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7b3277f faefa98 3d53332 7b3277f faefa98 7b3277f faefa98 7b3277f faefa98 7b3277f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | import json
import os
import urllib.request
from collections import deque
import cv2
import gradio as gr
import numpy as np
import onnxruntime as ort
ONNX_PATH = "mlp_asl.onnx"
CLASSES_PATH = "mlp_classes.json"
HAND_MODEL_PATH = "hand_landmarker.task"
HAND_MODEL_URL = ("https://storage.googleapis.com/mediapipe-models/hand_landmarker/"
"hand_landmarker/float16/1/hand_landmarker.task")
SMOOTH_WINDOW = 10
CONF_THRESH = 0.65
def ensure_hand_model(path=HAND_MODEL_PATH):
if not os.path.exists(path):
urllib.request.urlretrieve(HAND_MODEL_URL, path)
return path
def build_landmarker(model_path, det_conf=0.75, track_conf=0.6):
import mediapipe as mp
from mediapipe.tasks.python import BaseOptions
from mediapipe.tasks.python.vision import (
HandLandmarker, HandLandmarkerOptions, RunningMode,
)
options = HandLandmarkerOptions(
base_options=BaseOptions(model_asset_path=model_path),
running_mode=RunningMode.IMAGE,
num_hands=1,
min_hand_detection_confidence=det_conf,
min_hand_presence_confidence=det_conf,
min_tracking_confidence=track_conf,
)
return HandLandmarker.create_from_options(options), mp
with open(CLASSES_PATH) as f:
CLASS_NAMES = json.load(f)
SESSION = ort.InferenceSession(ONNX_PATH, providers=["CPUExecutionProvider"])
INPUT_NAME = SESSION.get_inputs()[0].name
LANDMARKER, MP = build_landmarker(ensure_hand_model())
HISTORY = deque(maxlen=SMOOTH_WINDOW)
def normalize_landmarks(pts):
wrist = pts[0]
centered = pts - wrist
scale = np.linalg.norm(centered[9])
if scale < 1e-6:
scale = 1.0
return (centered / scale).flatten().astype(np.float32)
def softmax(logits):
z = logits - logits.max()
e = np.exp(z)
return e / e.sum()
def smooth_prediction(history):
scores = {}
for label, conf in history:
scores[label] = scores.get(label, 0.0) + conf
total = sum(scores.values())
if total <= 0:
return "...", 0.0
winner = max(scores, key=scores.get)
smooth_conf = scores[winner] / total
if smooth_conf < CONF_THRESH:
return "...", smooth_conf
return winner, smooth_conf
def draw_landmarks(frame, lms, w, h, color=(0, 255, 0)):
for lm in lms:
cv2.circle(frame, (int(lm.x * w), int(lm.y * h)), 4, color, -1)
def draw_confidence_bar(frame, conf, x, y, width=240, height=24):
cv2.rectangle(frame, (x, y), (x + width, y + height), (60, 60, 60), -1)
fill = int(width * conf)
green = int(80 + 175 * conf)
cv2.rectangle(frame, (x, y), (x + fill, y + height), (0, green, 0), -1)
cv2.rectangle(frame, (x, y), (x + width, y + height), (200, 200, 200), 1)
cv2.putText(frame, f"{conf * 100:.1f}%", (x + width + 10, y + height - 4),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
def predict(frame):
if frame is None:
return None
frame = np.asarray(frame)
if frame.ndim == 2:
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
if frame.shape[2] == 4:
frame = frame[:, :, :3]
frame = np.ascontiguousarray(frame, dtype=np.uint8).copy()
h, w = frame.shape[:2]
try:
mp_image = MP.Image(image_format=MP.ImageFormat.SRGB, data=frame)
result = LANDMARKER.detect(mp_image)
except Exception as exc:
cv2.putText(frame, f"err: {type(exc).__name__}", (20, 44),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 80, 80), 2)
return frame
if not result.hand_landmarks:
HISTORY.clear()
cv2.putText(frame, "No hand detected", (20, 44),
cv2.FONT_HERSHEY_SIMPLEX, 1.1, (255, 80, 80), 3)
return frame
lms = result.hand_landmarks[0]
pts = np.array([[lm.x, lm.y, lm.z] for lm in lms], dtype=np.float32)
logits = SESSION.run(None, {INPUT_NAME: normalize_landmarks(pts).reshape(1, -1)})[0][0]
probs = softmax(logits)
idx = int(probs.argmax())
HISTORY.append((CLASS_NAMES[idx], float(probs[idx])))
label, conf = smooth_prediction(HISTORY)
draw_landmarks(frame, lms, w, h)
color = (0, 255, 0) if label != "..." else (255, 180, 0)
cv2.putText(frame, label, (20, 70),
cv2.FONT_HERSHEY_SIMPLEX, 2.2, color, 5)
draw_confidence_bar(frame, conf, 20, 90)
return frame
DESCRIPTION = (
"Show an ASL letter (A–Z) or digit (0–9) to your webcam and hold steady. "
"Dataset: ASL-HG by Pranto et al. (2026) — "
"https://www.sciencedirect.com/science/article/pii/S2352340926000454 | "
"Model by Doruk Doğular (nocontextdoruk)."
)
with gr.Blocks(title="ASL Hand Gesture Recognizer") as demo:
gr.Markdown("# ASL Hand Gesture Recognizer")
gr.Markdown(DESCRIPTION)
with gr.Row():
webcam = gr.Image(sources=["webcam"], streaming=True, type="numpy", label="Webcam")
output = gr.Image(label="Prediction", type="numpy")
webcam.stream(predict, inputs=webcam, outputs=output,
stream_every=0.15, concurrency_limit=30)
demo.queue()
if __name__ == "__main__":
demo.launch()
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