687b00ebb2cca6e7262534e0811fa3d3eaa80af4beca85
+size 6249400

Models/yolo_v8_nano_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:59653c5ebdc82c93372189910d73e64701a83e817ce496aa130121dc2af6a66a
+size 6246041

PROJECT_README.md

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+# Sign Language detection
+
+Detects the different letters represented by actions of hand in sign language
+
+
+🚀Trained on **YOLOv8 Nano** model achieving **mAP:50 - 0.94** & **mAP:50-95 - 0.89**
+
+🤗 Hugging Face APP Link: https://huggingface.co/spaces/Pradeep018/Sign-Language-detection/
+
+* 🦹‍♂️ Dataset used: https://universe.roboflow.com/david-lee-d0rhs/american-sign-language-letters/
+
+**Requirements for projects**
+```
+ultralytics
+torch
+numpy
+streamlit
+```

app.py

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+import streamlit as st
+from pipeline import detectPipeline
+
+
+st.title('Sign Language Detection')
+st.write('Detects Sign language Alphabets in an image \nPowered by CNN model')
+
+st.write('')
+
+detect_pipeline = detectPipeline()
+
+st.info('Sign Language Detection model loaded successfully!')
+
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+
+    with st.container():
+        col1, col2 = st.columns([3, 3])
+
+        col1.header('Input Image')
+        col1.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
+
+        col1.text('')
+        col1.text('')
+
+        if st.button('Detect'):
+            detections = detect_pipeline.detect_signs(img_path=uploaded_file)
+            detections_img = detect_pipeline.drawDetections2Image(img_path=uploaded_file, detections=detections)
+
+            col2.header('Detections')
+            col2.image(detections_img, caption='Predictions by model', use_column_width=True)
+
+            # Extract text results from detections
+            text_results = detect_pipeline.extractTextResults(detections)
+
+            # Display text results below the image
+            col2.text('Textual Results:')
+            col2.text(text_results)
+
+# Ensure you have implemented the `extractTextResults` method in your `pipeline.py` file
+

gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

pages/1_SIgn_Language_Detection_On_Live_Video_Stream.p.py

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+import streamlit as st 
+from streamlit_webrtc import webrtc_streamer
+import av
+from ultralytics import YOLO
+
+
+# load yolo model
+
+yolo =  YOLO('Models/yolo8n-signlanguagedetection.pt')
+
+
+def video_frame_callback(frame):
+    # img = frame.to_ndarray(format="bgr24")
+    # # any operation 
+    # #flipped = img[::-1,:,:]
+    # pred_img = yolo.predictions(img)
+
+    # return av.VideoFrame.from_ndarray(pred_img, format="bgr24")
+    img = frame.to_image()
+    res = yolo(img)
+    res_plotted = res[0].plot().astype('uint8')
+    return av.VideoFrame.from_ndarray(res_plotted, format="bgr24")
+
+webrtc_streamer(key="example", 
+                video_frame_callback=video_frame_callback,
+                media_stream_constraints={"video":True,"audio":False})

pipeline.py

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+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+import cv2 as cv
+import pandas as pd
+
+
+class detectPipeline():
+    def __init__(self) -> None:
+        self.model = YOLO('Models/yolo_v8_nano_model.pt')
+        self.class_names = {i: chr(65 + i) for i in range(26)}
+
+
+    def detect_signs(self, img_path: str):
+        # Data Preprocessing
+        img = Image.open(img_path).convert('RGB')
+        img_array = np.array(img)
+
+        # Making detections using YOLOv8 Nano
+        detections = self.model(img_array)[0]
+        sign_detections = []
+        for sign in detections.boxes.data.tolist():
+            x1, y1, x2, y2, score, class_id = sign
+            sign_detections.append([int(x1), int(y1), int(x2), int(y2), score, int(class_id)])
+        print(sign_detections)
+        return sign_detections
+
+    def drawDetections2Image(self, img_path, detections):
+        img = Image.open(img_path).convert('RGB')
+        img = np.array(img)
+        for bbox in detections:
+            x1, y1, x2, y2, score, class_id = bbox
+            cv.rectangle(img, pt1=(x1, y1), pt2=(x2, y2), color=(0, 255, 0), thickness=4)
+            cv.putText(img, text=f'{self.class_names[class_id]} ({round(score*100, 2)}%)', org=(x1, y1-20), fontFace=cv.FONT_HERSHEY_SIMPLEX, fontScale=1.5,
+                        color=(0, 0, 255), lineType=cv.LINE_AA, thickness=4)
+        img_detections = np.array(img)
+        return img_detections
+
+    # get sign_detetction
+    def extractTextResults(self, detections):
+        text_results = ''
+        for bbox in detections:
+            x1, y1, x2, y2, score, class_id = bbox
+            text_results += f'{self.class_names[class_id]} : {score}\n'
+        return text_results

requirements.txt

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+ultralytics
+torch
+numpy
+streamlit
+opencv-python
+streamlit-webrtc
+tensorflow