import argparse import os import random from collections import defaultdict import cv2 import re import numpy as np from PIL import Image import torch import html import gradio as gr import torchvision.transforms as T import torch.backends.cudnn as cudnn from minigpt4.common.config import Config from minigpt4.common.registry import registry from minigpt4.conversation.conversation import Conversation, SeparatorStyle, Chat # imports modules for registration from minigpt4.datasets.builders import * from minigpt4.models import * from minigpt4.processors import * from minigpt4.runners import * from minigpt4.tasks import * def parse_args(): parser = argparse.ArgumentParser(description="Demo") parser.add_argument("--cfg-path", default='eval_configs/minigptv2_eval.yaml', help="path to configuration file.") parser.add_argument("--gpu-id", type=int, default=0, help="specify the gpu to load the model.") parser.add_argument( "--options", nargs="+", help="override some settings in the used config, the key-value pair " "in xxx=yyy format will be merged into config file (deprecate), " "change to --cfg-options instead.", ) args = parser.parse_args() return args random.seed(42) np.random.seed(42) torch.manual_seed(42) cudnn.benchmark = False cudnn.deterministic = True print('Initializing Chat') args = parse_args() cfg = Config(args) device = 'cuda:{}'.format(args.gpu_id) model_config = cfg.model_cfg model_config.device_8bit = args.gpu_id model_cls = registry.get_model_class(model_config.arch) model = model_cls.from_config(model_config).to(device) bounding_box_size = 100 vis_processor_cfg = cfg.datasets_cfg.cc_sbu_align.vis_processor.train vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg) model = model.eval() CONV_VISION = Conversation( system="", roles=(r"[INST] ", r" [/INST]"), messages=[], offset=2, sep_style=SeparatorStyle.SINGLE, sep="", ) def extract_substrings(string): # first check if there is no-finished bracket index = string.rfind('}') if index != -1: string = string[:index + 1] pattern = r'

(.*?)\}(?!<)' matches = re.findall(pattern, string) substrings = [match for match in matches] return substrings def is_overlapping(rect1, rect2): x1, y1, x2, y2 = rect1 x3, y3, x4, y4 = rect2 return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4) def computeIoU(bbox1, bbox2): x1, y1, x2, y2 = bbox1 x3, y3, x4, y4 = bbox2 intersection_x1 = max(x1, x3) intersection_y1 = max(y1, y3) intersection_x2 = min(x2, x4) intersection_y2 = min(y2, y4) intersection_area = max(0, intersection_x2 - intersection_x1 + 1) * max(0, intersection_y2 - intersection_y1 + 1) bbox1_area = (x2 - x1 + 1) * (y2 - y1 + 1) bbox2_area = (x4 - x3 + 1) * (y4 - y3 + 1) union_area = bbox1_area + bbox2_area - intersection_area iou = intersection_area / union_area return iou def save_tmp_img(visual_img): file_name = "".join([str(random.randint(0, 9)) for _ in range(5)]) + ".jpg" file_path = "/tmp/gradio" + file_name visual_img.save(file_path) return file_path def mask2bbox(mask): if mask is None: return '' mask = mask.resize([100, 100], resample=Image.NEAREST) mask = np.array(mask)[:, :, 0] rows = np.any(mask, axis=1) cols = np.any(mask, axis=0) if rows.sum(): # Get the top, bottom, left, and right boundaries rmin, rmax = np.where(rows)[0][[0, -1]] cmin, cmax = np.where(cols)[0][[0, -1]] bbox = '{{<{}><{}><{}><{}>}}'.format(cmin, rmin, cmax, rmax) else: bbox = '' return bbox def escape_markdown(text): # List of Markdown special characters that need to be escaped md_chars = ['<', '>'] # Escape each special character for char in md_chars: text = text.replace(char, '\\' + char) return text def reverse_escape(text): md_chars = ['\\<', '\\>'] for char in md_chars: text = text.replace(char, char[1:]) return text colors = [ (255, 0, 0), (0, 255, 0), (0, 0, 255), (210, 210, 0), (255, 0, 255), (0, 255, 255), (114, 128, 250), (0, 165, 255), (0, 128, 0), (144, 238, 144), (238, 238, 175), (255, 191, 0), (0, 128, 0), (226, 43, 138), (255, 0, 255), (0, 215, 255), ] color_map = { f"{color_id}": f"#{hex(color[2])[2:].zfill(2)}{hex(color[1])[2:].zfill(2)}{hex(color[0])[2:].zfill(2)}" for color_id, color in enumerate(colors) } used_colors = colors def visualize_all_bbox_together(image, generation): if image is None: return None, '' generation = html.unescape(generation) image_width, image_height = image.size image = image.resize([500, int(500 / image_width * image_height)]) image_width, image_height = image.size string_list = extract_substrings(generation) if string_list: # it is grounding or detection mode = 'all' entities = defaultdict(list) i = 0 j = 0 for string in string_list: try: obj, string = string.split('

