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Running
Running
fcakyon
commited on
Commit
•
c30a8ce
1
Parent(s):
a76fe61
add sliding image comparator
Browse files
app.py
CHANGED
@@ -1,11 +1,12 @@
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import streamlit as st
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import sahi.utils.mmdet
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import sahi.model
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import sahi.predict
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from PIL import Image
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import numpy
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import random
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-
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MMDET_YOLACT_MODEL_URL = "https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco/yolact_r50_1x8_coco_20200908-f38d58df.pth"
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MMDET_YOLOX_MODEL_URL = "https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20210806_234250-4ff3b67e.pth"
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@@ -71,79 +72,39 @@ def get_mmdet_model(model_name: str):
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return detection_model
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def sahi_mmdet_inference(
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image,
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detection_model,
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slice_height=512,
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slice_width=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="UNIONMERGE",
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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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prediction_result_1 = sahi.predict.get_prediction(
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image=image, detection_model=detection_model, image_size=image_size
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)
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visual_result_1 = sahi.utils.cv.visualize_object_predictions(
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image=numpy.array(image),
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object_prediction_list=prediction_result_1.object_prediction_list,
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)
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output_1 = Image.fromarray(visual_result_1["image"])
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# sliced inference
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prediction_result_2 = sahi.predict.get_sliced_prediction(
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image=image,
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detection_model=detection_model,
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image_size=image_size,
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slice_height=slice_height,
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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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object_prediction_list=prediction_result_2.object_prediction_list,
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)
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output_2 = Image.fromarray(visual_result_2["image"])
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return output_1, output_2
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st.set_page_config(
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page_title="Small Object Detection with SAHI +
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page_icon="",
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layout="centered",
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initial_sidebar_state="auto",
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)
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st.markdown(
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"""
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Small Object Detection <br />
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with SAHI +
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</h2>
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unsafe_allow_html=True,
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)
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st.markdown(
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"
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unsafe_allow_html=True,
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)
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st.
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unsafe_allow_html=True,
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)
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col1, col2, col3 = st.columns([6, 1, 6])
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with col1:
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image_file = st.file_uploader(
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"Upload an image to test:", type=["jpg", "jpeg", "png"]
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)
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@@ -165,11 +126,13 @@ with col1:
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image = Image.open(slider)
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st.image(image, caption=slider, width=300)
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with col3:
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)
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slice_size = st.number_input("slice_size", 256, value=512, step=256)
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overlap_ratio = st.number_input("overlap_ratio", 0.0, 0.6, value=0.2, step=0.2)
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postprocess_type = st.selectbox(
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"postprocess_type", options=["NMS", "UNIONMERGE"], index=1
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)
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@@ -224,11 +187,6 @@ if "last_spinner_texts" not in st.session_state:
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if submit:
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# perform prediction
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st.markdown(
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"<h3 style='text-align: center'> Results: </h1>",
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unsafe_allow_html=True,
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)
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with st.spinner(
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text="Downloading model weight.. "
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+ st.session_state["last_spinner_texts"].get()
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@@ -257,7 +215,14 @@ if submit:
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postprocess_class_agnostic=postprocess_class_agnostic,
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)
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st.markdown(f"##### Standard
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import streamlit as st
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import sahi.utils.mmdet
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import sahi.model
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from PIL import Image
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import random
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from utils import imagecompare
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from utils import sahi_mmdet_inference
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import pathlib
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import os
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MMDET_YOLACT_MODEL_URL = "https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco/yolact_r50_1x8_coco_20200908-f38d58df.pth"
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MMDET_YOLOX_MODEL_URL = "https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20210806_234250-4ff3b67e.pth"
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return detection_model
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st.set_page_config(
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page_title="Small Object Detection with SAHI + YOLOX",
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page_icon="🚀",
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layout="centered",
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initial_sidebar_state="auto",
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)
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st.markdown(
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"""
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<h2 style='text-align: center'>
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Small Object Detection <br />
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with SAHI + YOLOX
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</h2>
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""",
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unsafe_allow_html=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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<a href='https://github.com/obss/sahi'>SAHI Github</a> | <a href='https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox'>YOLOX Github</a> | <a href='https://huggingface.co/spaces/fcakyon/sahi-yolov5'>SAHI+YOLOv5 Demo</a>
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<br />
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Follow me on <a href='https://twitter.com/fcakyon'>twitter</a>, <a href='https://www.linkedin.com/in/fcakyon/'>linkedin</a> and <a href='https://fcakyon.medium.com/'>medium</a> for more..
