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| import os | |
| import sys | |
| from pathlib import Path | |
| # setup Grouded-Segment-Anything | |
| # building GroundingDINO requires torch but imports it before installing, | |
| # so directly installing in requirements.txt causes dependency error. | |
| # 1. build with "-e" option to keep the bin file in ./GroundingDINO/groundingdino/, rather than in site-package dir. | |
| os.system("pip install -e ./GroundingDINO/") | |
| # 2. for unknown reason, "import groundingdino" will fill due to unable to find the module, even after installing. | |
| # add ./GroundingDINO/ to PATH, so package "groundingdino" can be imported. | |
| sys.path.append(str(Path(__file__).parent / "GroundingDINO")) | |
| import random # noqa: E402 | |
| import cv2 # noqa: E402 | |
| import groundingdino.datasets.transforms as T # noqa: E402 | |
| import numpy as np # noqa: E402 | |
| import torch # noqa: E402 | |
| import torchvision # noqa: E402 | |
| import torchvision.transforms as TS # noqa: E402 | |
| from groundingdino.models import build_model # noqa: E402 | |
| from groundingdino.util.slconfig import SLConfig # noqa: E402 | |
| from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap # noqa: E402 | |
| from PIL import Image, ImageDraw, ImageFont # noqa: E402 | |
| from ram import inference_ram # noqa: E402 | |
| from ram import inference_tag2text # noqa: E402 | |
| from ram.models import ram # noqa: E402 | |
| from ram.models import tag2text_caption # noqa: E402 | |
| from segment_anything import SamPredictor, build_sam # noqa: E402 | |
| # args | |
| config_file = "GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py" | |
| ram_checkpoint = "./ram_swin_large_14m.pth" | |
| tag2text_checkpoint = "./tag2text_swin_14m.pth" | |
| grounded_checkpoint = "./groundingdino_swint_ogc.pth" | |
| sam_checkpoint = "./sam_vit_h_4b8939.pth" | |
| box_threshold = 0.25 | |
| text_threshold = 0.2 | |
| iou_threshold = 0.5 | |
| device = "cpu" | |
| def load_model(model_config_path, model_checkpoint_path, device): | |
| args = SLConfig.fromfile(model_config_path) | |
| args.device = device | |
| model = build_model(args) | |
| checkpoint = torch.load(model_checkpoint_path, map_location="cpu") | |
| load_res = model.load_state_dict( | |
| clean_state_dict(checkpoint["model"]), strict=False) | |
| print(load_res) | |
| _ = model.eval() | |
| return model | |
| def get_grounding_output(model, image, caption, box_threshold, text_threshold, device="cpu"): | |
| caption = caption.lower() | |
| caption = caption.strip() | |
| if not caption.endswith("."): | |
| caption = caption + "." | |
| model = model.to(device) | |
| image = image.to(device) | |
| with torch.no_grad(): | |
| outputs = model(image[None], captions=[caption]) | |
| logits = outputs["pred_logits"].cpu().sigmoid()[0] # (nq, 256) | |
| boxes = outputs["pred_boxes"].cpu()[0] # (nq, 4) | |
| logits.shape[0] | |
| # filter output | |
| logits_filt = logits.clone() | |
| boxes_filt = boxes.clone() | |
| filt_mask = logits_filt.max(dim=1)[0] > box_threshold | |
| logits_filt = logits_filt[filt_mask] # num_filt, 256 | |
| boxes_filt = boxes_filt[filt_mask] # num_filt, 4 | |
| logits_filt.shape[0] | |
| # get phrase | |
| tokenlizer = model.tokenizer | |
| tokenized = tokenlizer(caption) | |
| # build pred | |
| pred_phrases = [] | |
| scores = [] | |
| for logit, box in zip(logits_filt, boxes_filt): | |
| pred_phrase = get_phrases_from_posmap( | |
| logit > text_threshold, tokenized, tokenlizer) | |
| pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})") | |
| scores.append(logit.max().item()) | |
| return boxes_filt, torch.Tensor(scores), pred_phrases | |
| def draw_mask(mask, draw, random_color=False): | |
| if random_color: | |
| color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 153) | |
| else: | |
| color = (30, 144, 255, 153) | |
| nonzero_coords = np.transpose(np.nonzero(mask)) | |
| for coord in nonzero_coords: | |
| draw.point(coord[::-1], fill=color) | |
| def draw_box(box, draw, label): | |
| # random color | |
| color = tuple(np.random.randint(0, 255, size=3).tolist()) | |
| line_width = int(max(4, min(20, 0.006*max(draw.im.size)))) | |
| draw.rectangle(((box[0], box[1]), (box[2], box[3])), outline=color, width=line_width) | |
| if label: | |
| font_path = os.path.join( | |
| cv2.__path__[0], 'qt', 'fonts', 'DejaVuSans.ttf') | |
| font_size = int(max(12, min(60, 0.02*max(draw.im.size)))) | |
| font = ImageFont.truetype(font_path, size=font_size) | |
| if hasattr(font, "getbbox"): | |
| bbox = draw.textbbox((box[0], box[1]), str(label), font) | |
| else: | |
| w, h = draw.textsize(str(label), font) | |
| bbox = (box[0], box[1], w + box[0], box[1] + h) | |
| draw.rectangle(bbox, fill=color) | |
| draw.text((box[0], box[1]), str(label), fill="white", font=font) | |
| draw.text((box[0], box[1]), label, font=font) | |
| def inference( | |
| raw_image, specified_tags, do_det_seg, | |
| tagging_model_type, tagging_model, grounding_dino_model, sam_model | |
| ): | |
| print(f"Start processing, image size {raw_image.size}") | |
| raw_image = raw_image.convert("RGB") | |
| # run tagging model | |
| normalize = TS.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| transform = TS.Compose([ | |
| TS.Resize((384, 384)), | |
| TS.ToTensor(), | |
| normalize | |
| ]) | |
| image = raw_image.resize((384, 384)) | |
| image = transform(image).unsqueeze(0).to(device) | |
| # Currently ", " is better for detecting single tags | |
| # while ". " is a little worse in some case | |
| if tagging_model_type == "RAM": | |
| res = inference_ram(image, tagging_model) | |
| tags = res[0].strip(' ').replace(' ', ' ').replace(' |', ',') | |
| tags_chinese = res[1].strip(' ').replace(' ', ' ').replace(' |', ',') | |
| print("Tags: ", tags) | |
| print("图像标签: ", tags_chinese) | |
| else: | |
| res = inference_tag2text(image, tagging_model, specified_tags) | |
| tags = res[0].strip(' ').replace(' ', ' ').replace(' |', ',') | |
| caption = res[2] | |
| print(f"Tags: {tags}") | |
| print(f"Caption: {caption}") | |
| # return | |
| if not do_det_seg: | |
| if tagging_model_type == "RAM": | |
| return tags.replace(", ", " | "), tags_chinese.replace(", ", " | "), None | |
| else: | |
| return tags.replace(", ", " | "), caption, None | |
| # run groundingDINO | |
| transform = T.Compose([ | |
| T.RandomResize([800], max_size=1333), | |
| T.ToTensor(), | |
| T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), | |
| ]) | |
| image, _ = transform(raw_image, None) # 3, h, w | |
| boxes_filt, scores, pred_phrases = get_grounding_output( | |
| grounding_dino_model, image, tags, box_threshold, text_threshold, device=device | |
| ) | |
| print("GroundingDINO finished") | |
| # run SAM | |
| image = np.asarray(raw_image) | |
| sam_model.set_image(image) | |
| size = raw_image.size | |
| H, W = size[1], size[0] | |
| for i in range(boxes_filt.size(0)): | |
| boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H]) | |
| boxes_filt[i][:2] -= boxes_filt[i][2:] / 2 | |
