Add: life

.gitignore

@@ -0,0 +1,172 @@
+# 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/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# 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/
+THESIS
+wandb
+dummy-project
+checkpoints
+models
+densenet121
+results
+samples
+sample-results
+.gradio

app.py

@@ -1,7 +1,21 @@
 import gradio as gr
+from glob import glob
+from PIL import Image
+from explain import get_results, reproduce
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+def classify_and_explain(image):
+    reproduce()
+    # This function will classify the image and return a list of image paths
+    list_of_images = get_results(img_for_testing=image)
+    return list_of_images
+
+
+def get_examples():
+    return [Image.open(i) for i in glob("samples/*")]
+
+
+demo = gr.Interface(
+    fn=classify_and_explain, inputs="image", outputs="gallery", examples=get_examples()
+)
 demo.launch()

explain.py

@@ -15,7 +15,7 @@ from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
 from pytorch_grad_cam.utils.image import show_cam_on_image, deprocess_image
 
 from ultralytics import YOLO
-from rembg import remove
+# from rembg import remove
 import uuid
 
 
@@ -131,9 +131,6 @@ def save_explanation_results(res, path):
     plt.close(fig)
 
 
-# load stuff
-reproduce()
-
 model, image_transform = get_model(model_name)
 model.load_state_dict(torch.load(model_path))
 model.train()
@@ -163,7 +160,14 @@ def get_results(img_path=None, img_for_testing=None):
         bbox = result.boxes.xyxy[0].cpu().numpy().astype(int)
         bbox_image = image.crop((bbox[0], bbox[1], bbox[2], bbox[3]))
 
-        bbox_image = remove(bbox_image).convert("RGB")
+        # bbox_image = remove(bbox_image).convert("RGB")
+        # bbox_image = Image.fromarray(
+        #     np.where(
+        #         np.array(bbox_image) == [0, 0, 0],
+        #         [255, 255, 255],
+        #         np.array(bbox_image),
+        #     ).astype(np.uint8)
+        # )
 
         res = make_prediction_and_explain(bbox_image)
         save_explanation_results(res, save_path)
@@ -198,14 +202,14 @@ if __name__ == "__main__":
             bbox = result.boxes.xyxy[0].cpu().numpy().astype(int)
             bbox_image = image.crop((bbox[0], bbox[1], bbox[2], bbox[3]))
 
-            bbox_image = remove(bbox_image).convert("RGB")
-            bbox_image = Image.fromarray(
-                np.where(
-                    np.array(bbox_image) == [0, 0, 0],
-                    [255, 255, 255],
-                    np.array(bbox_image),
-                ).astype(np.uint8)
-            )
+            # bbox_image = remove(bbox_image).convert("RGB")
+            # bbox_image = Image.fromarray(
+            #     np.where(
+            #         np.array(bbox_image) == [0, 0, 0],
+            #         [255, 255, 255],
+            #         np.array(bbox_image),
+            #     ).astype(np.uint8)
+            # )
 
             res = make_prediction_and_explain(bbox_image)
             save_explanation_results(res, save_path)