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8752957 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | import os, json
import numpy as np
import tensorflow as tf
import gradio as gr
import cv2
import mediapipe as mp
SEQ_LEN = 60
TOPK = 5
MODEL_PATH = "best_model.keras"
MEAN_PATH = "global_mean.npy"
STD_PATH = "global_std.npy"
LABEL_PATH = "label_map.json"
mean = np.load(MEAN_PATH).astype(np.float32)
std = np.load(STD_PATH).astype(np.float32)
with open(LABEL_PATH, "r", encoding="utf-8") as f:
lm = json.load(f)
id2label = {int(k): v for k, v in lm["id2label"].items()}
num_classes = len(id2label)
feature_dim = int(mean.shape[0])
@tf.keras.utils.register_keras_serializable()
class AttnPool(tf.keras.layers.Layer):
def __init__(self, units=128, **kwargs):
super().__init__(**kwargs)
self.units = units
self.supports_masking = True
self.d1 = tf.keras.layers.Dense(units, activation="tanh")
self.d2 = tf.keras.layers.Dense(1)
def build(self, input_shape):
self.d1.build(input_shape)
self.d2.build((input_shape[0], input_shape[1], self.units))
super().build(input_shape)
def call(self, x, mask=None):
s = self.d2(self.d1(x))
s = tf.squeeze(s, axis=-1)
if mask is not None:
mask_f = tf.cast(mask, tf.float32)
s = s + (1.0 - mask_f) * (-1e9)
a = tf.nn.softmax(s, axis=1)
a = tf.expand_dims(a, axis=-1)
return tf.reduce_sum(x * a, axis=1)
def compute_mask(self, inputs, mask=None):
return None
def get_config(self):
c = super().get_config()
c.update({"units": self.units})
return c
model = tf.keras.models.load_model(MODEL_PATH, custom_objects={"AttnPool": AttnPool})
mp_holistic = mp.solutions.holistic
def landmarks_to_vec(res):
def flat_landmarks(lms, dims):
if lms is None:
return np.zeros((dims,), dtype=np.float32)
arr = []
for p in lms.landmark:
if dims == 4:
arr.extend([p.x, p.y, p.z, getattr(p, "visibility", 0.0)])
else:
arr.extend([p.x, p.y, p.z])
return np.array(arr, dtype=np.float32)
pose = flat_landmarks(res.pose_landmarks, 4)
face = flat_landmarks(res.face_landmarks, 3)
lh = flat_landmarks(res.left_hand_landmarks, 3)
rh = flat_landmarks(res.right_hand_landmarks, 3)
v = np.concatenate([pose, face, lh, rh], axis=0).astype(np.float32)
if v.shape[0] != feature_dim:
if v.shape[0] > feature_dim:
v = v[:feature_dim]
else:
v = np.pad(v, (0, feature_dim - v.shape[0]))
return v
def build_sequence_from_video(video_path):
cap = cv2.VideoCapture(video_path)
frames = []
with mp_holistic.Holistic(
static_image_mode=False,
model_complexity=1,
enable_segmentation=False,
refine_face_landmarks=False
) as holistic:
total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
if total <= 0:
total = 300
idxs = np.linspace(max(0, total-1), 0, num=SEQ_LEN, dtype=int)[::-1]
idxs = sorted(list(set(idxs)))
want = set(idxs)
i = 0
got = {}
while True:
ok, frame = cap.read()
if not ok:
break
if i in want:
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
res = holistic.process(rgb)
got[i] = landmarks_to_vec(res)
i += 1
cap.release()
vecs = [got[k] for k in sorted(got.keys())]
if len(vecs) == 0:
X = np.zeros((SEQ_LEN, feature_dim), dtype=np.float32)
mask = np.zeros((SEQ_LEN,), dtype=np.bool_)
return X, mask
X = np.stack(vecs, axis=0).astype(np.float32)
if X.shape[0] >= SEQ_LEN:
X = X[-SEQ_LEN:]
mask = np.ones((SEQ_LEN,), dtype=np.bool_)
else:
pad = np.zeros((SEQ_LEN - X.shape[0], feature_dim), dtype=np.float32)
X = np.vstack([pad, X])
mask = np.zeros((SEQ_LEN,), dtype=np.bool_)
mask[-X.shape[0]:] = True
if mask.any():
X[mask] = (X[mask] - mean) / std
return X, mask
def predict_video(video_path):
X, _ = build_sequence_from_video(video_path)
prob = model.predict(X[None, ...], verbose=0)[0]
idx = np.argsort(prob)[::-1][:TOPK]
out = [(id2label[int(i)], float(prob[int(i)])) for i in idx]
return out
def ui_predict(video):
preds = predict_video(video)
return {k: v for k, v in preds}
demo = gr.Interface(
fn=ui_predict,
inputs=gr.Video(sources=["webcam"], format="mp4"),
outputs=gr.Label(num_top_classes=TOPK),
title="MSL Real-time-ish Demo (Webcam Video -> Prediction)",
description="Record a short clip (2-4s) and the model predicts the gloss."
)
if __name__ == "__main__":
demo.launch()
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