Support CPU
Browse files
app.py
CHANGED
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import os
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from unittest.mock import patch
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import spaces
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import gradio as gr
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from transformers import AutoProcessor, AutoModelForCausalLM
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import requests
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import
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import cv2
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import io
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if not str(filename).endswith("/modeling_florence2.py"):
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return get_imports(filename)
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imports = get_imports(filename)
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imports.remove("flash_attn")
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return imports
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processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large-ft", trust_remote_code=True)
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colormap = ['blue',
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'lime',
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def fig_to_pil(fig):
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buf = io.BytesIO()
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prompt = task_prompt
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else:
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prompt = task_prompt + text_input
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inputs = processor(text=prompt, images=image, return_tensors="pt").to(
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)
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
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parsed_answer = processor.post_process_generation(
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generated_text,
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task=task_prompt,
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image_size=(image.
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)
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return parsed_answer
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x1, y1, x2, y2 = bbox
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rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
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ax.add_patch(rect)
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plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='
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ax.axis('off')
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return
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def draw_polygons(image, prediction, fill_mask=False):
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ax.imshow(image)
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scale = 1
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for polygons, label in zip(prediction['polygons'], prediction['labels']):
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color = random.choice(colormap)
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fill_color = random.choice(colormap) if fill_mask else None
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for _polygon in polygons:
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_polygon = np.array(_polygon).reshape(-1, 2)
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if _polygon
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print('Invalid polygon:', _polygon)
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continue
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_polygon = (_polygon * scale).reshape(-1).tolist()
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if len(_polygon) % 2 != 0:
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print('Invalid polygon:', _polygon)
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continue
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polygon_points = np.array(_polygon).reshape(-1, 2)
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if fill_mask:
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polygon
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else:
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polygon
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def draw_ocr_bboxes(image, prediction):
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fig, ax = plt.subplots()
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ax.imshow(image)
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scale = 1
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bboxes, labels = prediction['quad_boxes'], prediction['labels']
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for box, label in zip(bboxes, labels):
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color = random.choice(colormap)
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new_box = (np.array(box) * scale).tolist()
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polygon
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return
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css = """
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#output {
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overflow: auto;
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border: 1px solid #ccc;
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}
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"""
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with gr.Blocks(css=css) as demo:
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gr.
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with gr.Tab(label="Image"):
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with gr.Row():
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with gr.Column():
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input_img = gr.Image(label="Input Picture"
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)
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text_input = gr.Textbox(label="Text Input (
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submit_btn = gr.Button(value="Submit")
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with gr.Column():
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output_text = gr.Textbox(label="
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input_video = gr.Video(label="Video")
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video_task_radio = gr.Radio(
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["Object Detection", "Dense Region Caption"],
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label="Video Task", value="Object Detection"
