Summary

.gitignore

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+# Compiled source #
+###################
+*.com
+*.class
+*.dll
+*.exe
+*.o
+*.so
+*.pyc
+
+# Packages #
+############
+# it's better to unpack these files and commit the raw source
+# git has its own built in compression methods
+*.7z
+*.dmg
+*.gz
+*.iso
+*.rar
+#*.tar
+*.zip
+
+# Logs and databases #
+######################
+*.log
+*.sqlite
+
+# OS generated files #
+######################
+.DS_Store
+ehthumbs.db
+Icon
+Thumbs.db
+.tmtags
+.idea
+.vscode
+tags
+vendor.tags
+tmtagsHistory
+*.sublime-project
+*.sublime-workspace
+.bundle
+
+# 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/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+node_modules/
+
+# 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
+.hypothesis/
+.pytest_cache/
+
+# 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
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.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
+
+# celery beat schedule file
+celerybeat-schedule
+
+# 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/
+
+# Custom
+*.pth

LICENSE

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+MIT License
+
+Copyright (c) 2024 Elena Ryumina
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

app.py

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+import torch
+from PIL import Image
+from torchvision import transforms
+import mediapipe as mp
+import numpy as np
+import math
+import requests
+
+import gradio as gr
+
+model_url = "https://huggingface.co/ElenaRyumina/face_emotion_recognition/resolve/main/FER_static_ResNet50_AffectNet.pth"
+model_path = "FER_static_ResNet50_AffectNet.pth"
+
+response = requests.get(model_url, stream=True)
+with open(model_path, 'wb') as file:
+    for chunk in response.iter_content(chunk_size=8192):
+        file.write(chunk)
+
+pth_model = torch.jit.load(model_path).to('cuda')
+pth_model.eval()
+
+DICT_EMO = {0: 'Neutral', 1: 'Happiness', 2: 'Sadness', 3: 'Surprise', 4: 'Fear', 5: 'Disgust', 6: 'Anger'}
+
+mp_face_mesh = mp.solutions.face_mesh
+
+def pth_processing(fp):
+    class PreprocessInput(torch.nn.Module):
+        def init(self):
+            super(PreprocessInput, self).init()
+
+        def forward(self, x):
+            x = x.to(torch.float32)
+            x = torch.flip(x, dims=(0,))
+            x[0, :, :] -= 91.4953
+            x[1, :, :] -= 103.8827
+            x[2, :, :] -= 131.0912
+            return x
+
+    def get_img_torch(img):
+        
+        ttransform = transforms.Compose([
+            transforms.PILToTensor(),
+            PreprocessInput()
+        ])
+        img = img.resize((224, 224), Image.Resampling.NEAREST)
+        img = ttransform(img)
+        img = torch.unsqueeze(img, 0).to('cuda')
+        return img
+    return get_img_torch(fp)
+
+def norm_coordinates(normalized_x, normalized_y, image_width, image_height):
+    
+    x_px = min(math.floor(normalized_x * image_width), image_width - 1)
+    y_px = min(math.floor(normalized_y * image_height), image_height - 1)
+    
+    return x_px, y_px
+
+def get_box(fl, w, h):
+    idx_to_coors = {}
+    for idx, landmark in enumerate(fl.landmark):
+        landmark_px = norm_coordinates(landmark.x, landmark.y, w, h)
+
+        if landmark_px:
+            idx_to_coors[idx] = landmark_px
+
+    x_min = np.min(np.asarray(list(idx_to_coors.values()))[:,0])
+    y_min = np.min(np.asarray(list(idx_to_coors.values()))[:,1])
+    endX = np.max(np.asarray(list(idx_to_coors.values()))[:,0])
+    endY = np.max(np.asarray(list(idx_to_coors.values()))[:,1])
+
+    (startX, startY) = (max(0, x_min), max(0, y_min))
+    (endX, endY) = (min(w - 1, endX), min(h - 1, endY))
+    
+    return startX, startY, endX, endY
+
+def predict(inp):
+
+    inp = np.array(inp)
+    h, w = inp.shape[:2]
+
+    with mp_face_mesh.FaceMesh(
+    max_num_faces=1,
+    refine_landmarks=False,
+    min_detection_confidence=0.5,
+    min_tracking_confidence=0.5) as face_mesh:
+        results = face_mesh.process(inp)
+        if results.multi_face_landmarks:
+            for fl in results.multi_face_landmarks:
+                startX, startY, endX, endY  = get_box(fl, w, h)
+                cur_face = inp[startY:endY, startX: endX]
+                cur_face_n = pth_processing(Image.fromarray(cur_face))
+                prediction = torch.nn.functional.softmax(pth_model(cur_face_n), dim=1).cpu().detach().numpy()[0]
+                confidences = {DICT_EMO[i]: float(prediction[i]) for i in range(7)}
+                
+    return cur_face, confidences
+
+def clear():
+    return (
+        gr.Image(value=None, type="pil"),
+        gr.Image(value=None,scale=1, elem_classes="dl2"),
+        gr.Label(value=None,num_top_classes=3, scale=1, elem_classes="dl3")
+    )
+
+style = """
+    div.dl1 div.upload-container {
+        height: 350px;
+        max-height: 350px;
+    }
+
+    div.dl2 {
+        max-height: 200px;
+    }
+
+    div.dl2 img {
+        max-height: 200px;
+    }
+
+    .submit {
+        display: inline-block;
+        padding: 10px 20px;
+        font-size: 16px;
+        font-weight: bold;
+        text-align: center;
+        text-decoration: none;
+        cursor: pointer;
+        border: var(--button-border-width) solid var(--button-primary-border-color);
+        background: var(--button-primary-background-fill);
+        color: var(--button-primary-text-color);
+        border-radius: 8px;
+        transition: all 0.3s ease;
+    }
+    
+    .submit[disabled] {
+        cursor: not-allowed;
+        opacity: 0.6;
+    }
+    
+    .submit:hover:not([disabled]) {
+        border-color: var(--button-primary-border-color-hover);
+        background: var(--button-primary-background-fill-hover);
+        color: var(--button-primary-text-color-hover);
+    }
+    
+    .submit:active:not([disabled]) {
+        transform: scale(0.98);
+    }
+"""
+
+with gr.Blocks(css=style) as demo:
+    with gr.Row():
+        with gr.Column(scale=2, elem_classes="dl1"):
+            input_image = gr.Image(type="pil")
+            with gr.Row():
+                submit = gr.Button(
+                    value="Submit", interactive=True, scale=1, elem_classes="submit"
+                )
+                clear_btn = gr.Button(
+                    value="Clear", interactive=True, scale=1
+                )
+        with gr.Column(scale=1, elem_classes="dl4"):
+            output_image = gr.Image(scale=1, elem_classes="dl2")
+            output_label = gr.Label(num_top_classes=3, scale=1, elem_classes="dl3")
+    gr.Examples(
+        ["images/fig7.jpg", "images/fig1.jpg", "images/fig2.jpg","images/fig3.jpg",
+        "images/fig4.jpg", "images/fig5.jpg", "images/fig6.jpg"],
+        [input_image],
+    )
+    
+
+    submit.click(
+        fn=predict,
+        inputs=[input_image],
+        outputs=[
+            output_image,
+            output_label
+        ],
+        queue=True,
+    )
+    clear_btn.click(
+        fn=clear,
+        inputs=[],
+        outputs=[
+            input_image,
+            output_image,
+            output_label,
+        ],
+        queue=True,
+    )
+
+if __name__ == "__main__":
+    demo.queue(api_open=False).launch(share=False)