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Runtime error
Daniel Cerda Escobar commited on
Commit ·
9f23d95
1
Parent(s): 78e6083
Update yolo model
Browse files- app.py +50 -38
- requirements.txt +3 -2
- utils.py +7 -7
app.py
CHANGED
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@@ -1,23 +1,32 @@
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import streamlit as st
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import sahi.utils.file
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import sahi.utils.mmdet
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from sahi import AutoDetectionModel
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from PIL import Image
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import random
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from utils import
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from streamlit_image_comparison import image_comparison
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IMAGE_TO_URL = {
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"apple_tree.jpg": "https://user-images.githubusercontent.com/34196005/142730935-2ace3999-a47b-49bb-83e0-2bdd509f1c90.jpg",
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"highway.jpg": "https://user-images.githubusercontent.com/34196005/142730936-1b397756-52e5-43be-a949-42ec0134d5d8.jpg",
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"highway2.jpg": "https://user-images.githubusercontent.com/34196005/142742871-bf485f84-0355-43a3-be86-96b44e63c3a2.jpg",
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"highway3.jpg": "https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg",
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"highway2-
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"highway2-sahi.jpg": "https://user-images.githubusercontent.com/34196005/143309867-42841f5a-9181-4d22-b570-65f90f2da231.jpg",
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}
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def download_comparison_images():
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sahi.utils.file.download_from_url(
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"https://user-images.githubusercontent.com/34196005/143309873-c0c1f31c-c42e-4a36-834e-da0a2336bb19.jpg",
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"highway2-
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)
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sahi.utils.file.download_from_url(
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"https://user-images.githubusercontent.com/34196005/143309867-42841f5a-9181-4d22-b570-65f90f2da231.jpg",
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@st.cache_data(show_spinner=False)
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def get_model():
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detection_model = AutoDetectionModel.from_pretrained(
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model_type='
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model_path=
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config_path=MMDET_YOLOX_TINY_CONFIG_PATH,
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confidence_threshold=0.5,
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device="cpu",
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)
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st.session_state["last_spinner_texts"] = SpinnerTexts()
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if "output_1" not in st.session_state:
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st.session_state["output_1"] = Image.open("highway2-
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if "output_2" not in st.session_state:
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st.session_state["output_2"] = Image.open("highway2-sahi.jpg")
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overlap_ratio = st.number_input(
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"overlap_ratio", min_value=0.0, max_value=0.6, value=0.2, step=0.2
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)
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postprocess_type = st.selectbox(
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)
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postprocess_match_metric = st.selectbox(
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)
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postprocess_match_threshold = st.number_input(
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"postprocess_match_threshold", value=0.5, step=0.1
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)
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with st.spinner(
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text="Performing prediction.. " + st.session_state["last_spinner_texts"].get()
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):
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output_1, output_2 =
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image,
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detection_model,
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image_size=image_size,
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st.session_state["output_1"] = output_1
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st.session_state["output_2"] = output_2
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st.markdown(f"#####
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static_component = image_comparison(
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img1=st.session_state["output_1"],
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img2=st.session_state["output_2"],
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make_responsive=True,
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in_memory=True,
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)
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st.markdown(
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)
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import streamlit as st
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from PIL import Image
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import random
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from sahi.utils.yolov8 import download_yolov8m_model
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from sahi import AutoDetectionModel
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from utils import sahi_yolov8m_inference
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import sahi.utils.file
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from streamlit_image_comparison import image_comparison
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#import sahi.utils.mmdet
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#MMDET_YOLOX_TINY_MODEL_URL = "https://huggingface.co/fcakyon/mmdet-yolox-tiny/resolve/main/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth"
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#MMDET_YOLOX_TINY_MODEL_PATH = "yolox.pt"
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#MMDET_YOLOX_TINY_CONFIG_URL = "https://huggingface.co/fcakyon/mmdet-yolox-tiny/raw/main/yolox_tiny_8x8_300e_coco.py"
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#MMDET_YOLOX_TINY_CONFIG_PATH = "config.py"
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#YOLOV8M_MODEL_URL = "https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt"
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#YOLOV8M_MODEL_PATH = "tests/data/models/yolov8/yolov8m.pt"
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YOLOV8M_MODEL_PATH = 'models/yolov8m.pt'
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IMAGE_TO_URL = {
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"apple_tree.jpg": "https://user-images.githubusercontent.com/34196005/142730935-2ace3999-a47b-49bb-83e0-2bdd509f1c90.jpg",
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"highway.jpg": "https://user-images.githubusercontent.com/34196005/142730936-1b397756-52e5-43be-a949-42ec0134d5d8.jpg",
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"highway2.jpg": "https://user-images.githubusercontent.com/34196005/142742871-bf485f84-0355-43a3-be86-96b44e63c3a2.jpg",
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"highway3.jpg": "https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg",
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"highway2-yolov8m.jpg": "https://user-images.githubusercontent.com/34196005/143309873-c0c1f31c-c42e-4a36-834e-da0a2336bb19.jpg",
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"highway2-sahi.jpg": "https://user-images.githubusercontent.com/34196005/143309867-42841f5a-9181-4d22-b570-65f90f2da231.jpg",
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}
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def download_comparison_images():
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sahi.utils.file.download_from_url(
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"https://user-images.githubusercontent.com/34196005/143309873-c0c1f31c-c42e-4a36-834e-da0a2336bb19.jpg",
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"highway2-yolov8m.jpg",
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)
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sahi.utils.file.download_from_url(
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"https://user-images.githubusercontent.com/34196005/143309867-42841f5a-9181-4d22-b570-65f90f2da231.jpg",
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@st.cache_data(show_spinner=False)
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def get_model():
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#sahi.utils.file.download_from_url(
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# MMDET_YOLOX_TINY_MODEL_URL,
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# MMDET_YOLOX_TINY_MODEL_PATH,