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)
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video_submit_btn = gr.Button(value="Process Video")
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with gr.Column():
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output_video = gr.Video(label="Video")
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def update_text_input(task):
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return gr.update(visible=task in ["Caption to Phrase Grounding", "Referring Expression Segmentation",
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"Region to Segmentation", "Open Vocabulary Detection", "Region to Category",
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"Region to Description"])
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task_radio.change(fn=update_text_input, inputs=task_radio, outputs=text_input)
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def process_image(image, task, text):
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task_mapping = {
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"Caption": ("<CAPTION>", lambda result: (result['<CAPTION>'], image)),
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"Detailed Caption": ("<DETAILED_CAPTION>", lambda result: (result['<DETAILED_CAPTION>'], image)),
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"More Detailed Caption": ("<MORE_DETAILED_CAPTION>", lambda result: (result['<MORE_DETAILED_CAPTION>'], image)),
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"Caption to Phrase Grounding": ("<CAPTION_TO_PHRASE_GROUNDING>", lambda result: (str(result['<CAPTION_TO_PHRASE_GROUNDING>']), plot_bbox(image, result['<CAPTION_TO_PHRASE_GROUNDING>']))),
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"Object Detection": ("<OD>", lambda result: (str(result['<OD>']), plot_bbox(image, result['<OD>']))),
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"Dense Region Caption": ("<DENSE_REGION_CAPTION>", lambda result: (str(result['<DENSE_REGION_CAPTION>']), plot_bbox(image, result['<DENSE_REGION_CAPTION>']))),
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"Region Proposal": ("<REGION_PROPOSAL>", lambda result: (str(result['<REGION_PROPOSAL>']), plot_bbox(image, result['<REGION_PROPOSAL>']))),
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"Referring Expression Segmentation": ("<REFERRING_EXPRESSION_SEGMENTATION>", lambda result: (str(result['<REFERRING_EXPRESSION_SEGMENTATION>']), draw_polygons(image, result['<REFERRING_EXPRESSION_SEGMENTATION>'], fill_mask=True))),
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"Region to Segmentation": ("<REGION_TO_SEGMENTATION>", lambda result: (str(result['<REGION_TO_SEGMENTATION>']), draw_polygons(image, result['<REGION_TO_SEGMENTATION>'], fill_mask=True))),
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"Open Vocabulary Detection": ("<OPEN_VOCABULARY_DETECTION>", lambda result: (str(convert_to_od_format(result['<OPEN_VOCABULARY_DETECTION>'])), plot_bbox(image, convert_to_od_format(result['<OPEN_VOCABULARY_DETECTION>'])))),
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"Region to Category": ("<REGION_TO_CATEGORY>", lambda result: (result['<REGION_TO_CATEGORY>'], image)),
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"Region to Description": ("<REGION_TO_DESCRIPTION>", lambda result: (result['<REGION_TO_DESCRIPTION>'], image)),
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"OCR": ("<OCR>", lambda result: (result['<OCR>'], image)),
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"OCR with Region": ("<OCR_WITH_REGION>", lambda result: (str(result['<OCR_WITH_REGION>']), draw_ocr_bboxes(image, result['<OCR_WITH_REGION>']))),
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}
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if task in task_mapping:
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prompt, process_func = task_mapping[task]
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result = run_example(prompt, image, text)
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return process_func(result)
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else:
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return "", image
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submit_btn.click(fn=process_image, inputs=[input_img, task_radio, text_input], outputs=[output_text, output_image])
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video_submit_btn.click(fn=process_video, inputs=[input_video, video_task_radio], outputs=output_video)
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demo.launch()
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import gradio as gr
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from transformers import AutoProcessor, AutoModelForCausalLM
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import spaces
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import requests
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import copy
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from PIL import Image, ImageDraw, ImageFont
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import io
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import random
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import numpy as np
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import os
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from unittest.mock import patch
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from transformers import AutoModelForCausalLM, AutoProcessor
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from transformers.dynamic_module_utils import get_imports
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def fixed_get_imports(filename: str | os.PathLike) -> list[str]:
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"""Work around for https://huggingface.co/microsoft/phi-1_5/discussions/72."""
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if not str(filename).endswith("/modeling_florence2.py"):
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return get_imports(filename)
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imports = get_imports(filename)
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imports.remove("flash_attn")
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return imports
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@spaces.GPU
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def get_device_type():
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import torch
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return "cuda" if torch.cuda.is_available() else "cpu"
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model_id = 'microsoft/Florence-2-base-ft'
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import subprocess
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device = get_device_type()
