File size: 11,296 Bytes
c426a27
 
 
 
10240e0
c426a27
 
 
 
10240e0
c426a27
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
10240e0
c426a27
 
10240e0
c426a27
10240e0
c426a27
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
from io import BytesIO
import string
import gradio as gr
import requests
from caption_anything import CaptionAnything
import torch
import json
import sys
import argparse
from caption_anything import parse_augment
import os

# download sam checkpoint if not downloaded
def download_checkpoint(url, folder, filename):
    os.makedirs(folder, exist_ok=True)
    filepath = os.path.join(folder, filename)

    if not os.path.exists(filepath):
        response = requests.get(url, stream=True)
        with open(filepath, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                if chunk:
                    f.write(chunk)

    return filepath
checkpoint_url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
folder = "segmenter"
filename = "sam_vit_h_4b8939.pth"

title = """<h1 align="center">Caption-Anything</h1>"""
description = """Gradio demo for Caption Anything, image to dense captioning generation with various language styles. To use it, simply upload your image, or click one of the examples to load them.
<br> <strong>Code</strong>: GitHub repo: <a href='https://github.com/ttengwang/Caption-Anything' target='_blank'></a>
"""

examples = [
    ["test_img/img2.jpg", "[[1000, 700, 1]]"]
]

args = parse_augment()

def get_prompt(chat_input, click_state):    
    points = click_state[0]
    labels = click_state[1]
    inputs = json.loads(chat_input)
    for input in inputs:
        points.append(input[:2])
        labels.append(input[2])
    
    prompt = {
        "prompt_type":["click"],
        "input_point":points,
        "input_label":labels,
        "multimask_output":"True",
    }
    return prompt
    
def inference_seg_cap(image_input, chat_input, language, sentiment, factuality, length, state, click_state):
    controls = {'length': length,
             'sentiment': sentiment,
             'factuality': factuality,
             'language': language}
    prompt = get_prompt(chat_input, click_state)
    print('prompt: ', prompt, 'controls: ', controls)
    out = model.inference(image_input, prompt, controls)
    state = state + [(None, "Image point: {}, Input label: {}".format(prompt["input_point"], prompt["input_label"]))]
    for k, v in out['generated_captions'].items():
        state = state + [(f'{k}: {v}', None)]
    click_state[2].append(out['generated_captions']['raw_caption'])
    image_output_mask = out['mask_save_path']
    image_output_crop = out['crop_save_path']
    return state, state, click_state, image_output_mask, image_output_crop


def upload_callback(image_input, state):
    state = state + [('Image size: ' + str(image_input.size), None)]
    return state

# get coordinate in format [[x,y,positive/negative]]
def get_select_coords(image_input, point_prompt, language, sentiment, factuality, length, state, click_state, evt: gr.SelectData):
        print("point_prompt: ", point_prompt)
        if point_prompt == 'Positive Point':
            coordinate = "[[{}, {}, 1]]".format(str(evt.index[0]), str(evt.index[1]))
        else:
            coordinate = "[[{}, {}, 0]]".format(str(evt.index[0]), str(evt.index[1]))
        return (coordinate,) + inference_seg_cap(image_input, coordinate, language, sentiment, factuality, length, state, click_state)
    
def chat_with_points(chat_input, click_state, state):
    points, labels, captions = click_state
    # point_chat_prompt = "I want you act as a chat bot in terms of image. I will give you some points (w, h) in the image and tell you what happed on the point in natural language. Note that (0, 0) refers to the top-left corner of the image, w refers to the width and h refers the height. You should chat with me based on the fact in the image instead of imagination. Now I tell you the points with their visual description:\n{points_with_caps}\n. Now begin chatting! Human: {chat_input}\nAI: "
    # "The image is of width {width} and height {height}." 
    point_chat_prompt = "a) Revised prompt: I am an AI trained to chat with you about an image based on specific points (w, h) you provide, along with their visual descriptions. Please note that (0, 0) refers to the top-left corner of the image, w refers to the width, and h refers to the height. Here are the points and their descriptions you've given me: {points_with_caps}. Now, let's chat! Human: {chat_input} AI:"
    prev_visual_context = ""
    pos_points = [f"{points[i][0]}, {points[i][1]}" for i in range(len(points)) if labels[i] == 1]
    prev_visual_context = ', '.join(pos_points) + captions[-1] + '\n'
    chat_prompt = point_chat_prompt.format(**{"points_with_caps": prev_visual_context, "chat_input": chat_input})
    response = model.text_refiner.llm(chat_prompt)
    state = state + [(chat_input, response)]
    return state, state

def init_openai_api_key(api_key):
    os.environ['OPENAI_API_KEY'] = api_key
    global model
    model = CaptionAnything(args)

