Add app file

app.py

@@ -14,108 +14,108 @@ from streamlit_image_comparison import image_comparison
 #MMDET_YOLOX_TINY_CONFIG_URL = "https://huggingface.co/fcakyon/mmdet-yolox-tiny/raw/main/yolox_tiny_8x8_300e_coco.py"
 #MMDET_YOLOX_TINY_CONFIG_PATH = "config.py"
 
-YOLOV8M_MODEL_URL = "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt"
-YOLOV8M_MODEL_PATH = "tests/data/models/yolov8/yolov8m.pt"
+#YOLOV8M_MODEL_URL = "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt"
+#YOLOV8M_MODEL_PATH = "tests/data/models/yolov8/yolov8m.pt"
 
 
 #YOLOV8M_MODEL_PATH = 'models/yolov8m.pt'
 
 
-IMAGE_TO_URL = {
-    "apple_tree.jpg": "https://user-images.githubusercontent.com/34196005/142730935-2ace3999-a47b-49bb-83e0-2bdd509f1c90.jpg",
-    "highway.jpg": "https://user-images.githubusercontent.com/34196005/142730936-1b397756-52e5-43be-a949-42ec0134d5d8.jpg",
-    "highway2.jpg": "https://user-images.githubusercontent.com/34196005/142742871-bf485f84-0355-43a3-be86-96b44e63c3a2.jpg",
-    "highway3.jpg": "https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg",
-    "highway2-yolov8m.jpg": "https://user-images.githubusercontent.com/34196005/143309873-c0c1f31c-c42e-4a36-834e-da0a2336bb19.jpg",
-    "highway2-sahi.jpg": "https://user-images.githubusercontent.com/34196005/143309867-42841f5a-9181-4d22-b570-65f90f2da231.jpg",
-}
+# IMAGE_TO_URL = {
+#     "apple_tree.jpg": "https://user-images.githubusercontent.com/34196005/142730935-2ace3999-a47b-49bb-83e0-2bdd509f1c90.jpg",
+#     "highway.jpg": "https://user-images.githubusercontent.com/34196005/142730936-1b397756-52e5-43be-a949-42ec0134d5d8.jpg",
+#     "highway2.jpg": "https://user-images.githubusercontent.com/34196005/142742871-bf485f84-0355-43a3-be86-96b44e63c3a2.jpg",
+#     "highway3.jpg": "https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg",
+#     "highway2-yolov8m.jpg": "https://user-images.githubusercontent.com/34196005/143309873-c0c1f31c-c42e-4a36-834e-da0a2336bb19.jpg",
+#     "highway2-sahi.jpg": "https://user-images.githubusercontent.com/34196005/143309867-42841f5a-9181-4d22-b570-65f90f2da231.jpg",
+# }
 
 
-@st.cache_data(show_spinner=False)
-def download_comparison_images():
-    sahi.utils.file.download_from_url(
-        "https://user-images.githubusercontent.com/34196005/143309873-c0c1f31c-c42e-4a36-834e-da0a2336bb19.jpg",
-        "highway2-yolov8m.jpg",
-    )
-    sahi.utils.file.download_from_url(
-        "https://user-images.githubusercontent.com/34196005/143309867-42841f5a-9181-4d22-b570-65f90f2da231.jpg",
-        "highway2-sahi.jpg",
-    )
+# @st.cache_data(show_spinner=False)
+# def download_comparison_images():
+#     sahi.utils.file.download_from_url(
+#         "https://user-images.githubusercontent.com/34196005/143309873-c0c1f31c-c42e-4a36-834e-da0a2336bb19.jpg",
+#         "highway2-yolov8m.jpg",
+#     )
+#     sahi.utils.file.download_from_url(
+#         "https://user-images.githubusercontent.com/34196005/143309867-42841f5a-9181-4d22-b570-65f90f2da231.jpg",
+#         "highway2-sahi.jpg",
+#     )
 
 
-@st.cache_data(show_spinner=False)
-def get_model():
+# @st.cache_data(show_spinner=False)
+# def get_model():
     