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if (device == "cuda"):
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subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
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model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large-ft", trust_remote_code=True)
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processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large-ft", trust_remote_code=True)
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else:
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#https://huggingface.co/microsoft/Florence-2-large-ft/discussions/4
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with patch("transformers.dynamic_module_utils.get_imports", fixed_get_imports):
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model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large-ft", trust_remote_code=True)
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processor = AutoProcessor.from_pretrained("microsoft/Florence-2-large-ft", trust_remote_code=True)
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DESCRIPTION = "# [Florence-2 base-ft Demo with CPU inference support](https://huggingface.co/microsoft/Florence-2-base-ft)"
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colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red',
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'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue']
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def fig_to_pil(fig):
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buf = io.BytesIO()
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prompt = task_prompt
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else:
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prompt = task_prompt + text_input
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inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
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generated_ids = model.generate(
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input_ids=inputs["input_ids"],
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pixel_values=inputs["pixel_values"],
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max_new_tokens=1024,
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early_stopping=False,
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do_sample=False,
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num_beams=3,
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)
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
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parsed_answer = processor.post_process_generation(
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generated_text,
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task=task_prompt,
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image_size=(image.width, image.height)
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)
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return parsed_answer
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x1, y1, x2, y2 = bbox
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rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
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ax.add_patch(rect)
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plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5))
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ax.axis('off')
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return fig
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def draw_polygons(image, prediction, fill_mask=False):
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draw = ImageDraw.Draw(image)
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scale = 1
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for polygons, label in zip(prediction['polygons'], prediction['labels']):
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color = random.choice(colormap)
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fill_color = random.choice(colormap) if fill_mask else None
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for _polygon in polygons:
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_polygon = np.array(_polygon).reshape(-1, 2)
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if len(_polygon) < 3:
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print('Invalid polygon:', _polygon)
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continue
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_polygon = (_polygon * scale).reshape(-1).tolist()
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if fill_mask:
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draw.polygon(_polygon, outline=color, fill=fill_color)
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else:
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draw.polygon(_polygon, outline=color)
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draw.text((_polygon[0] + 8, _polygon[1] + 2), label, fill=color)
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return image
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def convert_to_od_format(data):
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bboxes = data.get('bboxes', [])
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labels = data.get('bboxes_labels', [])
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od_results = {
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'bboxes': bboxes,
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'labels': labels
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}
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return od_results
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def draw_ocr_bboxes(image, prediction):
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scale = 1
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draw = ImageDraw.Draw(image)
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bboxes, labels = prediction['quad_boxes'], prediction['labels']
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for box, label in zip(bboxes, labels):
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color = random.choice(colormap)
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new_box = (np.array(box) * scale).tolist()
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draw.polygon(new_box, width=3, outline=color)
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draw.text((new_box[0]+8, new_box[1]+2),
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"{}".format(label),
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align="right",
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fill=color)
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return image
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def process_image(image, task_prompt, text_input=None):