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#)
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#sahi.utils.file.download_from_url(
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# MMDET_YOLOX_TINY_CONFIG_URL,
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# MMDET_YOLOX_TINY_CONFIG_PATH,
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#)
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download_yolov8m_model(destination_path = YOLOV8M_MODEL_PATH)
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detection_model = AutoDetectionModel.from_pretrained(
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model_type='yolov8',
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model_path=YOLOV8M_MODEL_PATH,
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#config_path=MMDET_YOLOX_TINY_CONFIG_PATH,
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confidence_threshold=0.5,
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device="cpu",
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)
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st.session_state["last_spinner_texts"] = SpinnerTexts()
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if "output_1" not in st.session_state:
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st.session_state["output_1"] = Image.open("highway2-yolov8m.jpg")
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if "output_2" not in st.session_state:
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st.session_state["output_2"] = Image.open("highway2-sahi.jpg")
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overlap_ratio = st.number_input(
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"overlap_ratio", min_value=0.0, max_value=0.6, value=0.2, step=0.2
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)
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#postprocess_type = st.selectbox(
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# "postprocess_type", options=["NMS", "GREEDYNMM"], index=0
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#)
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#postprocess_match_metric = st.selectbox(
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# "postprocess_match_metric", options=["IOU", "IOS"], index=0
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#)
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postprocess_match_threshold = st.number_input(
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"postprocess_match_threshold", value=0.5, step=0.1
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)
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with st.spinner(
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text="Performing prediction.. " + st.session_state["last_spinner_texts"].get()
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):
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output_1, output_2 = sahi_yolov8m_inference(
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image,
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detection_model,
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image_size=image_size,
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st.session_state["output_1"] = output_1
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st.session_state["output_2"] = output_2
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st.markdown(f"##### YOLOv8 Standard vs SAHI Prediction:")
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static_component = image_comparison(
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img1=st.session_state["output_1"],
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img2=st.session_state["output_2"],
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make_responsive=True,
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in_memory=True,
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)
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# st.markdown(
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# """
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# <p style='text-align: center'>
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# prepared with <a href='https://github.com/fcakyon/streamlit-image-comparison' target='_blank'>streamlit-image-comparison</a>
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# </p>
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# """,
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# unsafe_allow_html=True,
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# )
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requirements.txt
CHANGED
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-f https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html
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torch==1.12.1+cpu
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torchvision==0.13.1+cpu
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sahi==0.11.
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mmdet==2.25.2
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mmcv-full==1.6.1
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streamlit-image-comparison==0.0.4
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streamlit==1.22.0
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-f https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html
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torch==1.12.1+cpu
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torchvision==0.13.1+cpu
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sahi==0.11.14
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mmdet==2.25.2
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mmcv-full==1.6.1
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streamlit-image-comparison==0.0.4
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streamlit==1.22.0
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ultralyticsplus==0.0.14
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utils.py
CHANGED
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TEMP_DIR = "temp"
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def
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image,
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detection_model,
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slice_height=512,
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overlap_height_ratio=0.2,
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overlap_width_ratio=0.2,
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image_size=640,
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postprocess_type="GREEDYNMM",
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postprocess_match_metric="IOS",
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postprocess_match_threshold=0.5,
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postprocess_class_agnostic=False,
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):
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# standard inference
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slice_width=slice_width,
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overlap_height_ratio=overlap_height_ratio,
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overlap_width_ratio=overlap_width_ratio,
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postprocess_type=postprocess_type,
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postprocess_match_metric=postprocess_match_metric,
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postprocess_match_threshold=postprocess_match_threshold,
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postprocess_class_agnostic=postprocess_class_agnostic,
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)
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visual_result_2 = sahi.utils.cv.visualize_object_predictions(
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image=numpy.array(image),
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TEMP_DIR = "temp"
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def sahi_yolov8m_inference(
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image,
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detection_model,
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slice_height=512,
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overlap_height_ratio=0.2,
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overlap_width_ratio=0.2,
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image_size=640,
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#postprocess_type="GREEDYNMM",
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#postprocess_match_metric="IOS",
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postprocess_match_threshold=0.5,
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#postprocess_class_agnostic=False,
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):
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# standard inference
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slice_width=slice_width,
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overlap_height_ratio=overlap_height_ratio,
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overlap_width_ratio=overlap_width_ratio,
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#postprocess_type=postprocess_type,
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#postprocess_match_metric=postprocess_match_metric,
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postprocess_match_threshold=postprocess_match_threshold,
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#postprocess_class_agnostic=postprocess_class_agnostic,
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)
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visual_result_2 = sahi.utils.cv.visualize_object_predictions(
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image=numpy.array(image),
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