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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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st.write("##")
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col1, col2, col3 = st.columns([6, 1, 6])
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with col1:
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st.markdown(f"##### Set input image:")
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image_file = st.file_uploader(
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"Upload an image to test:", type=["jpg", "jpeg", "png"]
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)
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image = Image.open(slider)
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st.image(image, caption=slider, width=300)
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with col3:
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st.markdown(f"##### Set SAHI parameters:")
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model_name = "yolox"
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slice_size = st.number_input("slice_size", min_value=256, value=512, step=256)
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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", "UNIONMERGE"], index=1
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)
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if submit:
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# perform prediction
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with st.spinner(
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text="Downloading model weight.. "
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+ st.session_state["last_spinner_texts"].get()
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postprocess_class_agnostic=postprocess_class_agnostic,
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)
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st.markdown(f"##### YOLOX Standard vs SAHI Prediction:")
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imagecompare(
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output_1,
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output_2,
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label1="YOLOX",
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label2="SAHI+YOLOX",
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width=700,
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starting_position=50,
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show_labels=True,
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make_responsive=True,
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)
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utils.py
ADDED
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import streamlit.components.v1 as components
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import streamlit as st
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import numpy
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import sahi.predict
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import sahi.utils
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from PIL import Image
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import pathlib
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import os
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import uuid
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STREAMLIT_STATIC_PATH = pathlib.Path(st.__path__[0]) / "static"
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def sahi_mmdet_inference(
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image,
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detection_model,
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slice_height=512,
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slice_width=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="UNIONMERGE",
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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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prediction_result_1 = sahi.predict.get_prediction(
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image=image, detection_model=detection_model, image_size=image_size
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)
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visual_result_1 = sahi.utils.cv.visualize_object_predictions(
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image=numpy.array(image),
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object_prediction_list=prediction_result_1.object_prediction_list,
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)
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output_1 = Image.fromarray(visual_result_1["image"])
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# sliced inference
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prediction_result_2 = sahi.predict.get_sliced_prediction(
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image=image,
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detection_model=detection_model,
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image_size=image_size,
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slice_height=slice_height,
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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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object_prediction_list=prediction_result_2.object_prediction_list,
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)
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output_2 = Image.fromarray(visual_result_2["image"])
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return output_1, output_2
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def imagecompare(
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img1: str,
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img2: str,
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label1: str = "1",
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label2: str = "2",
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width: int = 700,
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show_labels: bool = True,
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starting_position: int = 50,
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make_responsive: bool = True,
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):
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"""Create a new juxtapose component.
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Parameters
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----------
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img1: str, PosixPath, PIL.Image or URL
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Input image to compare
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img2: str, PosixPath, PIL.Image or URL
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Input image to compare
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label1: str or None
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Label for image 1
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label2: str or None
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Label for image 2
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width: int or None
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Width of the component in px
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show_labels: bool or None
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Show given labels on images
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starting_position: int or None
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Starting position of the slider as percent (0-100)
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make_responsive: bool or None
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Enable responsive mode
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Returns
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-------
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static_component: Boolean
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Returns a static component with a timeline
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"""
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# prepare images
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for file_ in os.listdir(STREAMLIT_STATIC_PATH):
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if file_.endswith(".png") and "favicon" not in file_:
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os.remove(str(STREAMLIT_STATIC_PATH / file_))
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image_1_name = str(uuid.uuid4()) + ".png"
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image_1_path = STREAMLIT_STATIC_PATH / image_1_name
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image_1_path = str(image_1_path.resolve())
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sahi.utils.cv.read_image_as_pil(img1).save(image_1_path)
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image_2_name = str(uuid.uuid4()) + ".png"
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image_2_path = STREAMLIT_STATIC_PATH / image_2_name
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image_2_path = str(image_2_path.resolve())
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sahi.utils.cv.read_image_as_pil(img2).save(image_2_path)
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img_width, img_height = img1.size
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h_to_w = img_height / img_width
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height = width * h_to_w - 20
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# load css + js
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cdn_path = "https://cdn.knightlab.com/libs/juxtapose/latest"
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css_block = f'<link rel="stylesheet" href="{cdn_path}/css/juxtapose.css">'
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js_block = f'<script src="{cdn_path}/js/juxtapose.min.js"></script>'
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# write html block
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htmlcode = f"""
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{css_block}
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{js_block}
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<div id="foo"style="height: '%100'; width: {width or '%100'};"></div>
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<script>
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slider = new juxtapose.JXSlider('#foo',
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[
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{{
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129 |
+
src: '{image_1_name}',
|
130 |
+
label: '{label1}',
|
131 |
+
}},
|
132 |
+
{{
|
133 |
+
src: '{image_2_name}',
|
134 |
+
label: '{label2}',
|
135 |
+
}}
|
136 |
+
],
|
137 |
+
{{
|
138 |
+
animate: true,
|
139 |
+
showLabels: {'true' if show_labels else 'false'},
|
140 |
+
showCredits: true,
|
141 |
+
startingPosition: "{starting_position}%",
|
142 |
+
makeResponsive: {'true' if make_responsive else 'false'},
|
143 |
+
}});
|
144 |
+
</script>
|
145 |
+
"""
|
146 |
+
static_component = components.html(htmlcode, height=height, width=width)
|
147 |
+
|
148 |
+
return static_component
|