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import os |
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import sys |
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import logging |
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pth = '/'.join(sys.path[0].split('/')[:-1]) |
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sys.path.insert(0, pth) |
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from PIL import Image |
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import numpy as np |
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np.random.seed(0) |
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import cv2 |
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import torch |
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from torchvision import transforms |
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from utils.arguments import load_opt_command |
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from detectron2.data import MetadataCatalog |
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from modeling.language.loss import vl_similarity |
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from modeling.BaseModel import BaseModel |
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from modeling import build_model |
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from utils.visualizer import Visualizer |
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from utils.distributed import init_distributed |
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logger = logging.getLogger(__name__) |
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def main(args=None): |
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''' |
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Main execution point for PyLearn. |
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''' |
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opt, cmdline_args = load_opt_command(args) |
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if cmdline_args.user_dir: |
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absolute_user_dir = os.path.abspath(cmdline_args.user_dir) |
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opt['base_path'] = absolute_user_dir |
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opt = init_distributed(opt) |
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pretrained_pth = os.path.join(opt['RESUME_FROM']) |
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output_root = './output' |
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image_list = ['inference/images/coco/000.jpg', 'inference/images/coco/001.jpg', 'inference/images/coco/002.jpg', 'inference/images/coco/003.jpg'] |
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text = ['pizza on the plate'] |
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model = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_pth).eval().cuda() |
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model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(["background", "background"], is_eval=False) |
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t = [] |
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t.append(transforms.Resize(224, interpolation=Image.BICUBIC)) |
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transform_ret = transforms.Compose(t) |
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t = [] |
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t.append(transforms.Resize(512, interpolation=Image.BICUBIC)) |
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transform_grd = transforms.Compose(t) |
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metadata = MetadataCatalog.get('ade20k_panoptic_train') |
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color = [0/255, 255/255, 0/255] |
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with torch.no_grad(): |
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batch_inputs = [] |
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candidate_list = [] |
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for j in range(len(image_list)): |
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image_ori = Image.open(image_list[j]) |
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width = image_ori.size[0] |
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height = image_ori.size[1] |
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image = transform_ret(image_ori) |
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image = np.asarray(image) |
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candidate_list += [image] |
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image_list += [image] |
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image_ori = np.asarray(image_ori) |
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images = torch.from_numpy(image.copy()).permute(2,0,1).cuda() |
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batch_inputs += [{'image': images, 'height': height, 'width': width}] |
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outputs = model.model.evaluate(batch_inputs) |
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v_emb = torch.cat([x['captions'][-1:] for x in outputs]) |
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v_emb = v_emb / (v_emb.norm(dim=-1, keepdim=True) + 1e-7) |
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model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(text, is_eval=False, name='caption', prompt=False) |
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t_emb = getattr(model.model.sem_seg_head.predictor.lang_encoder, '{}_text_embeddings'.format('caption')) |
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temperature = model.model.sem_seg_head.predictor.lang_encoder.logit_scale |
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logits = vl_similarity(v_emb, t_emb, temperature) |
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max_prob, max_id = logits.softmax(0).max(dim=0) |
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frame_pth = image_list[max_id.item()] |
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image_ori = Image.open(frame_pth) |
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width = image_ori.size[0] |
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height = image_ori.size[1] |
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image = transform_grd(image_ori) |
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image = np.asarray(image) |
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image_ori = np.asarray(image_ori) |
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images = torch.from_numpy(image.copy()).permute(2,0,1).cuda() |
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batch_inputs = [{'image': images, 'height': height, 'width': width, 'groundings': {'texts': [text]}}] |
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outputs = model.model.evaluate_grounding(batch_inputs, None) |
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visual = Visualizer(image_ori, metadata=metadata) |
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grd_masks = (outputs[0]['grounding_mask'] > 0).float().cpu().numpy() |
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for text_, mask in zip(text, grd_masks): |
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demo = visual.draw_binary_mask(mask, color=color, text='', alpha=0.5) |
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region_img = demo.get_image() |
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candidate_list[max_id.item()] = region_img |
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out_image = np.zeros((224*4+60, 448*4, 3)) |
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for ii, img in enumerate(candidate_list): |
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img = cv2.resize(img, (448, 224)) |
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if ii != max_id.item(): |
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img = img * 0.4 |
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hs, ws = 60+(ii//4)*224, (ii%4)*448 |
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out_image[hs:hs+224,ws:ws+448,:] = img[:,:,::-1] |
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font = cv2.FONT_HERSHEY_DUPLEX |
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fontScale = 1.2 |
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thickness = 3 |
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lineType = 2 |
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bottomLeftCornerOfText = (10, 40) |
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fontColor = [255,255,255] |
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cv2.putText(out_image, text[0], |
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bottomLeftCornerOfText, |
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font, |
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fontScale, |
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fontColor, |
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thickness, |
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lineType) |
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x1 = (max_id.item()%4) * 448 |
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y1 = (max_id.item()//4) * 224 + 60 |
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cv2.rectangle(out_image, (x1, y1), (x1+448, y1+224), (0,0,255), 3) |
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x1 = x1 |
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y1 = y1 + 224 - 30 |
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cv2.rectangle(out_image, (x1+2, y1), (x1+60, y1+28), (0,0,0), -1) |
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fontScale = 1.0 |
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thickness = 2 |
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bottomLeftCornerOfText = (x1, y1+21) |
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cv2.putText(out_image, str(max_prob.item())[0:4], |
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bottomLeftCornerOfText, |
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font, |
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0.8, |
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[0,0,255], |
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thickness, |
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lineType) |
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if not os.path.exists(output_root): |
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os.makedirs(output_root) |
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cv2.imwrite(os.path.join(output_root, 'region_retrieval.png'), out_image) |
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if __name__ == "__main__": |
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main() |
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sys.exit(0) |