| boxes_filt[i][2:] += boxes_filt[i][:2] | |
| boxes_filt = boxes_filt.cpu() | |
| # use NMS to handle overlapped boxes | |
| print(f"Before NMS: {boxes_filt.shape[0]} boxes") | |
| nms_idx = torchvision.ops.nms(boxes_filt, scores, iou_threshold).numpy().tolist() | |
| boxes_filt = boxes_filt[nms_idx] | |
| pred_phrases = [pred_phrases[idx] for idx in nms_idx] | |
| print(f"After NMS: {boxes_filt.shape[0]} boxes") | |
| transformed_boxes = sam_model.transform.apply_boxes_torch(boxes_filt, image.shape[:2]).to(device) | |
| masks, _, _ = sam_model.predict_torch( | |
| point_coords=None, | |
| point_labels=None, | |
| boxes=transformed_boxes.to(device), | |
| multimask_output=False, | |
| ) | |
| print("SAM finished") | |
| # draw output image | |
| mask_image = Image.new('RGBA', size, color=(0, 0, 0, 0)) | |
| mask_draw = ImageDraw.Draw(mask_image) | |
| for mask in masks: | |
| draw_mask(mask[0].cpu().numpy(), mask_draw, random_color=True) | |
| image_draw = ImageDraw.Draw(raw_image) | |
| for box, label in zip(boxes_filt, pred_phrases): | |
| draw_box(box, image_draw, label) | |
| out_image = raw_image.convert('RGBA') | |
| out_image.alpha_composite(mask_image) | |
| # return | |
| if tagging_model_type == "RAM": | |
| return tags.replace(", ", " | "), tags_chinese.replace(", ", " | "), out_image | |
| else: | |
| return tags.replace(", ", " | "), caption, out_image | |
| if __name__ == "__main__": | |
| import gradio as gr | |
| # load RAM | |
| ram_model = ram(pretrained=ram_checkpoint, image_size=384, vit='swin_l') | |
| ram_model.eval() | |
| ram_model = ram_model.to(device) | |
| # load Tag2Text | |
| delete_tag_index = [] # filter out attributes and action categories which are difficult to grounding | |
| for i in range(3012, 3429): | |
| delete_tag_index.append(i) | |
| tag2text_model = tag2text_caption(pretrained=tag2text_checkpoint, | |
| image_size=384, | |
| vit='swin_b', | |
| delete_tag_index=delete_tag_index) | |
| tag2text_model.threshold = 0.64 # we reduce the threshold to obtain more tags | |
| tag2text_model.eval() | |
| tag2text_model = tag2text_model.to(device) | |
| # load groundingDINO | |
| grounding_dino_model = load_model(config_file, grounded_checkpoint, device=device) | |
| # load SAM | |
| sam_model = SamPredictor(build_sam(checkpoint=sam_checkpoint).to(device)) | |
| # build GUI | |
| def build_gui(): | |
| description = """ | |
| <center><strong><font size='10'>Recognize Anything Model + Grounded-SAM</font></strong></center> | |
| <br> | |
| Welcome to the RAM/Tag2Text + Grounded-SAM demo! <br><br> | |
| <li> | |
| <b>Recognize Anything Model:</b> Upload your image to get the <b>English and Chinese tags</b>! | |
| </li> | |
| <li> | |
| <b>Tag2Text Model:</b> Upload your image to get the <b>tags and caption</b>! | |
| (Optional: Specify tags to get the corresponding caption.) | |
| </li> | |
| <li> | |
| <b>Grounded-SAM:</b> Tick the checkbox to get <b>boxes</b> and <b>masks</b> of tags! | |
| </li> | |
| <br> | |
| Great thanks to <a href='https://huggingface.co/majinyu' target='_blank'>Ma Jinyu</a>, the major contributor of this demo! | |
| """ # noqa | |
| article = """ | |
| <p style='text-align: center'> | |
| RAM and Tag2Text are trained on open-source datasets, and we are persisting in refining and iterating upon it.<br/> | |
| Grounded-SAM is a combination of Grounding DINO and SAM aming to detect and segment anything with text inputs.<br/> | |
| <a href='https://recognize-anything.github.io/' target='_blank'>Recognize Anything: A Strong Image Tagging Model</a> | |
