File size: 12,331 Bytes
ade70cf
1d51385
d69fd19
d7f29ce
ade70cf
1d51385
d7f29ce
d9cf2fe
d7f29ce
ade70cf
 
d7f29ce
ade70cf
1d51385
d7f29ce
39ae23a
 
ade70cf
d502400
 
 
 
 
 
 
 
 
 
 
 
 
ade70cf
d256f3b
6172e67
ca16909
d256f3b
 
 
2f7ee0a
 
 
 
 
69958d1
beec895
d502400
 
 
1d51385
 
 
 
69958d1
ade70cf
 
 
 
 
 
 
 
 
 
1d51385
 
ade70cf
 
 
 
 
 
 
 
 
 
 
 
1d51385
 
ade70cf
 
6172e67
ade70cf
1d51385
ade70cf
 
1d51385
 
 
 
 
 
 
 
 
 
 
 
 
 
6172e67
 
 
 
 
 
 
 
 
1d51385
 
 
 
 
 
 
 
 
 
 
 
 
 
d502400
6172e67
 
 
d502400
 
6172e67
 
d502400
 
6172e67
 
d502400
 
4d69588
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6172e67
 
d502400
6172e67
 
 
 
d502400
6172e67
 
 
 
d502400
6172e67
 
 
 
d502400
6172e67
 
 
 
d502400
6172e67
 
 
 
 
d502400
6172e67
 
 
 
 
d502400
6172e67
 
 
 
 
d502400
6172e67
 
 
d502400
6172e67
 
 
d502400
 
6172e67
 
d502400
6172e67
 
 
 
 
 
 
 
 
 
 
 
 
 
4d69588
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6172e67
 
 
1d51385
6172e67
 
d502400
4d69588
90d93e3
4d69588
6172e67
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d502400
6172e67
4d69588
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM
import spaces

import requests
import copy

from PIL import Image, ImageDraw, ImageFont 
import io
import matplotlib.pyplot as plt
import matplotlib.patches as patches

import random
import numpy as np

import subprocess
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

models = {
    'microsoft/Florence-2-large-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large-ft', trust_remote_code=True).to("cuda").eval(),
    'microsoft/Florence-2-large': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True).to("cuda").eval(),
    'microsoft/Florence-2-base-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True).to("cuda").eval(),
    'microsoft/Florence-2-base': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to("cuda").eval(),
}

processors = {
    'microsoft/Florence-2-large-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-large-ft', trust_remote_code=True),
    'microsoft/Florence-2-large': AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True),
    'microsoft/Florence-2-base-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True),
    'microsoft/Florence-2-base': AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True),
}


DESCRIPTION = "# [Florence-2 Demo](https://huggingface.co/microsoft/Florence-2-large)"

colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red',
            'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue']

def fig_to_pil(fig):
    buf = io.BytesIO()
    fig.savefig(buf, format='png')
    buf.seek(0)
    return Image.open(buf)

@spaces.GPU
def run_example(task_prompt, image, text_input=None, model_id='microsoft/Florence-2-large'):
    model = models[model_id]
    processor = processors[model_id]
    if text_input is None:
        prompt = task_prompt
    else:
        prompt = task_prompt + text_input
    inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
    generated_ids = model.generate(
        input_ids=inputs["input_ids"],
        pixel_values=inputs["pixel_values"],
        max_new_tokens=1024,
        early_stopping=False,
        do_sample=False,
        num_beams=3,
    )
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
    parsed_answer = processor.post_process_generation(
        generated_text,
        task=task_prompt,
        image_size=(image.width, image.height)
    )
    return parsed_answer

def plot_bbox(image, data):
    fig, ax = plt.subplots()
    ax.imshow(image)
    for bbox, label in zip(data['bboxes'], data['labels']):
        x1, y1, x2, y2 = bbox
        rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
        ax.add_patch(rect)
        plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5))
    ax.axis('off')
    return fig

def draw_polygons(image, prediction, fill_mask=False):

