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| import streamlit as st | |
| import sahi.utils.file | |
| import sahi.utils.mmdet | |
| from sahi import AutoDetectionModel | |
| from PIL import Image | |
| import random | |
| from utils import sahi_mmdet_inference | |
| from streamlit_image_comparison import image_comparison | |
| MMDET_YOLOX_TINY_MODEL_URL = "https://huggingface.co/fcakyon/mmdet-yolox-tiny/resolve/main/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth" | |
| MMDET_YOLOX_TINY_MODEL_PATH = "yolox.pt" | |
| 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" | |
| 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-yolox.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", | |
| } | |
| slice_size=512 | |
| overlap_ratio=0.2 | |
| postprocess_match_metric = 'IOU' | |
| postprocess_type = 'NMS' | |
| postprocess_match_threshold = 0.5 | |
| postprocess_class_agnostic = True | |
| def download_comparison_images(): | |
| sahi.utils.file.download_from_url( | |
| "https://user-images.githubusercontent.com/34196005/143309873-c0c1f31c-c42e-4a36-834e-da0a2336bb19.jpg", | |
| "highway2-yolox.jpg", | |
| ) | |
| sahi.utils.file.download_from_url( | |
| "https://user-images.githubusercontent.com/34196005/143309867-42841f5a-9181-4d22-b570-65f90f2da231.jpg", | |
| "highway2-sahi.jpg", | |
| ) | |
| def get_model(): | |
| 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, | |
| ) | |
| detection_model = AutoDetectionModel.from_pretrained( | |
| model_type='mmdet', | |
| model_path=MMDET_YOLOX_TINY_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 = [ | |
| "Loading...", | |
| ] | |
| 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="A Demonstration of SARAI's Utility", | |
| page_icon="🐦", | |
| layout="wide", | |
| initial_sidebar_state="auto", | |
| ) | |
| download_comparison_images() | |
| 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-yolox.jpg") | |
| if "output_2" not in st.session_state: | |
| st.session_state["output_2"] = Image.open("highway2-sahi.jpg") | |
| st.markdown( | |
| """ | |
| <h2 style='text-align: center'> | |
| A Demonstration of SARAI's Utility | |
| </h2> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| st.write("##") | |
| with st.expander("Instructions for Use"): | |
| st.markdown( | |
| """ | |
| <p> | |
| 1. Upload or select the input image | |
| <br /> | |
| 2. Press "Perform Prediction" to start image processing" | |
| </p> | |
| """, | |
| unsafe_allow_html=True, | |
| ) | |
| st.write("##") | |
| col1, col2, col3 = st.columns([4, 1, 6]) | |
| with col1: | |
| st.markdown(f"##### Set input image:") | |
| # set input image by upload | |
| image_file = st.file_uploader( | |
| "Upload an image:", 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 our sample 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=325) | |
| with col3: | |
| st.markdown(f"##### YOLOX Standard vs SARAI Prediction:") | |
| static_component = image_comparison( | |
| img1=st.session_state["output_1"], | |
| img2=st.session_state["output_2"], | |
| label1="YOLOX", | |
| label2="SARAI", | |
| width=700, | |
| starting_position=50, | |
| show_labels=True, | |
| make_responsive=True, | |
| in_memory=True, | |
| ) | |
| col1, col2, col3, col4, col5= st.columns([1, 2, 4, 2, 2]) | |
| with col2: | |
| # submit button | |
| 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 = 416 | |
| with st.spinner( | |
| text="Performing prediction.. " + st.session_state["last_spinner_texts"].get() | |
| ): | |
| output_1, output_2 = sahi_mmdet_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 | |
| with col4: | |
| st.markdown(f"##### Slide to Compare") | |