-    sahi.utils.file.download_from_url(
-        YOLOV8M_MODEL_URL,
-        YOLOV8M_MODEL_PATH,
-    )
-    #sahi.utils.file.download_from_url(
-    #    MMDET_YOLOX_TINY_MODEL_URL,
-    #    MMDET_YOLOX_TINY_MODEL_PATH,
-    #)
-    #sahi.utils.file.download_from_url(
-    #    MMDET_YOLOX_TINY_CONFIG_URL,
-    #    MMDET_YOLOX_TINY_CONFIG_PATH,
-    #)
+#     sahi.utils.file.download_from_url(
+#         YOLOV8M_MODEL_URL,
+#         YOLOV8M_MODEL_PATH,
+#     )
+#     #sahi.utils.file.download_from_url(
+#     #    MMDET_YOLOX_TINY_MODEL_URL,
+#     #    MMDET_YOLOX_TINY_MODEL_PATH,
+#     #)
+#     #sahi.utils.file.download_from_url(
+#     #    MMDET_YOLOX_TINY_CONFIG_URL,
+#     #    MMDET_YOLOX_TINY_CONFIG_PATH,
+#     #)
     
-    #sahi.utils.yolov8.download_yolov8m_model(destination_path = YOLOV8M_MODEL_PATH)
-
-    detection_model = AutoDetectionModel.from_pretrained(
-        model_type='yolov8',
-        model_path=YOLOV8M_MODEL_PATH,
-        #config_path=MMDET_YOLOX_TINY_CONFIG_PATH,
-        confidence_threshold=0.5,
-        device="cpu",
-    )
-    return detection_model
-
-
-class SpinnerTexts:
-    def __init__(self):
-        self.ind_history_list = []
-        self.text_list = [
-            "Meanwhile check out [MMDetection Colab notebook of SAHI](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb)!",
-            "Meanwhile check out [YOLOv5 Colab notebook of SAHI](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb)!",
-            "Meanwhile check out [aerial object detection with SAHI](https://blog.ml6.eu/how-to-detect-small-objects-in-very-large-images-70234bab0f98?gi=b434299595d4)!",
-            "Meanwhile check out [COCO Utilities of SAHI](https://github.com/obss/sahi/blob/main/docs/COCO.md)!",
-            "Meanwhile check out [FiftyOne utilities of SAHI](https://github.com/obss/sahi#fiftyone-utilities)!",
-            "Meanwhile [give a Github star to SAHI](https://github.com/obss/sahi/stargazers)!",
-            "Meanwhile see [how easy is to install SAHI](https://github.com/obss/sahi#getting-started)!",
-            "Meanwhile check out [Medium blogpost of SAHI](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80)!",
-            "Meanwhile try out [YOLOv5 HF Spaces demo of SAHI](https://huggingface.co/spaces/fcakyon/sahi-yolov5)!",
-        ]
-
-    def _store(self, ind):
-         if len(self.ind_history_list) == 6:
-             self.ind_history_list.pop(0)
-         self.ind_history_list.append(ind)
-
-    def get(self):
-        ind = 0
-        while ind in self.ind_history_list:
-            ind = random.randint(0, len(self.text_list) - 1)
-        self._store(ind)
-        return self.text_list[ind]
+#     #sahi.utils.yolov8.download_yolov8m_model(destination_path = YOLOV8M_MODEL_PATH)
+
+#     detection_model = AutoDetectionModel.from_pretrained(
+#         model_type='yolov8',
+#         model_path=YOLOV8M_MODEL_PATH,
+#         #config_path=MMDET_YOLOX_TINY_CONFIG_PATH,
+#         confidence_threshold=0.5,
+#         device="cpu",
+#     )
+#     return detection_model
+
+
+# class SpinnerTexts:
+#     def __init__(self):
+#         self.ind_history_list = []
+#         self.text_list = [
+#             "Meanwhile check out [MMDetection Colab notebook of SAHI](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_mmdetection.ipynb)!",
+#             "Meanwhile check out [YOLOv5 Colab notebook of SAHI](https://colab.research.google.com/github/obss/sahi/blob/main/demo/inference_for_yolov5.ipynb)!",
+#             "Meanwhile check out [aerial object detection with SAHI](https://blog.ml6.eu/how-to-detect-small-objects-in-very-large-images-70234bab0f98?gi=b434299595d4)!",
+#             "Meanwhile check out [COCO Utilities of SAHI](https://github.com/obss/sahi/blob/main/docs/COCO.md)!",
+#             "Meanwhile check out [FiftyOne utilities of SAHI](https://github.com/obss/sahi#fiftyone-utilities)!",
+#             "Meanwhile [give a Github star to SAHI](https://github.com/obss/sahi/stargazers)!",
+#             "Meanwhile see [how easy is to install SAHI](https://github.com/obss/sahi#getting-started)!",
+#             "Meanwhile check out [Medium blogpost of SAHI](https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80)!",
+#             "Meanwhile try out [YOLOv5 HF Spaces demo of SAHI](https://huggingface.co/spaces/fcakyon/sahi-yolov5)!",
+#         ]
+
+#     def _store(self, ind):
+#          if len(self.ind_history_list) == 6:
+#              self.ind_history_list.pop(0)
+#          self.ind_history_list.append(ind)
+
+#     def get(self):
+#         ind = 0
+#         while ind in self.ind_history_list:
+#             ind = random.randint(0, len(self.text_list) - 1)
+#         self._store(ind)
+#         return self.text_list[ind]
 