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image = Image.fromarray(image) # Convert NumPy array to PIL Image
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if task_prompt == 'Caption':
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task_prompt = '<CAPTION>'
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result = run_example(task_prompt, image)
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return result, None
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elif task_prompt == 'Detailed Caption':
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task_prompt = '<DETAILED_CAPTION>'
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result = run_example(task_prompt, image)
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return result, None
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elif task_prompt == 'More Detailed Caption':
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task_prompt = '<MORE_DETAILED_CAPTION>'
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result = run_example(task_prompt, image)
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return result, None
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elif task_prompt == 'Object Detection':
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task_prompt = '<OD>'
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results = run_example(task_prompt, image)
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fig = plot_bbox(image, results['<OD>'])
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return results, fig_to_pil(fig)
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elif task_prompt == 'Dense Region Caption':
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task_prompt = '<DENSE_REGION_CAPTION>'
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results = run_example(task_prompt, image)
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fig = plot_bbox(image, results['<DENSE_REGION_CAPTION>'])
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return results, fig_to_pil(fig)
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elif task_prompt == 'Region Proposal':
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task_prompt = '<REGION_PROPOSAL>'
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results = run_example(task_prompt, image)
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fig = plot_bbox(image, results['<REGION_PROPOSAL>'])
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return results, fig_to_pil(fig)
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elif task_prompt == 'Caption to Phrase Grounding':
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task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
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results = run_example(task_prompt, image, text_input)
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fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
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return results, fig_to_pil(fig)
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elif task_prompt == 'Referring Expression Segmentation':
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task_prompt = '<REFERRING_EXPRESSION_SEGMENTATION>'
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results = run_example(task_prompt, image, text_input)
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output_image = copy.deepcopy(image)
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output_image = draw_polygons(output_image, results['<REFERRING_EXPRESSION_SEGMENTATION>'], fill_mask=True)
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+
return results, output_image
|
176 |
+
elif task_prompt == 'Region to Segmentation':
|
177 |
+
task_prompt = '<REGION_TO_SEGMENTATION>'
|
178 |
+
results = run_example(task_prompt, image, text_input)
|
179 |
+
output_image = copy.deepcopy(image)
|
180 |
+
output_image = draw_polygons(output_image, results['<REGION_TO_SEGMENTATION>'], fill_mask=True)
|
181 |
+
return results, output_image
|
182 |
+
elif task_prompt == 'Open Vocabulary Detection':
|
183 |
+
task_prompt = '<OPEN_VOCABULARY_DETECTION>'
|
184 |
+
results = run_example(task_prompt, image, text_input)
|
185 |
+
bbox_results = convert_to_od_format(results['<OPEN_VOCABULARY_DETECTION>'])
|
186 |
+
fig = plot_bbox(image, bbox_results)
|
187 |
+
return results, fig_to_pil(fig)
|
188 |
+
elif task_prompt == 'Region to Category':
|
189 |
+
task_prompt = '<REGION_TO_CATEGORY>'
|
190 |
+
results = run_example(task_prompt, image, text_input)
|
191 |
+
return results, None
|
192 |
+
elif task_prompt == 'Region to Description':
|
193 |
+
task_prompt = '<REGION_TO_DESCRIPTION>'
|
194 |
+
results = run_example(task_prompt, image, text_input)
|
195 |
+
return results, None
|
196 |
+
elif task_prompt == 'OCR':
|
197 |
+
task_prompt = '<OCR>'
|
198 |
+
result = run_example(task_prompt, image)
|
199 |
+
return result, None
|
200 |
+
elif task_prompt == 'OCR with Region':
|
201 |
+
task_prompt = '<OCR_WITH_REGION>'
|
202 |
+
results = run_example(task_prompt, image)
|
203 |
+
output_image = copy.deepcopy(image)
|
204 |
+
output_image = draw_ocr_bboxes(output_image, results['<OCR_WITH_REGION>'])
|
205 |
+
return results, output_image
|
206 |
+
else:
|
207 |
+
return "", None # Return empty string and None for unknown task prompts
|
208 |
|
209 |
css = """
|
210 |
+
#output {
|
211 |
+
height: 500px;
|
212 |
+
overflow: auto;
|
213 |
+
border: 1px solid #ccc;
|
214 |
+
}
|
215 |
"""
|
216 |
|
217 |
with gr.Blocks(css=css) as demo:
|
218 |
+
gr.Markdown(DESCRIPTION)
|
219 |
+
with gr.Tab(label="Florence-2 Image Captioning"):
|
220 |
with gr.Row():
|
221 |
with gr.Column():
|
222 |
+
input_img = gr.Image(label="Input Picture")
|
223 |
+
task_prompt = gr.Dropdown(choices=[
|
224 |
+
'Caption', 'Detailed Caption', 'More Detailed Caption', 'Object Detection',
|
225 |
+
'Dense Region Caption', 'Region Proposal', 'Caption to Phrase Grounding',
|
226 |
+
'Referring Expression Segmentation', 'Region to Segmentation',
|
227 |
+
'Open Vocabulary Detection', 'Region to Category', 'Region to Description',
|
228 |
+
'OCR', 'OCR with Region'
|
229 |
+
], label="Task Prompt", value= 'Caption')
|
230 |
+
text_input = gr.Textbox(label="Text Input (optional)")
|
231 |
submit_btn = gr.Button(value="Submit")
|
232 |
with gr.Column():
|
233 |
+
output_text = gr.Textbox(label="Output Text")
|
234 |
+
output_img = gr.Image(label="Output Image")
|
235 |
+
|
236 |
+
gr.Examples(
|
237 |
+
examples=[
|
238 |
+
["image1.jpg", 'Object Detection'],
|
239 |
+
["image2.jpg", 'OCR with Region']
|
240 |
+
],
|
241 |
+
inputs=[input_img, task_prompt],
|
242 |
+
outputs=[output_text, output_img],
|
243 |
+
fn=process_image,
|
244 |
+
cache_examples=True,
|
245 |
+
label='Try examples'
|
246 |
+
)
|
247 |
|
248 |
+
submit_btn.click(process_image, [input_img, task_prompt, text_input], [output_text, output_img])
|
249 |
+
|
250 |
+
demo.launch(debug=True)
|
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