| import torch | |
| import cv2 | |
| import videotransforms | |
| import numpy as np | |
| import gradio as gr | |
| from einops import rearrange | |
| from torchvision import transforms | |
| from pytorch_i3d import InceptionI3d | |
| def preprocess(vidpath): | |
| cap = cv2.VideoCapture(vidpath) | |
| frames = [] | |
| cap.set(cv2.CAP_PROP_POS_FRAMES, 0) | |
| num = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) | |
| for _ in range(num): | |
| _, img = cap.read() | |
| w, h, c = img.shape | |
| if w < 226 or h < 226: | |
| d = 226. - min(w, h) | |
| sc = 1 + d / min(w, h) | |
| img = cv2.resize(img, dsize=(0, 0), fx=sc, fy=sc) | |
| img = (img / 255.) * 2 - 1 | |
| frames.append(img) | |
| frames = torch.Tensor(np.asarray(frames, dtype=np.float32)) | |
| transform = transforms.Compose([videotransforms.CenterCrop(224)]) | |
| frames = transform(frames) | |
| frames = rearrange(frames, 't h w c-> 1 c t h w') | |
| return frames | |
| def classify(video,dataset='WLASL100'): | |
| to_load = { | |
| 'WLASL100':{'logits':100,'path':'weights/asl100/FINAL_nslt_100_iters=896_top1=65.89_top5=84.11_top10=89.92.pt'}, | |
| 'WLASL2000':{'logits':2000,'path':'weights/asl2000/FINAL_nslt_2000_iters=5104_top1=32.48_top5=57.31_top10=66.31.pt'} | |
| } | |
| input = preprocess(video) | |
| model = InceptionI3d() | |
| model.load_state_dict(torch.load('weights/rgb_imagenet.pt')) | |
| model.replace_logits(to_load[dataset]['logits']) | |
| model.load_state_dict(torch.load(to_load[dataset]['path'])) | |
| model.eval() | |
| with torch.no_grad(): | |
| per_frame_logits = model(input) | |
| predictions = rearrange(per_frame_logits,'1 j k -> j k') | |
| predictions = torch.mean(predictions, dim = 1) | |
| top = torch.argmax(predictions).item() | |
| _, index = torch.topk(predictions,10) | |
| index = index.numpy() | |
| with open('wlasl_class_list.txt') as f: | |
| idx2label = dict() | |
| for line in f: | |
| idx2label[int(line.split()[0])]=line.split()[1] | |
| predictions = torch.nn.functional.softmax(predictions, dim=0).numpy() | |
| return {idx2label[i]:float(predictions[i]) for i in index} | |
| title = "I3D Sign Language Recognition" | |
| description = "Description here" | |
| examples = [['videos/no.mp4','WLASL100'],['videos/all.mp4','WLASL100'],['videos/blue.mp4','WLASL2000'],['videos/white.mp4','WLASL2000'],['videos/accident.mp4','WLASL2000']] | |
| gr.Interface( fn=classify, | |
| inputs=[gr.inputs.Video(label="VIDEO"),gr.inputs.Dropdown(choices=['WLASL100','WLASL2000'], default='WLASL100', label='DATASET USED')], | |
| outputs=[gr.outputs.Label(num_top_classes=5, label='Top 5 Predictions')], | |
| allow_flagging="never", | |
| title=title, | |
| description=description, | |
| examples=examples).launch(cache_examples=True) | |