florence-sam2-masking

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jiuface commited on Aug 22

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8b6ee19

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1 Parent(s): 6d7bbcd

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app.py +1 -2

app.py CHANGED Viewed

@@ -168,9 +168,8 @@ with gr.Blocks() as demo:
             image = gr.Image(type='pil', label='Upload image')
             image_url =  gr.Textbox(label='Image url', placeholder='Enter text prompts (Optional)')
             task_prompt = gr.Dropdown(
-                ['<OD>', '<CAPTION_TO_PHRASE_GROUNDING>', '<DENSE_REGION_CAPTION>', '<REGION_PROPOSAL>', '<OCR_WITH_REGION>', '<REFERRING_EXPRESSION_SEGMENTATION>', '<REGION_TO_SEGMENTATION>', '<OPEN_VOCABULARY_DETECTION>', '<REGION_TO_CATEGORY>', '<REGION_TO_DESCRIPTION>'], value="<CAPTION_TO_PHRASE_GROUNDING>", label="Task Prompt", info="task prompts"
             )
-            task_info = gr.Textbox(label='Task Info', value=update_task_info("<CAPTION_TO_PHRASE_GROUNDING>"), interactive=False)
             dilate = gr.Slider(label="dilate mask", minimum=0, maximum=50, value=10, step=1, info="The dilate parameter controls the expansion of the mask's white areas by a specified number of pixels. Increasing this value will enlarge the white regions, which can help in smoothing out the mask's edges or covering more area in the segmentation.")
             merge_masks = gr.Checkbox(label="Merge masks", value=False, info="The merge_masks parameter combines all the individual masks into a single mask. When enabled, the separate masks generated for different objects or regions will be merged into one unified mask, which can simplify further processing or visualization.")
             return_rectangles = gr.Checkbox(label="Return Rectangles", value=False, info="The return_rectangles parameter, when enabled, generates masks as filled white rectangles corresponding to the bounding boxes of detected objects, rather than detailed contours or segments. This option is useful for simpler, box-based visualizations.")

             image = gr.Image(type='pil', label='Upload image')
             image_url =  gr.Textbox(label='Image url', placeholder='Enter text prompts (Optional)')
             task_prompt = gr.Dropdown(
+                ['<OD>', '<CAPTION_TO_PHRASE_GROUNDING>', '<DENSE_REGION_CAPTION>', '<REGION_PROPOSAL>', '<OCR_WITH_REGION>', '<REFERRING_EXPRESSION_SEGMENTATION>', '<REGION_TO_SEGMENTATION>', '<OPEN_VOCABULARY_DETECTION>', '<REGION_TO_CATEGORY>', '<REGION_TO_DESCRIPTION>'], value="<CAPTION_TO_PHRASE_GROUNDING>", label="Task Prompt", info=update_task_info("<CAPTION_TO_PHRASE_GROUNDING>")
             )
             dilate = gr.Slider(label="dilate mask", minimum=0, maximum=50, value=10, step=1, info="The dilate parameter controls the expansion of the mask's white areas by a specified number of pixels. Increasing this value will enlarge the white regions, which can help in smoothing out the mask's edges or covering more area in the segmentation.")
             merge_masks = gr.Checkbox(label="Merge masks", value=False, info="The merge_masks parameter combines all the individual masks into a single mask. When enabled, the separate masks generated for different objects or regions will be merged into one unified mask, which can simplify further processing or visualization.")
             return_rectangles = gr.Checkbox(label="Return Rectangles", value=False, info="The return_rectangles parameter, when enabled, generates masks as filled white rectangles corresponding to the bounding boxes of detected objects, rather than detailed contours or segments. This option is useful for simpler, box-based visualizations.")