') except ValueError: print('wrong string: ', string) continue bbox_list = string.split('') flag = False for bbox_string in bbox_list: integers = re.findall(r'-?\d+', bbox_string) if len(integers) == 4: x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3]) left = x0 / bounding_box_size * image_width bottom = y0 / bounding_box_size * image_height right = x1 / bounding_box_size * image_width top = y1 / bounding_box_size * image_height entities[obj].append([left, bottom, right, top]) j += 1 flag = True if flag: i += 1 else: integers = re.findall(r'-?\d+', generation) if len(integers) == 4: # it is refer mode = 'single' entities = list() x0, y0, x1, y1 = int(integers[0]), int(integers[1]), int(integers[2]), int(integers[3]) left = x0 / bounding_box_size * image_width bottom = y0 / bounding_box_size * image_height right = x1 / bounding_box_size * image_width top = y1 / bounding_box_size * image_height entities.append([left, bottom, right, top]) else: # don't detect any valid bbox to visualize return None, '' if len(entities) == 0: return None, '' if isinstance(image, Image.Image): image_h = image.height image_w = image.width image = np.array(image) elif isinstance(image, str): if os.path.exists(image): pil_img = Image.open(image).convert("RGB") image = np.array(pil_img)[:, :, [2, 1, 0]] image_h = pil_img.height image_w = pil_img.width else: raise ValueError(f"invaild image path, {image}") elif isinstance(image, torch.Tensor): image_tensor = image.cpu() reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None] reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None] image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean pil_img = T.ToPILImage()(image_tensor) image_h = pil_img.height image_w = pil_img.width image = np.array(pil_img)[:, :, [2, 1, 0]] else: raise ValueError(f"invaild image format, {type(image)} for {image}") indices = list(range(len(entities))) new_image = image.copy() previous_bboxes = [] # size of text text_size = 0.5 # thickness of text text_line = 1 # int(max(1 * min(image_h, image_w) / 512, 1)) box_line = 2 (c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line) base_height = int(text_height * 0.675) text_offset_original = text_height - base_height text_spaces = 2 # num_bboxes = sum(len(x[-1]) for x in entities) used_colors = colors # random.sample(colors, k=num_bboxes) color_id = -1 for entity_idx, entity_name in enumerate(entities): if mode == 'single' or mode == 'identify': bboxes = entity_name bboxes = [bboxes] else: bboxes = entities[entity_name] color_id += 1 for bbox_id, (x1_norm, y1_norm, x2_norm, y2_norm) in enumerate(bboxes): skip_flag = False orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm), int(y1_norm), int(x2_norm), int(y2_norm) color = used_colors[entity_idx % len(used_colors)] # tuple(np.random.randint(0, 255, size=3).tolist()) new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line) if mode == 'all': l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1 x1 = orig_x1 - l_o y1 = orig_y1 - l_o if y1 < text_height + text_offset_original + 2 * text_spaces: y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces x1 = orig_x1 + r_o # add text background (text_width, text_height), _ = cv2.getTextSize(f" {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line) text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - ( text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1 for prev_bbox in previous_bboxes: if computeIoU((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']) > 0.95 and \ prev_bbox['phrase'] == entity_name: skip_flag = True break while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox['bbox']): text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces) text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces) y1 += (text_height + text_offset_original + 2 * text_spaces) if text_bg_y2 >= image_h: text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces)) text_bg_y2 = image_h y1 = image_h break if not skip_flag: alpha = 0.5 for i in range(text_bg_y1, text_bg_y2): for j in range(text_bg_x1, text_bg_x2): if i < image_h and j < image_w: if j < text_bg_x1 + 1.35 * c_width: # original color bg_color = color else: # white bg_color = [255, 255, 255] new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype( np.uint8) cv2.putText( new_image, f" {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces), cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA ) previous_bboxes.append( {'bbox': (text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), 'phrase': entity_name}) if mode == 'all': def color_iterator(colors): while True: for color in colors: yield color color_gen = color_iterator(colors) # Add colors to phrases and remove

def colored_phrases(match): phrase = match.group(1) color = next(color_gen) return f'{phrase}' generation = re.sub(r'{<\d+><\d+><\d+><\d+>}|', '', generation) generation_colored = re.sub(r'

(.*?)