    draw = ImageDraw.Draw(image)
    scale = 1
    for polygons, label in zip(prediction['polygons'], prediction['labels']):
        color = random.choice(colormap)
        fill_color = random.choice(colormap) if fill_mask else None
        for _polygon in polygons:
            _polygon = np.array(_polygon).reshape(-1, 2)
            if len(_polygon) < 3:
                print('Invalid polygon:', _polygon)
                continue
            _polygon = (_polygon * scale).reshape(-1).tolist()
            if fill_mask:
                draw.polygon(_polygon, outline=color, fill=fill_color)
            else:
                draw.polygon(_polygon, outline=color)
            draw.text((_polygon[0] + 8, _polygon[1] + 2), label, fill=color)
    return image

def convert_to_od_format(data):
    bboxes = data.get('bboxes', [])
    labels = data.get('bboxes_labels', [])
    od_results = {
        'bboxes': bboxes,
        'labels': labels
    }
    return od_results

def draw_ocr_bboxes(image, prediction):
    scale = 1
    draw = ImageDraw.Draw(image)
    bboxes, labels = prediction['quad_boxes'], prediction['labels']
    for box, label in zip(bboxes, labels):
        color = random.choice(colormap)
        new_box = (np.array(box) * scale).tolist()
        draw.polygon(new_box, width=3, outline=color)
        draw.text((new_box[0]+8, new_box[1]+2),
                  "{}".format(label),
                  align="right",
                  fill=color)
    return image

def process_image(image, task_prompt, text_input=None, model_id='microsoft/Florence-2-large'):
    image = Image.fromarray(image)  # Convert NumPy array to PIL Image
    if task_prompt == 'Caption':
        task_prompt = '<CAPTION>'
        results = run_example(task_prompt, image, model_id=model_id)
        return results, None
    elif task_prompt == 'Detailed Caption':
        task_prompt = '<DETAILED_CAPTION>'
        results = run_example(task_prompt, image, model_id=model_id)
        return results, None
    elif task_prompt == 'More Detailed Caption':
        task_prompt = '<MORE_DETAILED_CAPTION>'
        results = run_example(task_prompt, image, model_id=model_id)
        return results, None
    elif task_prompt == 'Caption + Grounding':
        task_prompt = '<CAPTION>'
        results = run_example(task_prompt, image, model_id=model_id)
        text_input = results[task_prompt]
        task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
        results = run_example(task_prompt, image, text_input, model_id)
        results['<CAPTION>'] = text_input
        fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
        return results, fig_to_pil(fig)
    elif task_prompt == 'Detailed Caption + Grounding':
        task_prompt = '<DETAILED_CAPTION>'
        results = run_example(task_prompt, image, model_id=model_id)
        text_input = results[task_prompt]
        task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
        results = run_example(task_prompt, image, text_input, model_id)
        results['<DETAILED_CAPTION>'] = text_input
        fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
        return results, fig_to_pil(fig)
    elif task_prompt == 'More Detailed Caption + Grounding':
        task_prompt = '<MORE_DETAILED_CAPTION>'
        results = run_example(task_prompt, image, model_id=model_id)
        text_input = results[task_prompt]
        task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
        results = run_example(task_prompt, image, text_input, model_id)
        results['<MORE_DETAILED_CAPTION>'] = text_input
        fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
        return results, fig_to_pil(fig)
    elif task_prompt == 'Object Detection':
        task_prompt = '<OD>'
        results = run_example(task_prompt, image, model_id=model_id)
        fig = plot_bbox(image, results['<OD>'])
        return results, fig_to_pil(fig)
    elif task_prompt == 'Dense Region Caption':
        task_prompt = '<DENSE_REGION_CAPTION>'
        results = run_example(task_prompt, image, model_id=model_id)
        fig = plot_bbox(image, results['<DENSE_REGION_CAPTION>'])
        return results, fig_to_pil(fig)
    elif task_prompt == 'Region Proposal':
        task_prompt = '<REGION_PROPOSAL>'
        results = run_example(task_prompt, image, model_id=model_id)
        fig = plot_bbox(image, results['<REGION_PROPOSAL>'])
        return results, fig_to_pil(fig)
    elif task_prompt == 'Caption to Phrase Grounding':
        task_prompt = '<CAPTION_TO_PHRASE_GROUNDING>'
        results = run_example(task_prompt, image, text_input, model_id)
        fig = plot_bbox(image, results['<CAPTION_TO_PHRASE_GROUNDING>'])
        return results, fig_to_pil(fig)
    elif task_prompt == 'Referring Expression Segmentation':
        task_prompt = '<REFERRING_EXPRESSION_SEGMENTATION>'
        results = run_example(task_prompt, image, text_input, model_id)
        output_image = copy.deepcopy(image)
        output_image = draw_polygons(output_image, results['<REFERRING_EXPRESSION_SEGMENTATION>'], fill_mask=True)
        return results, output_image
    elif task_prompt == 'Region to Segmentation':
        task_prompt = '<REGION_TO_SEGMENTATION>'
        results = run_example(task_prompt, image, text_input, model_id)
        output_image = copy.deepcopy(image)
        output_image = draw_polygons(output_image, results['<REGION_TO_SEGMENTATION>'], fill_mask=True)
        return results, output_image
    elif task_prompt == 'Open Vocabulary Detection':
        task_prompt = '<OPEN_VOCABULARY_DETECTION>'
        results = run_example(task_prompt, image, text_input, model_id)
        bbox_results = convert_to_od_format(results['<OPEN_VOCABULARY_DETECTION>'])
        fig = plot_bbox(image, bbox_results)
        return results, fig_to_pil(fig)
    elif task_prompt == 'Region to Category':
        task_prompt = '<REGION_TO_CATEGORY>'
        results = run_example(task_prompt, image, text_input, model_id)
        return results, None
    elif task_prompt == 'Region to Description':
        task_prompt = '<REGION_TO_DESCRIPTION>'
        results = run_example(task_prompt, image, text_input, model_id)
        return results, None
    elif task_prompt == 'OCR':
        task_prompt = '<OCR>'
        results = run_example(task_prompt, image, model_id=model_id)
        return results, None
    elif task_prompt == 'OCR with Region':
        task_prompt = '<OCR_WITH_REGION>'
        results = run_example(task_prompt, image, model_id=model_id)
        output_image = copy.deepcopy(image)
        output_image = draw_ocr_bboxes(output_image, results['<OCR_WITH_REGION>'])
        return results, output_image
    else:
        return "", None  # Return empty string and None for unknown task prompts

