idea-research--grounded-segment-anything
wehub-resource-sync
265 行
10 KiB
Python
265 行
10 KiB
Python
import argparse
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import os
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import sys
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import time
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import torch
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import numpy as np
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import json
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from PIL import Image
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from concurrent.futures import ThreadPoolExecutor
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sys.path.append(os.path.join(os.getcwd(), "GroundingDINO"))
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sys.path.append(os.path.join(os.getcwd(), "segment_anything"))
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# Grounding DINO imports
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import GroundingDINO.groundingdino.datasets.transforms as T
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from GroundingDINO.groundingdino.models import build_model
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from GroundingDINO.groundingdino.util.slconfig import SLConfig
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from GroundingDINO.groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
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# Segment Anything imports
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from segment_anything import sam_model_registry, sam_hq_model_registry, SamPredictor
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import cv2
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import matplotlib.pyplot as plt
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def load_image(image_path):
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image_pil = Image.open(image_path).convert("RGB")
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transform = T.Compose([
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T.RandomResize([800], max_size=1333),
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T.ToTensor(),
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
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])
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image, _ = transform(image_pil, None)
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return image_pil, image
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def load_model(model_config_path, model_checkpoint_path, device):
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print("Loading model from...........", device)
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args = SLConfig.fromfile(model_config_path)
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args.device = device
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model = build_model(args)
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# Load the model checkpoint onto the specific GPU
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checkpoint = torch.load(model_checkpoint_path, map_location=device)
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model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
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model.eval()
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model.to(device)
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return model
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def get_grounding_output(model, image, caption, box_threshold, text_threshold, device="cpu"):
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caption = caption.lower().strip()
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if not caption.endswith("."):
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caption += "."
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model.to(device)
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image = image.to(device)
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with torch.no_grad():
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outputs = model(image[None], captions=[caption])
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logits = outputs["pred_logits"].sigmoid()[0] # Keep it on the device
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boxes = outputs["pred_boxes"][0] # Keep it on the device
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filt_mask = logits.max(dim=1)[0] > box_threshold
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logits_filt = logits[filt_mask]
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boxes_filt = boxes[filt_mask]
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tokenlizer = model.tokenizer
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tokenized = tokenlizer(caption)
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pred_phrases = []
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for logit, box in zip(logits_filt, boxes_filt):
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pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)
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pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
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return boxes_filt, pred_phrases
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def process_image(image_path, model, predictor, output_dir, text_prompt, box_threshold, text_threshold, device):
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# Load the image and move to GPU
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image_pil, image = load_image(image_path)
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# image_pil.save(os.path.join(output_dir, f"raw_image_{os.path.basename(image_path)}.jpg"))
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# Run GroundingDINO model to get bounding boxes and labels
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boxes_filt, pred_phrases = get_grounding_output(
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model, image, text_prompt, box_threshold, text_threshold, device=device
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)
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# Load SAM model onto GPU
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image_cv = cv2.imread(image_path)
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image_cv = cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB)
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predictor.set_image(image_cv)
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# Convert boxes to original image size
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size = image_pil.size
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H, W = size[1], size[0]
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for i in range(boxes_filt.size(0)):
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boxes_filt[i] = boxes_filt[i] * torch.tensor([W, H, W, H], device=device)
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boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
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boxes_filt[i][2:] += boxes_filt[i][:2]
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# Transform boxes to be compatible with SAM
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transformed_boxes = predictor.transform.apply_boxes_torch(boxes_filt, image_cv.shape[:2]).to(device)
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# Get masks using SAM
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masks, _, _ = predictor.predict_torch(
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point_coords=None,
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point_labels=None,
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boxes=transformed_boxes.to(device),
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multimask_output=False,
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)
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# Visualization and saving
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plt.figure(figsize=(10, 10))
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plt.imshow(image_cv)
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# for mask in masks:
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# show_mask(mask.cpu().numpy(), plt.gca(), random_color=True)
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for box, label in zip(boxes_filt, pred_phrases):
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show_box(box.cpu().numpy(), plt.gca(), label)
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image_base_name = os.path.basename(image_path).split('.')[0]
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plt.axis('off')
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plt.savefig(
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os.path.join(output_dir, f"grounded_sam_output_{image_base_name}.jpg"),
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bbox_inches="tight", dpi=300, pad_inches=0.0
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)
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plt.close()
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save_mask_data(output_dir, masks, boxes_filt, pred_phrases, image_base_name)
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# Clear GPU memory
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del image, transformed_boxes, masks # model, sam
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# torch.cuda.empty_cache()
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def show_mask(mask, ax, random_color=False):
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if random_color:
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color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
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else:
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color = np.array([30/255, 144/255, 255/255, 0.6])
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h, w = mask.shape[-2:]
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# print("mask.shape:", mask.shape)
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mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
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ax.imshow(mask_image)
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def show_box(box, ax, label):