', colored_phrases, generation) else: generation_colored = '' pil_image = Image.fromarray(new_image) return pil_image, generation_colored def gradio_reset(chat_state, img_list, path_list): if chat_state is not None: chat_state.messages = [] if img_list is not None: img_list = [] for path in path_list: os.remove(path) return None, gr.update(value=None, interactive=True), gr.update(placeholder='Upload your image and chat', interactive=True), chat_state, img_list def image_upload_trigger(upload_flag, replace_flag, img_list): # set the upload flag to true when receive a new image. # if there is an old image (and old conversation), set the replace flag to true to reset the conv later. upload_flag = 1 if img_list: replace_flag = 1 return upload_flag, replace_flag def example_trigger(text_input, image, upload_flag, replace_flag, img_list): # set the upload flag to true when receive a new image. # if there is an old image (and old conversation), set the replace flag to true to reset the conv later. upload_flag = 1 if img_list or replace_flag == 1: replace_flag = 1 return upload_flag, replace_flag def gradio_ask(user_message, chatbot, chat_state, gr_img, img_list, upload_flag, replace_flag): if len(user_message) == 0: text_box_show = 'Input should not be empty!' else: text_box_show = '' if isinstance(gr_img, dict): gr_img, mask = gr_img['image'], gr_img['mask'] else: mask = None if '[identify]' in user_message: # check if user provide bbox in the text input integers = re.findall(r'-?\d+', user_message) if len(integers) != 4: # no bbox in text bbox = mask2bbox(mask) user_message = user_message + bbox if chat_state is None: chat_state = CONV_VISION.copy() if upload_flag: if replace_flag: chat_state = CONV_VISION.copy() # new image, reset everything replace_flag = 0 chatbot = [] img_list = [] llm_message = chat.upload_img(gr_img, chat_state, img_list) upload_flag = 0 chat.ask(user_message, chat_state) chatbot = chatbot + [[user_message, None]] if '[identify]' in user_message: visual_img, _ = visualize_all_bbox_together(gr_img, user_message) if visual_img is not None: file_path = save_tmp_img(visual_img) chatbot = chatbot + [[(file_path,), None]] return text_box_show, chatbot, chat_state, img_list, upload_flag, replace_flag def gradio_answer(chatbot, chat_state, img_list, temperature): llm_message = chat.answer(conv=chat_state, img_list=img_list, temperature=temperature, max_new_tokens=500, max_length=2000)[0] chatbot[-1][1] = llm_message return chatbot, chat_state def gradio_stream_answer(chatbot, chat_state, img_list, temperature): if len(img_list) > 0: if not isinstance(img_list[0], torch.Tensor): chat.encode_img(img_list) streamer = chat.stream_answer(conv=chat_state, img_list=img_list, temperature=temperature, max_new_tokens=500, max_length=2000) output = '' for new_output in streamer: escapped = escape_markdown(new_output) output += escapped chatbot[-1][1] = output yield chatbot, chat_state print(output) chat_state.messages[-1][1] = '
' return chatbot, chat_state def gradio_visualize(chatbot, gr_img, path_list): if isinstance(gr_img, dict): gr_img, mask = gr_img['image'], gr_img['mask'] unescaped = reverse_escape(chatbot[-1][1]) visual_img, generation_color = visualize_all_bbox_together(gr_img, unescaped) if visual_img is not None: if len(generation_color): chatbot[-1][1] = generation_color file_path = save_tmp_img(visual_img) path_list.append(file_path) chatbot = chatbot + [[None, (file_path,)]] return chatbot def gradio_taskselect(idx): prompt_list = [ '', '[grounding] describe this image in detail', '[refer] ', '[detection] ', '[identify] what is this ', '[vqa] ' ] instruct_list = [ '**Hint:** Type in whatever you want', '**Hint:** Send the command to generate a grounded image description', '**Hint:** Type in a phrase about an object in the image and send the command', '**Hint:** Type in a caption or phrase, and see object locations in the image', '**Hint:** Draw a bounding box on the uploaded image then send the command. Click the "clear" botton on the top right of the image before redraw', '**Hint:** Send a question to get a short answer', ] return prompt_list[idx], instruct_list[idx] chat = Chat(model, vis_processor, device=device) title = """