css = """
  #output {
    height: 500px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
"""


single_task_list =[
    'Caption', 'Detailed Caption', 'More Detailed Caption', 'Object Detection',
    'Dense Region Caption', 'Region Proposal', 'Caption to Phrase Grounding',
    'Referring Expression Segmentation', 'Region to Segmentation',
    'Open Vocabulary Detection', 'Region to Category', 'Region to Description',
    'OCR', 'OCR with Region'
]

cascased_task_list =[
    'Caption + Grounding', 'Detailed Caption + Grounding', 'More Detailed Caption + Grounding'
]


def update_task_dropdown(choice):
    if choice == 'Cascased task':
        return gr.Dropdown(choices=cascased_task_list, value='Caption + Grounding')
    else:
        return gr.Dropdown(choices=single_task_list, value='Caption')



with gr.Blocks(css=css) as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Tab(label="Florence-2 Image Captioning"):
        with gr.Row():
            with gr.Column():
                input_img = gr.Image(label="Input Picture")
                model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value='microsoft/Florence-2-large')
                task_type = gr.Radio(choices=['Single task', 'Cascased task'], label='Task type selector', value='Single task')
                task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="Caption")
                task_type.change(fn=update_task_dropdown, inputs=task_type, outputs=task_prompt)
                text_input = gr.Textbox(label="Text Input (optional)")
                submit_btn = gr.Button(value="Submit")
            with gr.Column():
                output_text = gr.Textbox(label="Output Text")
                output_img = gr.Image(label="Output Image")

        gr.Examples(
            examples=[
                ["image1.jpg", 'Object Detection'],
                ["image2.jpg", 'OCR with Region']
            ],
            inputs=[input_img, task_prompt],
            outputs=[output_text, output_img],
            fn=process_image,
            cache_examples=True,
            label='Try examples'
        )

        submit_btn.click(process_image, [input_img, task_prompt, text_input, model_selector], [output_text, output_img])

demo.launch(debug=True)