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| import streamlit as st | |
| import sahi.utils.mmdet | |
| import sahi.model | |
| import sahi.predict | |
| from PIL import Image | |
| import numpy | |
| MMDET_YOLACT_MODEL_URL = "https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco/yolact_r50_1x8_coco_20200908-f38d58df.pth" | |
| 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" | |
| MMDET_FASTERRCNN_MODEL_URL = "https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth" | |
| # Images | |
| sahi.utils.file.download_from_url( | |
| "https://user-images.githubusercontent.com/34196005/142730935-2ace3999-a47b-49bb-83e0-2bdd509f1c90.jpg", | |
| "apple_tree.jpg", | |
| ) | |
| sahi.utils.file.download_from_url( | |
| "https://user-images.githubusercontent.com/34196005/142730936-1b397756-52e5-43be-a949-42ec0134d5d8.jpg", | |
| "highway.jpg", | |
| ) | |
| sahi.utils.file.download_from_url( | |
| "https://user-images.githubusercontent.com/34196005/142742871-bf485f84-0355-43a3-be86-96b44e63c3a2.jpg", | |
| "highway2.jpg", | |
| ) | |
| sahi.utils.file.download_from_url( | |
| "https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg", | |
| "highway3.jpg", | |
| ) | |
| def get_mmdet_model(model_name: str): | |
| if model_name == "yolact": | |
| model_path = "yolact.pt" | |
| sahi.utils.file.download_from_url( | |
| MMDET_YOLACT_MODEL_URL, | |
| model_path, | |
| ) | |
| config_path = sahi.utils.mmdet.download_mmdet_config( | |
| model_name="yolact", config_file_name="yolact_r50_1x8_coco.py" | |
| ) | |
| elif model_name == "yolox": | |
| model_path = "yolox.pt" | |
| sahi.utils.file.download_from_url( | |
| MMDET_YOLOX_MODEL_URL, | |
| model_path, | |
| ) | |
| config_path = sahi.utils.mmdet.download_mmdet_config( | |
| model_name="yolox", config_file_name="yolox_tiny_8x8_300e_coco.py" | |
| ) | |
| elif model_name == "fasterrcnn": | |
| model_path = "fasterrcnn.pt" | |
| sahi.utils.file.download_from_url( | |
| MMDET_FASTERRCNN_MODEL_URL, | |
| model_path, | |
| ) | |
| config_path = sahi.utils.mmdet.download_mmdet_config( | |
| model_name="faster_rcnn", config_file_name="faster_rcnn_r50_fpn_2x_coco.py" | |
| ) | |
| detection_model = sahi.model.MmdetDetectionModel( | |
| model_path=model_path, | |
| config_path=config_path, | |
| confidence_threshold=0.4, | |
| device="cpu", | |
| ) | |
| return detection_model | |
| def sahi_mmdet_inference( | |
| image, | |
| detection_model, | |
| slice_height=512, | |
| slice_width=512, | |
| overlap_height_ratio=0.2, | |
| overlap_width_ratio=0.2, | |
| image_size=640, | |
| postprocess_type="UNIONMERGE", | |
| postprocess_match_metric="IOS", | |
| postprocess_match_threshold=0.5, | |
| postprocess_class_agnostic=False, | |
| ): | |
| # standard inference | |
| prediction_result_1 = sahi.predict.get_prediction( | |
| image=image, detection_model=detection_model, image_size=image_size | |
| ) | |
| visual_result_1 = sahi.utils.cv.visualize_object_predictions( | |
| image=numpy.array(image), | |
| object_prediction_list=prediction_result_1.object_prediction_list, | |
| ) | |
| output_1 = Image.fromarray(visual_result_1["image"]) | |
| # sliced inference | |
| prediction_result_2 = sahi.predict.get_sliced_prediction( | |
| image=image, | |
| detection_model=detection_model, | |
| image_size=image_size, | |
| slice_height=slice_height, | |
