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from PIL.ImageDraw import ImageDraw |
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from pyrception_utils import PyrceptionDataset |
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st.set_page_config(layout="wide") |
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#--------------------------------Custom component----------------------------------------------------------------------- |
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import streamlit.components.v1 as components |
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root_dir = os.path.dirname(os.path.abspath(__file__)) |
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build_dir = os.path.join(root_dir, "slider/build") |
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_discrete_slider = components.declare_component( |
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"discrete_slider", |
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path=build_dir |
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) |
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def discrete_slider(greeting, name, key,default=0): |
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return _discrete_slider(greeting=greeting, name=name, default=default, key=key) |
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#-------------------------------------END------------------------------------------------------------------------------- |
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def list_datasets(path) -> List: |
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""" |
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:param base_dataset_dir: The directory that contains the perceptions datasets. |
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:type str: |
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""" |
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st.markdown("# Synthetic Dataset Preview\n ## Unity Technologies ") |
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#st.markdown("# Synthetic Dataset Preview\n ## Unity Technologies ") |
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num_rows = 5 |
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num_cols = 3 |
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classes = dataset.classes |
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st.sidebar.selectbox( |
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"hello", classes |
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) |
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image_index = frame_selector_ui(dataset) |
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image, segmentation, target = dataset[image_index] |
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labels = target["labels"] |
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boxes = target["boxes"] |
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#classes = dataset.classes |
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#st.sidebar.selectbox( |
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# "hello", classes |
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#) |
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#image_index = frame_selector_ui(dataset) |
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#image, segmentation, target = dataset[image_index] |
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#labels = target["labels"] |
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#boxes = target["boxes"] |
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#st.image(image, use_column_width=True) |
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#draw_image_with_boxes( |
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# image, classes, labels, boxes, colors, "Bounding Boxes Preview", "" |
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#) |
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image = draw_image_with_semantic_segmentation( |
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image, dataset.metadata.image_size[0], dataset.metadata.image_size[1], segmentation, "Semantic Segmentation Preview", "" |
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) |
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image = draw_image_with_boxes( |
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image, classes, labels, boxes, colors, "Bounding Boxes Preview", "" |
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) |
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st.image(image, use_column_width=True) |
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#image = draw_image_with_semantic_segmentation( |
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# image, dataset.metadata.image_size[0], dataset.metadata.image_size[1], segmentation, "Semantic Segmentation Preview", "" |
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#) |
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#image = draw_image_with_boxes( |
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# image, classes, labels, boxes, colors, "Bounding Boxes Preview", "" |
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#) |
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grid_view(num_cols, num_rows, colors, dataset) |
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def sidebar(): |
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return None |
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def navbar(): |
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return None |
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def grid_view(num_cols, num_rows, colors, dataset): |
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print("Now did I create the slider?") |
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count_of_clicks = discrete_slider("Hello", "Leopoldo", "123") |
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st.write("Return value: ", count_of_clicks) |
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inner_cols = st.beta_columns([0.1, 0.0001]) |
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cols = st.beta_columns(num_cols) |
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semantic_segmentation = st.sidebar.checkbox("Semantic Segmentation", key="ss") |
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bounding_boxes_2d = st.sidebar.checkbox("Bounding Boxes", key="bb2d") |
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app_state = st.experimental_get_query_params() |
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if "start_at" in app_state: |
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start_at = int(app_state["start_at"][0]) |
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else: |
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start_at = 0 |
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if inner_cols[1].button('>'): |
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start_at = min(start_at + num_cols * num_rows, len(dataset)-(len(dataset) % (num_cols * num_rows))) |
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if inner_cols[0].button('<'): |
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start_at = max(0,start_at - num_cols * num_rows) |
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st.experimental_set_query_params(start_at=start_at) |
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for i in range(start_at, min(start_at + (num_cols * num_rows), len(dataset))): |
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classes = dataset.classes |
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image, segmentation, target = dataset[i] |
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labels = target["labels"] |
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boxes = target["boxes"] |
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if semantic_segmentation: |
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image = draw_image_with_semantic_segmentation( |
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image, dataset.metadata.image_size[0], dataset.metadata.image_size[1], segmentation, "Semantic Segmentation Preview", "" |
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) |
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if bounding_boxes_2d: |
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image = draw_image_with_boxes( |
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image, classes, labels, boxes, colors, "Bounding Boxes Preview", "" |
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) |
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cols[i % num_cols].image(image, caption=str(i), use_column_width = True) |
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def zoom(index): |
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return None |
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def preview_app(args): |
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""" |
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raise ValueError("Please specify the path to the main dataset directory!") |
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preview_app(args) |
leopoldo-zugasti
4 年前