Spaces:
Sleeping
Sleeping
| import os, sys | |
| import random | |
| import warnings | |
| os.system("python -m pip install -e segment_anything") | |
| os.system("python -m pip install -e GroundingDINO") | |
| os.system("pip install --upgrade diffusers[torch]") | |
| os.system("pip install opencv-python pycocotools matplotlib onnxruntime onnx ipykernel") | |
| os.system("wget http://cleancoastindia.com//SsagarSvc/Uploads/sample1.jpg") | |
| os.system("wget http://cleancoastindia.com/SsagarSvc/Uploads/sample2.jpg") | |
| os.system("wget http://cleancoastindia.com/SsagarSvc/Uploads/sample3.jpg") | |
| os.system("wget http://cleancoastindia.com/SsagarSvc/Uploads/sample4.jpg") | |
| os.system("wget http://cleancoastindia.com/SsagarSvc/Uploads/sample5.jpg") | |
| os.system("wget http://cleancoastindia.com/SsagarSvc/Uploads/sample6.jpg") | |
| os.system("wget http://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth") | |
| os.system("wget http://huggingface.co/spaces/mrtlive/segment-anything-model/resolve/main/sam_vit_h_4b8939.pth") | |
| sys.path.append(os.path.join(os.getcwd(), "GroundingDINO")) | |
| sys.path.append(os.path.join(os.getcwd(), "segment_anything")) | |
| sys.path.append(os.path.join(os.getcwd(), "Roboto-Regular")) | |
| warnings.filterwarnings("ignore") | |
| import gradio as gr | |
| import argparse | |
| import numpy as np | |
| import torch | |
| import torchvision | |
| from PIL import Image, ImageDraw, ImageFont | |
| # Grounding DINO | |
| import GroundingDINO.groundingdino.datasets.transforms as T | |
| from GroundingDINO.groundingdino.models import build_model | |
| from GroundingDINO.groundingdino.util.slconfig import SLConfig | |
| from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap | |
| # segment anything | |
| from segment_anything import build_sam, SamPredictor | |
| import numpy as np | |
| # diffusers | |
| import torch | |
| from diffusers import StableDiffusionInpaintPipeline | |
| # BLIP | |
| from transformers import BlipProcessor, BlipForConditionalGeneration | |
| def generate_caption(processor, blip_model, raw_image): | |
| # unconditional image captioning | |
| inputs = processor(raw_image, return_tensors="pt").to( | |
| "cuda", torch.float16) | |
| out = blip_model.generate(**inputs) | |
| caption = processor.decode(out[0], skip_special_tokens=True) | |
| return caption | |
| def transform_image(image_pil): | |
| 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(image_pil, None) # 3, h, w | |
| return image | |
| 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, with_logits=True): | |
| # caption="all plastic.all metal.all paper.all glass.all cardboard.all wood.all rubber" | |
| # caption="plastic.metal.paper.glass.cardboard.wood.rubber" | |
| print(caption) | |
| caption = caption.lower() | |
| caption = caption.strip() | |
| if not caption.endswith("."): | |
| caption = caption + "." | |
| 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) | |
| if with_logits: | |
| pred_phrases.append( | |
| pred_phrase + f"({str(logit.max().item())[:4]})") | |
| else: | |
| pred_phrases.append(pred_phrase) | |
| 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()) | |
| draw.rectangle(((box[0], box[1]), (box[2], box[3])), | |
| outline=color, width=2) | |
| if label: | |
| # font = ImageFont.load_default() | |
| font = ImageFont.truetype("Roboto-Regular.ttf", 20) | |
| 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") | |
| draw.text((box[0], box[1]), label) | |
| config_file = 'GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py' | |
| ckpt_repo_id = "ShilongLiu/GroundingDINO" | |
| ckpt_filenmae = "groundingdino_swint_ogc.pth" | |
| sam_checkpoint = 'sam_vit_h_4b8939.pth' | |
| output_dir = "outputs" | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| blip_processor = None | |
| blip_model = None | |
| groundingdino_model = None | |
| sam_predictor = None | |
| inpaint_pipeline = None | |
| def run_grounded_sam(input_image, text_prompt, box_threshold, text_threshold, iou_threshold): | |
| global blip_processor, blip_model, groundingdino_model, sam_predictor, inpaint_pipeline | |
| task_type = "seg" | |
| box_threshold = 0.3 | |
| text_threshold = 0.3 | |
| iou_threshold = 0.75 | |
| # make dir | |
| os.makedirs(output_dir, exist_ok=True) | |
| # load image | |
| image_pil = input_image.convert("RGB") | |
| transformed_image = transform_image(image_pil) | |
| if groundingdino_model is None: | |
| groundingdino_model = load_model( | |
| config_file, ckpt_filenmae, device=device) | |