 
 st.set_page_config(
-    page_title="Small Object Detection with SAHI + YOLOv8",
+    page_title="small object detection with sahi + yolov8",
     page_icon="🚀",
     layout="centered",
     initial_sidebar_state="auto",
-)
+ )
 
-download_comparison_images()
+# download_comparison_images()
 
-if "last_spinner_texts" not in st.session_state:
-    st.session_state["last_spinner_texts"] = SpinnerTexts()
+# if "last_spinner_texts" not in st.session_state:
+#     st.session_state["last_spinner_texts"] = SpinnerTexts()
 
-if "output_1" not in st.session_state:
-    st.session_state["output_1"] = Image.open("highway2-yolov8m.jpg")
+# if "output_1" not in st.session_state:
+#     st.session_state["output_1"] = Image.open("highway2-yolov8m.jpg")
 
-if "output_2" not in st.session_state:
-    st.session_state["output_2"] = Image.open("highway2-sahi.jpg")
+# if "output_2" not in st.session_state:
+#     st.session_state["output_2"] = Image.open("highway2-sahi.jpg")
 
 st.markdown(
     """
@@ -126,139 +126,139 @@ st.markdown(
     """,
     unsafe_allow_html=True,
 )
-# st.markdown(
-#     """
-#     <p style='text-align: center'>
-#     <a href='https://github.com/obss/sahi' target='_blank'>SAHI Github</a> | <a href='https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox' target='_blank'>YOLOX Github</a> | <a href='https://huggingface.co/spaces/fcakyon/sahi-yolov5' target='_blank'>SAHI+YOLOv5 Demo</a>
-#     <br />
-#     Follow me for more! <a href='https://twitter.com/fcakyon' target='_blank'> <img src="https://img.icons8.com/color/48/000000/twitter--v1.png" height="30"></a><a href='https://github.com/fcakyon' target='_blank'><img src="https://img.icons8.com/fluency/48/000000/github.png" height="27"></a><a href='https://www.linkedin.com/in/fcakyon/' target='_blank'><img src="https://img.icons8.com/fluency/48/000000/linkedin.png" height="30"></a> <a href='https://fcakyon.medium.com/' target='_blank'><img src="https://img.icons8.com/ios-filled/48/000000/medium-monogram.png" height="26"></a>
-#     </p>
-#     """,
-#     unsafe_allow_html=True,
+# # st.markdown(
+# #     """
+# #     <p style='text-align: center'>
+# #     <a href='https://github.com/obss/sahi' target='_blank'>SAHI Github</a> | <a href='https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox' target='_blank'>YOLOX Github</a> | <a href='https://huggingface.co/spaces/fcakyon/sahi-yolov5' target='_blank'>SAHI+YOLOv5 Demo</a>
+# #     <br />
+# #     Follow me for more! <a href='https://twitter.com/fcakyon' target='_blank'> <img src="https://img.icons8.com/color/48/000000/twitter--v1.png" height="30"></a><a href='https://github.com/fcakyon' target='_blank'><img src="https://img.icons8.com/fluency/48/000000/github.png" height="27"></a><a href='https://www.linkedin.com/in/fcakyon/' target='_blank'><img src="https://img.icons8.com/fluency/48/000000/linkedin.png" height="30"></a> <a href='https://fcakyon.medium.com/' target='_blank'><img src="https://img.icons8.com/ios-filled/48/000000/medium-monogram.png" height="26"></a>
+# #     </p>
+# #     """,
+# #     unsafe_allow_html=True,
+# # )
+
+# st.write("##")
+
+# with st.expander("Usage"):
+#     st.markdown(
+#         """
+#         <p>
+#         1. Upload or select the input image 🖼️
+#         <br />
+#         2. (Optional) Set SAHI parameters ✔️
+#         <br />
+#         3. Press to "🚀 Perform Prediction"
+#         <br />
+#         4. Enjoy sliding image comparison 🔥
+#         </p>
+#         """,
+#         unsafe_allow_html=True,
+#     )
+
+# st.write("##")
+
+# col1, col2, col3 = st.columns([6, 1, 6])
+# with col1:
+#     st.markdown(f"##### Set input image:")
+