MiniGPT-v2 Demo

""" description = 'Welcome to Our MiniGPT-v2 Chatbot Demo!' # article = """

""" article = """

""" introduction = ''' For Abilities Involving Visual Grounding: 1. Grounding: CLICK **Send** to generate a grounded image description. 2. Refer: Input a referring object and CLICK **Send**. 3. Detection: Write a caption or phrase, and CLICK **Send**. 4. Identify: Draw the bounding box on the uploaded image window and CLICK **Send** to generate the bounding box. (CLICK "clear" button before re-drawing next time). 5. VQA: Input a visual question and CLICK **Send**. 6. No Tag: Input whatever you want and CLICK **Send** without any tagging You can also simply chat in free form! ''' text_input = gr.Textbox(placeholder='Upload your image and chat', interactive=True, show_label=False, container=False, scale=8) with gr.Blocks() as demo: gr.Markdown(title) # gr.Markdown(description) gr.Markdown(article) with gr.Row(): with gr.Column(scale=0.5): image = gr.Image(type="pil", tool='sketch', brush_radius=20) temperature = gr.Slider( minimum=0.1, maximum=1.5, value=0.6, step=0.1, interactive=True, label="Temperature", ) clear = gr.Button("Restart") gr.Markdown(introduction) with gr.Column(): chat_state = gr.State(value=None) img_list = gr.State(value=[]) chatbot = gr.Chatbot(label='MiniGPT-v2') dataset = gr.Dataset( components=[gr.Textbox(visible=False)], samples=[['No Tag'], ['Grounding'], ['Refer'], ['Detection'], ['Identify'], ['VQA']], type="index", label='Task Shortcuts', ) task_inst = gr.Markdown('**Hint:** Upload your image and chat') with gr.Row(): text_input.render() send = gr.Button("Send", variant='primary', size='sm', scale=1) upload_flag = gr.State(value=0) replace_flag = gr.State(value=0) path_list = gr.State(value=0) image.upload(image_upload_trigger, [upload_flag, replace_flag, img_list], [upload_flag, replace_flag]) with gr.Row(): with gr.Column(): gr.Examples(examples=[ ["examples_v2/office.jpg", "[grounding] describe this image in detail", upload_flag, replace_flag, img_list], ["examples_v2/sofa.jpg", "[detection] sofas", upload_flag, replace_flag, img_list], ["examples_v2/2000x1372_wmkn_0012149409555.jpg", "[refer] the world cup", upload_flag, replace_flag, img_list], ["examples_v2/KFC-20-for-20-Nuggets.jpg", "[identify] what is this {<4><50><30><65>}", upload_flag, replace_flag, img_list], ], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger, outputs=[upload_flag, replace_flag]) with gr.Column(): gr.Examples(examples=[ ["examples_v2/glip_test.jpg", "[vqa] where should I hide in this room when playing hide and seek", upload_flag, replace_flag, img_list], ["examples_v2/float.png", "Please write a poem about the image", upload_flag, replace_flag, img_list], ["examples_v2/thief.png", "Is the weapon fateful", upload_flag, replace_flag, img_list], ["examples_v2/cockdial.png", "What might happen in this image in the next second", upload_flag, replace_flag, img_list], ], inputs=[image, text_input, upload_flag, replace_flag, img_list], fn=example_trigger, outputs=[upload_flag, replace_flag]) dataset.click( gradio_taskselect, inputs=[dataset], outputs=[text_input, task_inst], show_progress="hidden", postprocess=False, queue=False, ) text_input.submit( gradio_ask, [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag], [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag], queue=False ).success( gradio_stream_answer, [chatbot, chat_state, img_list, temperature], [chatbot, chat_state] ).success( gradio_visualize, [chatbot, image, path_list], [chatbot, path_list], queue=False, ) send.click( gradio_ask, [text_input, chatbot, chat_state, image, img_list, upload_flag, replace_flag], [text_input, chatbot, chat_state, img_list, upload_flag, replace_flag] ).success( gradio_stream_answer, [chatbot, chat_state, img_list, temperature], [chatbot, chat_state] ).success( gradio_visualize, [chatbot, image, path_list], [chatbot, path_list], ) clear.click(gradio_reset, [chat_state, img_list, path_list], [chatbot, image, text_input, chat_state, img_list], queue=False) demo.launch(enable_queue=True)