| # run grounding dino model | |
| boxes_filt, scores, pred_phrases = get_grounding_output( | |
| groundingdino_model, transformed_image, text_prompt, box_threshold, text_threshold | |
| ) | |
| size = image_pil.size | |
| # process boxes | |
| 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() | |
| # nms | |
| 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") | |
| if task_type == 'seg' and boxes_filt.shape[0] > 0: | |
| if sam_predictor is None: | |
| # initialize SAM | |
| assert sam_checkpoint, 'sam_checkpoint is not found!' | |
| sam = build_sam(checkpoint=sam_checkpoint) | |
| sam.to(device=device) | |
| sam_predictor = SamPredictor(sam) | |
| image = np.array(image_pil) | |
| sam_predictor.set_image(image) | |
| transformed_boxes = sam_predictor.transform.apply_boxes_torch( | |
| boxes_filt, image.shape[:2]).to(device) | |
| masks, _, _ = sam_predictor.predict_torch( | |
| point_coords=None, | |
| point_labels=None, | |
| boxes=transformed_boxes, | |
| multimask_output=False, | |
| ) | |
| # masks: [1, 1, 512, 512] | |
| if task_type == 'seg' and len(masks) > 0: | |
| 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(image_pil) | |
| label_string = "" | |
| label_stringarr = [] | |
| for box, label in zip(boxes_filt, pred_phrases): | |
| # draw_box(box, image_draw, label) | |
| index = label.find("(") | |
| if index != -1: | |
| new_label = label[:index].strip() | |
| label_stringarr.append(new_label) | |
| draw_box(box, image_draw, new_label) | |
| else: | |
| new_label = label | |
| label_stringarr.append(new_label) | |
| draw_box(box, image_draw, new_label) | |
| # print(label_stringarr) | |
| label_counts = [] | |
| counts = {} | |
| for label in label_stringarr: | |
| if label in counts: | |
| counts[label] += 1 | |
| else: | |
| counts[label] = 1 | |
| for label, count in counts.items(): | |
| label_counts.append(f"{label}: {count}") | |
| label_counts_string = "\n".join(label_counts) | |
| print(label_counts_string) | |
| image_pil = image_pil.convert('RGBA') | |
| image_pil.alpha_composite(mask_image) | |
| return image_pil, label_counts_string | |
| # return [image_pil, mask_image] | |
| else: | |
| raise gr.InvalidOutputException("Model output is empty") | |
| print("number of masks".format(len(masks))) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser("Marine Litter", add_help=True) | |
| parser.add_argument("--debug", action="store_true", | |
| help="using debug mode") | |
| parser.add_argument("--share", action="store_true", help="share the app") | |
| parser.add_argument('--no-gradio-queue', action="store_true", | |
| help='path to the SAM checkpoint') | |
| args = parser.parse_args() | |
| print(args) | |
| block = gr.Blocks() | |
| if not args.no_gradio_queue: | |
| block = block.queue() | |
| with block: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("# Litter Segmentation and Detection") | |
| input_image = gr.Image(type="pil", label="Upload Image", interactive=True) | |
| # , value="demo1.jpg" | |
| # task_type = gr.Dropdown( | |
| text_prompt = gr.Dropdown(["plastic.metal.paper.glass.rubber.wood", | |
| "bottle.cap.cup.rope.tire.cigarette.bag.slippers.can.card board box.paper.wrapper.food.coconut.dustbin.cover"], | |
| value="plastic.metal.paper.glass.rubber.wood", label="select or enter prompt separate with dot . ",allow_custom_value=True) | |
| # text_prompt = gr.Textbox(label="Text Prompt", | |
| # value="plastic.metal.paper.glass.cardboard.wood.rubber") | |
| run_button = gr.Button("Run") | |
| gr.Markdown("# Image Examples") | |
| #gr.Examples(["sample1.jpg", "sample2.jpg","sample3.jpg","sample4.jpg","sample5.jpg","sample6.jpg"], input_image,cache_examples=False) | |
| gr.Examples(examples=["sample1.jpg", "sample2.jpg", "sample3.jpg", "sample4.jpg", "sample5.jpg", "sample6.jpg"],inputs=[input_image],cache_examples=False) | |
| # with gr.Accordion("Advanced options", open=False): | |
| with gr.Column(): | |
| gr.Markdown("# Model Output") | |
| gallery = gr.Image("Segmented Output") | |
| gr.Markdown("# No of classes and count") | |
| lbl = gr.Label("Classes Detected") | |
| # gallery = gr.Gallery( | |
| # label="Generated images", show_label=True, elem_id="gallery" | |
| # ).style(preview=True, grid=2, object_fit="scale-down") | |
| run_button.click(fn=run_grounded_sam, inputs=[input_image, text_prompt], | |
| outputs=[gallery, lbl]) | |
| block.launch(debug=args.debug, share=args.share, show_error=True) | |
| #block.launch(debug=True, show_error=True) | |