Update app.py

app.py

@@ -34,10 +34,109 @@ import fairseq
 from fairseq import checkpoint_utils, options, tasks, utils
 from fairseq.dataclass.configs import GenerationConfig
 from huggingface_hub import hf_hub_download
+import gradio as gr
 
 ckpt_path = hf_hub_download('vumichien/AV-HuBERT', 'model.pt')
 user_dir = "/home/user/app/av_hubert/avhubert"
 face_detector_path = "/home/user/app/mmod_human_face_detector.dat"
 face_predictor_path = "/home/user/app/shape_predictor_68_face_landmarks.dat"
 mean_face_path = "/home/user/app/20words_mean_face.npy"
-mouth_roi_path = "/home/user/app/roi.mp4"
+mouth_roi_path = "/home/user/app/roi.mp4"
+
+def detect_landmark(image, detector, predictor):
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    face_locations  = detector(gray, 1)
+    coords = None
+    for (_, face_location) in enumerate(face_locations):
+        if torch.cuda.is_available():
+            rect = face_location.rect
+        else:
+            rect = face_location
+        shape = predictor(gray, rect)
+        coords = np.zeros((68, 2), dtype=np.int32)
+        for i in range(0, 68):
+            coords[i] = (shape.part(i).x, shape.part(i).y)
+    return coords
+
+def preprocess_video(input_video_path):
+    if torch.cuda.is_available():
+        detector = dlib.cnn_face_detection_model_v1(face_detector_path)
+    else:
+        detector = dlib.get_frontal_face_detector()
+    
+    predictor = dlib.shape_predictor(face_predictor_path)
+    STD_SIZE = (256, 256)
+    mean_face_landmarks = np.load(mean_face_path)
+    stablePntsIDs = [33, 36, 39, 42, 45]
+    videogen = skvideo.io.vread(input_video_path)
+    frames = np.array([frame for frame in videogen])
+    landmarks = []
+    for frame in tqdm(frames):
+        landmark = detect_landmark(frame, detector, predictor)
+        landmarks.append(landmark)
+    preprocessed_landmarks = landmarks_interpolate(landmarks)
+    rois = crop_patch(input_video_path, preprocessed_landmarks, mean_face_landmarks, stablePntsIDs, STD_SIZE, 
+                          window_margin=12, start_idx=48, stop_idx=68, crop_height=96, crop_width=96)
+    write_video_ffmpeg(rois, mouth_roi_path, "/usr/bin/ffmpeg")
+    return mouth_roi_path
+
+def predict(process_video):
+    num_frames = int(cv2.VideoCapture(process_video).get(cv2.CAP_PROP_FRAME_COUNT))
+    data_dir = tempfile.mkdtemp()
+    tsv_cont = ["/\n", f"test-0\t{process_video}\t{None}\t{num_frames}\t{int(16_000*num_frames/25)}\n"]
+    label_cont = ["DUMMY\n"]
+    with open(f"{data_dir}/test.tsv", "w") as fo:
+      fo.write("".join(tsv_cont))
+    with open(f"{data_dir}/test.wrd", "w") as fo:
+      fo.write("".join(label_cont))
+    utils.import_user_module(Namespace(user_dir=user_dir))
+    modalities = ["video"]
+    gen_subset = "test"
+    gen_cfg = GenerationConfig(beam=20)
+    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task([ckpt_path])
+    models = [model.eval().cuda() if torch.cuda.is_available() else model.eval() for model in models]
+    saved_cfg.task.modalities = modalities
+    saved_cfg.task.data = data_dir
+    saved_cfg.task.label_dir = data_dir
+    task = tasks.setup_task(saved_cfg.task)
+    task.load_dataset(gen_subset, task_cfg=saved_cfg.task)
+    generator = task.build_generator(models, gen_cfg)
+
+    def decode_fn(x):
+        dictionary = task.target_dictionary
+        symbols_ignore = generator.symbols_to_strip_from_output
+        symbols_ignore.add(dictionary.pad())
+        return task.datasets[gen_subset].label_processors[0].decode(x, symbols_ignore)
+
+    itr = task.get_batch_iterator(dataset=task.dataset(gen_subset)).next_epoch_itr(shuffle=False)
+    sample = next(itr)
+    if torch.cuda.is_available():
+        sample = utils.move_to_cuda(sample)
+    hypos = task.inference_step(generator, models, sample)
+    ref = decode_fn(sample['target'][0].int().cpu())
+    hypo = hypos[0][0]['tokens'].int().cpu()
+    hypo = decode_fn(hypo)
+    return hypo
+
+
+# ---- Gradio Layout -----
+demo = gr.Blocks()
+demo.encrypt = False
+text_output = gr.Textbox()
+with demo:
+    with gr.Row():  
+        video_in = gr.Video(label="Input Video", mirror_webcam=False, interactive=True)
+        video_out = gr.Video(label="Audio Visual Video", mirror_webcam=False, interactive=True)  
+    with gr.Row():
+        detect_landmark_btn = gr.Button("Detect landmark")
+        detect_landmark_btn.click(preprocess_video, [video_in], [
+            video_out])
+        predict_btn = gr.Button("Predict")
+        predict_btn.click(predict, [video_out], [
+            text_output])
+    with gr.Row():
+        # video_lip = gr.Video(label="Audio Visual Video", mirror_webcam=False) 
+        text_output.render()
+        
+        
+demo.launch(debug=True)