css='''
#image_upload{min-height:200px}
#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 200px}
'''

with gr.Blocks(css=css) as iface:
    state = gr.State([])
    click_state = gr.State([[],[],[]])
    caption_state = gr.State([[]])
    gr.Markdown(title)
    gr.Markdown(description)

    with gr.Column():
        openai_api_key = gr.Textbox(
            placeholder="Input your openAI API key and press Enter",
            show_label=False,
            lines=1,
            type="password",
        )
        openai_api_key.submit(init_openai_api_key, inputs=[openai_api_key])
        
        with gr.Row():
            with gr.Column(scale=0.7):
                image_input = gr.Image(type="pil", interactive=True, label="Image", elem_id="image_upload").style(height=260,scale=1.0)

                with gr.Row(scale=0.7):
                    point_prompt = gr.Radio(
                        choices=["Positive Point",  "Negative Point"],
                        value="Positive Point",
                        label="Points",
                        interactive=True,
                    )
                
                # with gr.Row():
                language = gr.Radio(
                    choices=["English", "Chinese", "French", "Spanish", "Arabic", "Portuguese","Cantonese"],
                    value="English",
                    label="Language",
                    interactive=True,
                )
                sentiment = gr.Radio(
                    choices=["Positive", "Natural", "Negative"],
                    value="Natural",
                    label="Sentiment",
                    interactive=True,
                )
                factuality = gr.Radio(
                    choices=["Factual", "Imagination"],
                    value="Factual",
                    label="Factuality",
                    interactive=True,
                )
                length = gr.Slider(
                    minimum=5,
                    maximum=100,
                    value=10,
                    step=1,
                    interactive=True,
                    label="Length",
                )

            with gr.Column(scale=1.5):
                with gr.Row():
                    image_output_mask= gr.Image(type="pil", interactive=False, label="Mask").style(height=260,scale=1.0)
                    image_output_crop= gr.Image(type="pil", interactive=False, label="Cropped Image by Mask", show_progress=False).style(height=260,scale=1.0)
                chatbot = gr.Chatbot(label="Chat Output",).style(height=450,scale=0.5)
        
        with gr.Row():
            with gr.Column(scale=0.7):
                prompt_input = gr.Textbox(lines=1, label="Input Prompt (A list of points like : [[100, 200, 1], [200,300,0]])")
                prompt_input.submit(
                    inference_seg_cap,
                    [
                        image_input,
                        prompt_input,
                        language,
                        sentiment,
                        factuality,
                        length,
                        state,
                        click_state
                    ],
                    [chatbot, state, click_state, image_output_mask, image_output_crop],
                    show_progress=False
                )
                
                image_input.upload(
                    upload_callback,
                    [image_input, state],
                    [chatbot]
                    )

                with gr.Row():
                    clear_button = gr.Button(value="Clear Click", interactive=True)
                    clear_button.click(
                        lambda: ("", [[], [], []], None, None),
                        [],
                        [prompt_input, click_state, image_output_mask, image_output_crop],
                        queue=False,
                        show_progress=False
                    )
                    
                    clear_button = gr.Button(value="Clear", interactive=True)
                    clear_button.click(
                        lambda: ("", [], [], [[], [], []], None, None),
                        [],
                        [prompt_input, chatbot, state, click_state, image_output_mask, image_output_crop],
                        queue=False,
                        show_progress=False
                    )
                    
                    submit_button = gr.Button(
                        value="Submit", interactive=True, variant="primary"
                    )
                    submit_button.click(
                        inference_seg_cap,
                        [
                            image_input,
                            prompt_input,
                            language,
                            sentiment,
                            factuality,
                            length,
                            state,
                            click_state
                        ],
                        [chatbot, state, click_state, image_output_mask, image_output_crop],
                        show_progress=False
                    )
                    
                # select coordinate
                image_input.select(
                    get_select_coords, 
                    inputs=[image_input,point_prompt,language,sentiment,factuality,length,state,click_state], 
                    outputs=[prompt_input, chatbot, state, click_state, image_output_mask, image_output_crop],
                    show_progress=False
                    )

                image_input.change(
                    lambda: ("", [], [[], [], []]),
                    [],
                    [chatbot, state, click_state],
                    queue=False,
                )
                
            with gr.Column(scale=1.5):
                chat_input = gr.Textbox(lines=1, label="Chat Input")
                chat_input.submit(chat_with_points, [chat_input, click_state, state], [chatbot, state])
                
                    
    examples = gr.Examples(
        examples=examples,
        inputs=[image_input, prompt_input],
    )

iface.queue(concurrency_count=1, api_open=False, max_size=10)
iface.launch(server_name="0.0.0.0", enable_queue=True, server_port=args.port, share=args.gradio_share)