| | | |
| <a href='https://https://tag2text.github.io/' target='_blank'>Tag2Text: Guiding Language-Image Model via Image Tagging</a> | |
| | | |
| <a href='https://github.com/IDEA-Research/Grounded-Segment-Anything' target='_blank'>Grounded-Segment-Anything</a> | |
| </p> | |
| """ # noqa | |
| def inference_with_ram(img, do_det_seg): | |
| return inference( | |
| img, None, do_det_seg, | |
| "RAM", ram_model, grounding_dino_model, sam_model | |
| ) | |
| def inference_with_t2t(img, input_tags, do_det_seg): | |
| return inference( | |
| img, input_tags, do_det_seg, | |
| "Tag2Text", tag2text_model, grounding_dino_model, sam_model | |
| ) | |
| with gr.Blocks(title="Recognize Anything Model") as demo: | |
| ############### | |
| # components | |
| ############### | |
| gr.HTML(description) | |
| with gr.Tab(label="Recognize Anything Model"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| ram_in_img = gr.Image(type="pil") | |
| ram_opt_det_seg = gr.Checkbox(label="Get Boxes and Masks with Grounded-SAM", value=True) | |
| with gr.Row(): | |
| ram_btn_run = gr.Button(value="Run") | |
| ram_btn_clear = gr.ClearButton() | |
| with gr.Column(): | |
| ram_out_img = gr.Image(type="pil") | |
| ram_out_tag = gr.Textbox(label="Tags") | |
| ram_out_biaoqian = gr.Textbox(label="标签") | |
| gr.Examples( | |
| examples=[ | |
| ["images/demo1.jpg", True], | |
| ["images/demo2.jpg", True], | |
| ["images/demo4.jpg", True], | |
| ], | |
| fn=inference_with_ram, | |
| inputs=[ram_in_img, ram_opt_det_seg], | |
| outputs=[ram_out_tag, ram_out_biaoqian, ram_out_img], | |
| cache_examples=True | |
| ) | |
| with gr.Tab(label="Tag2Text Model"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| t2t_in_img = gr.Image(type="pil") | |
| t2t_in_tag = gr.Textbox(label="User Specified Tags (Optional, separated by comma)") | |
| t2t_opt_det_seg = gr.Checkbox(label="Get Boxes and Masks with Grounded-SAM", value=True) | |
| with gr.Row(): | |
| t2t_btn_run = gr.Button(value="Run") | |
| t2t_btn_clear = gr.ClearButton() | |
| with gr.Column(): | |
| t2t_out_img = gr.Image(type="pil") | |
| t2t_out_tag = gr.Textbox(label="Tags") | |
| t2t_out_cap = gr.Textbox(label="Caption") | |
| gr.Examples( | |
| examples=[ | |
| ["images/demo4.jpg", "", True], | |
| ["images/demo4.jpg", "power line", False], | |
| ["images/demo4.jpg", "track, train", False], | |
| ], | |
| fn=inference_with_t2t, | |
| inputs=[t2t_in_img, t2t_in_tag, t2t_opt_det_seg], | |
| outputs=[t2t_out_tag, t2t_out_cap, t2t_out_img], | |
| cache_examples=True | |
| ) | |
| gr.HTML(article) | |
| ############### | |
| # events | |
| ############### | |
| # run inference | |
| ram_btn_run.click( | |
| fn=inference_with_ram, | |
| inputs=[ram_in_img, ram_opt_det_seg], | |
| outputs=[ram_out_tag, ram_out_biaoqian, ram_out_img] | |
| ) | |
| t2t_btn_run.click( | |
| fn=inference_with_t2t, | |
| inputs=[t2t_in_img, t2t_in_tag, t2t_opt_det_seg], | |
| outputs=[t2t_out_tag, t2t_out_cap, t2t_out_img] | |
| ) | |
| # hide or show image output | |
| ram_opt_det_seg.change(fn=lambda b: gr.update(visible=b), inputs=[ram_opt_det_seg], outputs=[ram_out_img]) | |
| t2t_opt_det_seg.change(fn=lambda b: gr.update(visible=b), inputs=[t2t_opt_det_seg], outputs=[t2t_out_img]) | |
| # clear | |
| ram_btn_clear.add([ram_in_img, ram_out_img, ram_out_tag, ram_out_biaoqian]) | |
| t2t_btn_clear.add([t2t_in_img, t2t_in_tag, t2t_out_img, t2t_out_tag, t2t_out_cap]) | |
| return demo | |
| build_gui().launch(enable_queue=True) | |