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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 subprocess |
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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_id = 'microsoft/Florence-2-large' |
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model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda").eval() |
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processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) |
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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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fig.savefig(buf, format='png') |
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buf.seek(0) |
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return Image.open(buf) |
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@spaces.GPU |
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def run_example(task_prompt, image, text_input=None): |
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if text_input is None: |
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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("cuda") |
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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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def plot_bbox(image, data): |
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fig, ax = plt.subplots() |
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ax.imshow(image) |
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for bbox, label in zip(data['bboxes'], data['labels']): |
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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) |
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if 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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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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result = run_example(task_prompt, image) |
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return result, None |
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elif 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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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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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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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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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 |
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elif task_prompt == '<REGION_TO_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['<REGION_TO_SEGMENTATION>'], fill_mask=True) |
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return results, output_image |
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elif task_prompt == '<OPEN_VOCABULARY_DETECTION>': |
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results = run_example(task_prompt, image, text_input) |
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bbox_results = convert_to_od_format(results['<OPEN_VOCABULARY_DETECTION>']) |
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fig = plot_bbox(image, bbox_results) |
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return results, fig_to_pil(fig) |
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elif task_prompt == '<REGION_TO_CATEGORY>': |
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results = run_example(task_prompt, image, text_input) |
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return results, None |
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elif task_prompt == '<REGION_TO_DESCRIPTION>': |
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results = run_example(task_prompt, image, text_input) |
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return results, None |
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elif task_prompt == '<OCR>': |
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result = run_example(task_prompt, image) |
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return result, None |
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elif task_prompt == '<OCR_WITH_REGION>': |
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results = run_example(task_prompt, image) |
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output_image = copy.deepcopy(image) |
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output_image = draw_ocr_bboxes(output_image, results['<OCR_WITH_REGION>']) |
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return results, output_image |
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else: |
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return "", None |
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css = """ |
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#output { |
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height: 500px; |
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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.HTML("<h1><center>Florence-2 Demo<center><h1>") |
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with gr.Tab(label="Florence-2 Image Captioning"): |
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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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task_prompt = gr.Dropdown(choices=[ |
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'<CAPTION>', '<DETAILED_CAPTION>', '<MORE_DETAILED_CAPTION>', '<OD>', |
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'<DENSE_REGION_CAPTION>', '<REGION_PROPOSAL>', '<CAPTION_TO_PHRASE_GROUNDING>', |
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'<REFERRING_EXPRESSION_SEGMENTATION>', '<REGION_TO_SEGMENTATION>', |
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'<OPEN_VOCABULARY_DETECTION>', '<REGION_TO_CATEGORY>', '<REGION_TO_DESCRIPTION>', |
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'<OCR>', '<OCR_WITH_REGION>' |
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], label="Task Prompt") |
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text_input = gr.Textbox(label="Text Input (optional)") |
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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="Output Text") |
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output_img = gr.Image(label="Output Image") |
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gr.Examples( |
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examples=[ |
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["image1.jpg", '<OD>'], |
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["image2.jpg", '<OCR_WITH_REGION>'] |
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], |
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inputs=[input_img, task_prompt], |
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outputs=[output_text, output_img], |
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fn=process_image, |
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cache_examples=True, |
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label='Try examples' |
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) |
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submit_btn.click(process_image, [input_img, task_prompt, text_input], [output_text, output_img]) |
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demo.launch(debug=True) |
