Update app

app.py

@@ -14,89 +14,89 @@ 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(
@@ -106,16 +106,16 @@ st.set_page_config(
     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.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'>
+#     <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'>
-# #     prepared with <a href='https://github.com/fcakyon/streamlit-image-comparison' target='_blank'>streamlit-image-comparison</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.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,
+# )