+#     # set input image by upload
+#     image_file = st.file_uploader(
+#         "Upload an image to test:", type=["jpg", "jpeg", "png"]
+#     )
+
+#     # set input image from exapmles
+#     def slider_func(option):
+#         option_to_id = {
+#             "apple_tree.jpg": str(1),
+#             "highway.jpg": str(2),
+#             "highway2.jpg": str(3),
+#             "highway3.jpg": str(4),
+#         }
+#         return option_to_id[option]
+
+#     slider = st.select_slider(
+#         "Or select from example images:",
+#         options=["apple_tree.jpg", "highway.jpg", "highway2.jpg", "highway3.jpg"],
+#         format_func=slider_func,
+#         value="highway2.jpg",
+#     )
+
+#     # visualize input image
+#     if image_file is not None:
+#         image = Image.open(image_file)
+#     else:
+#         image = sahi.utils.cv.read_image_as_pil(IMAGE_TO_URL[slider])
+#     st.image(image, width=300)
+
+# with col3:
+#     st.markdown(f"##### Set SAHI parameters:")
+
+#     slice_size = st.number_input("slice_size", min_value=256, value=512, step=256)
+#     overlap_ratio = st.number_input(
+#         "overlap_ratio", min_value=0.0, max_value=0.6, value=0.2, step=0.2
+#     )
+#     #postprocess_type = st.selectbox(
+#     #    "postprocess_type", options=["NMS", "GREEDYNMM"], index=0
+#     #)
+#     #postprocess_match_metric = st.selectbox(
+#     #    "postprocess_match_metric", options=["IOU", "IOS"], index=0
+#     #)
+#     postprocess_match_threshold = st.number_input(
+#         "postprocess_match_threshold", value=0.5, step=0.1
+#     )
+#     #postprocess_class_agnostic = st.checkbox("postprocess_class_agnostic", value=True)
+
+# col1, col2, col3 = st.columns([4, 3, 4])
+# with col2:
+#     submit = st.button("🚀 Perform Prediction")
+
+# if submit:
+#     # perform prediction
+#     with st.spinner(
+#         text="Downloading model weight.. "
+#         + st.session_state["last_spinner_texts"].get()
+#     ):
+#         detection_model = get_model()
+
+#     image_size = 1280
+
+#     with st.spinner(
+#         text="Performing prediction.. " + st.session_state["last_spinner_texts"].get()
+#     ):
+#         output_1, output_2 = sahi_yolov8m_inference(
+#             image,
+#             detection_model,
+#             image_size=image_size,
+#             slice_height=slice_size,
+#             slice_width=slice_size,
+#             overlap_height_ratio=overlap_ratio,
+#             overlap_width_ratio=overlap_ratio,
+#             #postprocess_type=postprocess_type,
+#             #postprocess_match_metric=postprocess_match_metric,
+#             postprocess_match_threshold=postprocess_match_threshold,
+#             #postprocess_class_agnostic=postprocess_class_agnostic,
+#         )
+
+#     st.session_state["output_1"] = output_1
+#     st.session_state["output_2"] = output_2
+
+# st.markdown(f"##### YOLOv8 Standard vs SAHI Prediction:")
+# static_component = image_comparison(
+#     img1=st.session_state["output_1"],
+#     img2=st.session_state["output_2"],
+#     label1="YOLOX",
+#     label2="SAHI+YOLOX",
+#     width=700,
+#     starting_position=50,
+#     show_labels=True,
+#     make_responsive=True,
+#     in_memory=True,
 # )
-
-st.write("##")
-
-with st.expander("Usage"):
-    st.markdown(
-        """
-        <p>
-        1. Upload or select the input image ����️
-        <br />
-        2. (Optional) Set SAHI parameters ✔️
-        <br />
-        3. Press to "🚀 Perform Prediction"
-        <br />
-        4. Enjoy sliding image comparison 🔥
-        </p>
-        """,
-        unsafe_allow_html=True,
-    )
-
-st.write("##")
-
-col1, col2, col3 = st.columns([6, 1, 6])