| slice_width=slice_width, | |
| overlap_height_ratio=overlap_height_ratio, | |
| overlap_width_ratio=overlap_width_ratio, | |
| postprocess_type=postprocess_type, | |
| postprocess_match_metric=postprocess_match_metric, | |
| postprocess_match_threshold=postprocess_match_threshold, | |
| postprocess_class_agnostic=postprocess_class_agnostic, | |
| ) | |
| visual_result_2 = sahi.utils.cv.visualize_object_predictions( | |
| image=numpy.array(image), | |
| object_prediction_list=prediction_result_2.object_prediction_list, | |
| ) | |
| output_2 = Image.fromarray(visual_result_2["image"]) | |
| return output_1, output_2 | |
| st.set_page_config( | |
| page_title="SAHI + MMDetection Demo", | |
| page_icon="", | |
| layout="centered", | |
| initial_sidebar_state="auto", | |
| ) | |
| st.markdown( | |
| "<h2 style='text-align: center'> SAHI + MMDetection Demo </h1>", | |
| unsafe_allow_html=True, | |
| ) | |
| st.markdown( | |
| "<p style='text-align: center'>SAHI is a lightweight vision library for performing large scale object detection/ instance segmentation.. <a href='https://github.com/obss/sahi'>SAHI Github</a> | <a href='https://medium.com/codable/sahi-a-vision-library-for-performing-sliced-inference-on-large-images-small-objects-c8b086af3b80'>SAHI Blog</a> | <a href='https://github.com/fcakyon/yolov5-pip'>YOLOv5 Github</a> </p>", | |
| unsafe_allow_html=True, | |
| ) | |
| st.markdown( | |
| "<h3 style='text-align: center'> Parameters: </h1>", | |
| unsafe_allow_html=True, | |
| ) | |
| col1, col2, col3 = st.columns([6, 1, 6]) | |
| with col1: | |
| image_file = st.file_uploader( | |
| "Upload an image to test:", type=["jpg", "jpeg", "png"] | |
| ) | |
| 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, | |
| ) | |
| image = Image.open(slider) | |
| st.image(image, caption=slider, width=300) | |
| with col3: | |
| model_name = st.selectbox( | |
| "Select MMDetection model:", ("fasterrcnn", "yolact", "yolox") | |
| ) | |
| slice_size = st.number_input("slice_size", 256, value=512, step=256) | |
| overlap_ratio = st.number_input("overlap_ratio", 0.0, 0.6, value=0.2, step=0.2) | |
| postprocess_type = st.selectbox( | |
| "postprocess_type", options=["NMS", "UNIONMERGE"], index=1 | |
| ) | |
| postprocess_match_metric = st.selectbox( | |
| "postprocess_match_metric", options=["IOU", "IOS"], index=1 | |
| ) | |
| 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([6, 1, 6]) | |
| with col2: | |
| submit = st.button("Submit") | |
| if image_file is not None: | |
| image = Image.open(image_file) | |
| else: | |
| image = Image.open(slider) | |
| if submit: | |
| # perform prediction | |
| st.markdown( | |
| "<h3 style='text-align: center'> Results: </h1>", | |
| unsafe_allow_html=True, | |
| ) | |
| with st.spinner(text="Downloading model weight.."): | |
| detection_model = get_mmdet_model(model_name) | |
| if model_name == "yolox": | |
| image_size = 416 | |
| else: | |
| image_size = 640 | |
| with st.spinner( | |
| text="Performing prediction.. Meanwhile check out [other features of SAHI](https://github.com/obss/sahi/blob/main/README.md)!" | |
| ): | |
| 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, | |
| ) | |
| st.markdown(f"##### Standard {model_name} Prediction:") | |
| st.image(output_1, width=700) | |
| st.markdown(f"##### Sliced {model_name} Prediction:") | |
| st.image(output_2, width=700) | |