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x0, y0 = box[0], box[1]
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w, h = box[2] - box[0], box[3] - box[1]
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ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0, 0, 0, 0), lw=2))
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ax.text(x0, y0, label)
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def save_mask_data(output_dir, mask_list, box_list, label_list, image_base_name=''):
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value = 0 # 0 for background
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mask_img = torch.zeros(mask_list.shape[-2:], device=mask_list.device)
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for idx, mask in enumerate(mask_list):
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mask_img[mask[0] == True] = value + idx + 1
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plt.figure(figsize=(10, 10))
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plt.imshow(mask_img.cpu().numpy())
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plt.axis('off')
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plt.savefig(os.path.join(output_dir, f'{image_base_name}.jpg'), bbox_inches="tight", dpi=300, pad_inches=0.0)
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plt.close()
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json_data = [{
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'value': value,
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'label': 'background'
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}]
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for label, box in zip(label_list, box_list):
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value += 1
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name, logit = label.split('(')
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logit = logit[:-1] # the last is ')'
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json_data.append({
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'value': value,
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'label': name,
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'logit': float(logit),
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'box': box.cpu().numpy().tolist(),
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})
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with open(os.path.join(output_dir, f'{image_base_name}.json'), 'w') as f:
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json.dump(json_data, f)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Grounded-Segment-Anything Demo", add_help=True)
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parser.add_argument("--config", type=str, required=True, help="path to config file")
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parser.add_argument("--grounded_checkpoint", type=str, required=True, help="path to checkpoint file")
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parser.add_argument("--sam_version", type=str, default="vit_h", required=False, help="SAM ViT version: vit_b / vit_l / vit_h")
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parser.add_argument("--sam_checkpoint", type=str, required=False, help="path to sam checkpoint file")
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parser.add_argument("--sam_hq_checkpoint", type=str, default=None, help="path to sam-hq checkpoint file")
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parser.add_argument("--use_sam_hq", action="store_true", help="using sam-hq for prediction")
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parser.add_argument("--input_path", type=str, required=True, help="path to directory containing image files")
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parser.add_argument("--text_prompt", type=str, required=True, help="text prompt")
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parser.add_argument("--output_dir", "-o", type=str, default="outputs", required=True, help="output directory")
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parser.add_argument("--box_threshold", type=float, default=0.3, help="box threshold")
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parser.add_argument("--text_threshold", type=float, default=0.25, help="text threshold")
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parser.add_argument("--device", type=str, default="cuda", help="device to run the inference on, e.g., 'cuda' or 'cuda:0'")
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args = parser.parse_args()
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torch.backends.cudnn.enabled = False
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torch.backends.cudnn.benchmark = True
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start_time = time.time()
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# Determine if we are using a single GPU or all available GPUs
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if args.device == "cuda":
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if torch.cuda.device_count() > 1:
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device_list = [torch.device(f"cuda:{i}") for i in range(torch.cuda.device_count())] # Use all GPUs
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else:
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device_list = [torch.device("cuda:0")] # Default to first GPU
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else:
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device_list = [torch.device(args.device)]
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print("device_list:", device_list)
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# Get list of images
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image_paths = [os.path.join(args.input_path, img) for img in os.listdir(args.input_path) if img.endswith(('.png', '.jpg', '.jpeg'))]
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# Split images among available GPUs
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image_batches = np.array_split(image_paths, len(device_list))
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print("Processing images:", image_batches)
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# Function to process a batch of images on the specified device
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def process_batch(batch_images, model_config, model_checkpoint, sam_version, sam_checkpoint, sam_hq_checkpoint, use_sam_hq, device, output_dir):
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# Load model onto GPU
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torch.cuda.set_device(device)
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model = load_model(model_config, model_checkpoint, device)
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# Load SAM model onto GPU
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if use_sam_hq:
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sam = sam_hq_model_registry[sam_version](checkpoint=sam_hq_checkpoint).to(device)
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else:
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sam = sam_model_registry[sam_version](checkpoint=sam_checkpoint).to(device)
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# Move model to the correct device
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device = torch.device(device)
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model.to(device)
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sam.to(device)
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predictor = SamPredictor(sam)
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for image_path in batch_images:
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# Process each image
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print("Processing image:", image_path)
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process_image(
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image_path=image_path,
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model=model,
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predictor=predictor,
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output_dir=output_dir,
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text_prompt=args.text_prompt,
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box_threshold=args.box_threshold,
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text_threshold=args.text_threshold,
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device=device
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)
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print("Image processing complete {}".format(image_path))
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# Clear GPU memory after processing the batch
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# del model, sam
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torch.cuda.empty_cache()
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# Use ThreadPoolExecutor to parallelize the processing across GPUs
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with ThreadPoolExecutor(max_workers=len(device_list)*2) as executor:
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futures = []
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for i, device in enumerate(device_list):
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print(f"Processing images on device {device}")
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print("Image batches for each GPU:", len(image_batches[i]))
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futures.append(executor.submit(
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process_batch, image_batches[i], args.config, args.grounded_checkpoint, args.sam_version, args.sam_checkpoint, args.sam_hq_checkpoint, args.use_sam_hq, device, args.output_dir
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))
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# Wait for all threads to complete
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for future in futures:
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future.result()
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print("Processing complete. Results saved to the output directory.")
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print(f"Total time taken: {time.time() - start_time:.2f} seconds") |