-with col1:
-    st.markdown(f"##### Set input image:")
-
-    # set input image by upload
-    image_file = st.file_uploader(
-        "Upload an image to test:", type=["jpg", "jpeg", "png"]
-    )
-
-    # set input image from exapmles
-    def slider_func(option):
-        option_to_id = {
-            "apple_tree.jpg": str(1),
-            "highway.jpg": str(2),
-            "highway2.jpg": str(3),
-            "highway3.jpg": str(4),
-        }
-        return option_to_id[option]
-
-    slider = st.select_slider(
-        "Or select from example images:",
-        options=["apple_tree.jpg", "highway.jpg", "highway2.jpg", "highway3.jpg"],
-        format_func=slider_func,
-        value="highway2.jpg",
-    )
-
-    # visualize input image
-    if image_file is not None:
-        image = Image.open(image_file)
-    else:
-        image = sahi.utils.cv.read_image_as_pil(IMAGE_TO_URL[slider])
-    st.image(image, width=300)
-
-with col3:
-    st.markdown(f"##### Set SAHI parameters:")
-
-    slice_size = st.number_input("slice_size", min_value=256, value=512, step=256)
-    overlap_ratio = st.number_input(
-        "overlap_ratio", min_value=0.0, max_value=0.6, value=0.2, step=0.2
-    )
-    #postprocess_type = st.selectbox(
-    #    "postprocess_type", options=["NMS", "GREEDYNMM"], index=0
-    #)
-    #postprocess_match_metric = st.selectbox(
-    #    "postprocess_match_metric", options=["IOU", "IOS"], index=0
-    #)
-    postprocess_match_threshold = st.number_input(
-        "postprocess_match_threshold", value=0.5, step=0.1
-    )
-    #postprocess_class_agnostic = st.checkbox("postprocess_class_agnostic", value=True)
-
-col1, col2, col3 = st.columns([4, 3, 4])
-with col2:
-    submit = st.button("🚀 Perform Prediction")
-
-if submit:
-    # perform prediction
-    with st.spinner(
-        text="Downloading model weight.. "
-        + st.session_state["last_spinner_texts"].get()
-    ):
-        detection_model = get_model()
-
-    image_size = 1280
-
-    with st.spinner(
-        text="Performing prediction.. " + st.session_state["last_spinner_texts"].get()
-    ):
-        output_1, output_2 = sahi_yolov8m_inference(
-            image,
-            detection_model,
-            image_size=image_size,
-            slice_height=slice_size,
-            slice_width=slice_size,
-            overlap_height_ratio=overlap_ratio,
-            overlap_width_ratio=overlap_ratio,
-            #postprocess_type=postprocess_type,
-            #postprocess_match_metric=postprocess_match_metric,
-            postprocess_match_threshold=postprocess_match_threshold,
-            #postprocess_class_agnostic=postprocess_class_agnostic,
-        )
-
-    st.session_state["output_1"] = output_1
-    st.session_state["output_2"] = output_2
-
-st.markdown(f"##### YOLOv8 Standard vs SAHI Prediction:")
-static_component = image_comparison(
-    img1=st.session_state["output_1"],
-    img2=st.session_state["output_2"],
-    label1="YOLOX",
-    label2="SAHI+YOLOX",
-    width=700,
-    starting_position=50,
-    show_labels=True,
-    make_responsive=True,
-    in_memory=True,
-)
-# st.markdown(
-#     """
-#     <p style='text-align: center'>
-#     prepared with <a href='https://github.com/fcakyon/streamlit-image-comparison' target='_blank'>streamlit-image-comparison</a>
-#     </p>
-#     """,
-#     unsafe_allow_html=True,
-# )
+# # st.markdown(
+# #     """
+# #     <p style='text-align: center'>
+# #     prepared with <a href='https://github.com/fcakyon/streamlit-image-comparison' target='_blank'>streamlit-image-comparison</a>
+# #     </p>
+# #     """,
+# #     unsafe_allow_html=True,
+# # )