329 lines
11 KiB
Python
329 lines
11 KiB
Python
import cv2
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import math
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from collections import deque, Counter
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import pandas as pd # ← added for rebuilding labels
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# Modern MediaPipe Tasks API (no legacy solutions module)
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import mediapipe as mp
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from mediapipe.tasks import python
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from mediapipe.tasks.python import vision
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# PyTorch ≥ 2.6 checkpoint loading fix
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import numpy as np
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import numpy.core.multiarray
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import numpy.dtypes
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torch.serialization.add_safe_globals([
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np.ndarray,
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np.dtype,
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np.dtypes.Int64DType,
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np.core.multiarray._reconstruct
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])
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# ===============================
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# MODEL DEFINITION
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# ===============================
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class PositionalEncoding(nn.Module):
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def __init__(self, d_model, max_len=128):
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super().__init__()
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pe = torch.zeros(max_len, d_model)
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position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
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div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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self.register_buffer('pe', pe.unsqueeze(0))
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def forward(self, x):
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return x + self.pe[:, :x.size(1)]
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class TransformerASL(nn.Module):
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def __init__(self, input_dim, num_classes, d_model=256, nhead=8, num_layers=4):
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super().__init__()
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self.proj = nn.Linear(input_dim, d_model)
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self.norm_in = nn.LayerNorm(d_model)
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self.pos = PositionalEncoding(d_model, max_len=128)
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enc_layer = nn.TransformerEncoderLayer(
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d_model=d_model,
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nhead=nhead,
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dim_feedforward=d_model * 4,
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dropout=0.15,
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activation='gelu',
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batch_first=True,
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norm_first=True
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)
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self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers)
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self.head = nn.Sequential(
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nn.LayerNorm(d_model),
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nn.Dropout(0.25),
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nn.Linear(d_model, num_classes)
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)
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def forward(self, x, key_padding_mask=None):
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x = self.proj(x)
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x = self.norm_in(x)
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x = self.pos(x)
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x = self.encoder(x, src_key_padding_mask=key_padding_mask)
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x = x.mean(dim=1)
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return self.head(x)
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# ===============================
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# FEATURE EXTRACTION
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# ===============================
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def get_features_sequence(landmarks_seq, max_frames=100):
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if landmarks_seq is None or len(landmarks_seq) == 0:
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return None, None
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wrist = landmarks_seq[:, 0:1, :]
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landmarks_seq = landmarks_seq - wrist
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scale = np.linalg.norm(landmarks_seq[:, 9], axis=1, keepdims=True)
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scale = np.maximum(scale, 1e-6)
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landmarks_seq = landmarks_seq / scale[:, :, np.newaxis]
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landmarks_seq = np.nan_to_num(landmarks_seq, nan=0.0, posinf=0.0, neginf=0.0)
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landmarks_seq = np.clip(landmarks_seq, -10, 10)
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tips = [4, 8, 12, 16, 20]
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bases = [1, 5, 9, 13, 17]
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curls = [np.linalg.norm(landmarks_seq[:, t] - landmarks_seq[:, b], axis=1)
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for b, t in zip(bases, tips)]
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curl_features = np.stack(curls, axis=1)
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deltas = np.zeros_like(landmarks_seq)
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if len(landmarks_seq) > 1:
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deltas[1:] = landmarks_seq[1:] - landmarks_seq[:-1]
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pos_flat = landmarks_seq.reshape(len(landmarks_seq), -1)
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delta_flat = deltas.reshape(len(landmarks_seq), -1)
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seq = np.concatenate([pos_flat, delta_flat, curl_features], axis=1)
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T, F = seq.shape
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if T < max_frames:
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pad = np.zeros((max_frames - T, F), dtype=np.float32)
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seq_padded = np.concatenate([seq, pad], axis=0)
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else:
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seq_padded = seq[:max_frames]
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mask = np.zeros(max_frames, dtype=bool)
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mask[:min(T, max_frames)] = True
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return seq_padded.astype(np.float32), mask
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# ===============================
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# MANUAL DRAWING FUNCTION
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# ===============================
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HAND_CONNECTIONS = [
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(0, 1), (1, 2), (2, 3), (3, 4),
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(0, 5), (5, 6), (6, 7), (7, 8),
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(0, 9), (9, 10), (10, 11), (11, 12),
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(0, 13), (13, 14), (14, 15), (15, 16),
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(0, 17), (17, 18), (18, 19), (19, 20),
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(5, 9), (9, 13), (13, 17)
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]
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def draw_hand_landmarks(image, landmarks_list):
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h, w = image.shape[:2]
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# Draw connections (blue lines)
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for start_idx, end_idx in HAND_CONNECTIONS:
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start = landmarks_list[start_idx]
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end = landmarks_list[end_idx]
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start_pt = (int(start.x * w), int(start.y * h))
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end_pt = (int(end.x * w), int(end.y * h))
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cv2.line(image, start_pt, end_pt, (255, 0, 0), 2)
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# Draw landmarks (green circles)
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for lm in landmarks_list:
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x = int(lm.x * w)
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y = int(lm.y * h)
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cv2.circle(image, (x, y), 5, (0, 255, 0), -1)
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# ===============================
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# MAIN PROGRAM
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# ===============================
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print("Loading trained model...")
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checkpoint = torch.load("best_asl_transformer.pth", map_location="cpu")
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model = TransformerASL(
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input_dim=checkpoint['input_dim'],
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num_classes=checkpoint['num_classes'],
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d_model=checkpoint['d_model'],
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nhead=checkpoint['nhead'],
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num_layers=checkpoint['num_layers']
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)
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model.load_state_dict(checkpoint['model'])
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model.eval()
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# ─── FIX: Rebuild real sign names from train.csv ─────────────────────
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print("\n" + "=" * 70)
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print("Rebuilding sign name mapping from train.csv...")
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try:
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# CHANGE THIS PATH to where your train.csv actually is
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train_df = pd.read_csv("asl_kaggle/train.csv") # ← most important line!
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# Get unique signs, sorted (same order LabelEncoder usually uses)
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real_signs = sorted(train_df['sign'].unique())
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# Use real sign names instead of numbers
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label_encoder_classes = real_signs
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print("SUCCESS! Loaded real sign names")
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print("Number of classes:", len(real_signs))
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print("First 15 signs:", real_signs[:15])
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print("=" * 70 + "\n")
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except Exception as e:
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print("ERROR loading train.csv:", e)
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print("Falling back to numeric labels (you'll see numbers instead of words)")
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label_encoder_classes = checkpoint['label_encoder_classes']
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print("First 15 (still numbers):", label_encoder_classes[:15])
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print("=" * 70 + "\n")
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# MediaPipe Tasks setup
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BaseOptions = python.BaseOptions
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HandLandmarker = vision.HandLandmarker
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HandLandmarkerOptions = vision.HandLandmarkerOptions
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VisionRunningMode = vision.RunningMode
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MODEL_PATH = "hand_landmarker.task" # Make sure this file is in the folder
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options = HandLandmarkerOptions(
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base_options=BaseOptions(model_asset_path=MODEL_PATH),
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running_mode=VisionRunningMode.VIDEO,
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num_hands=1,
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min_hand_detection_confidence=0.5,
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min_hand_presence_confidence=0.5,
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min_tracking_confidence=0.5
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)
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landmarker = HandLandmarker.create_from_options(options)
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# Buffers
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MAX_FRAMES = 100
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sequence_buffer = []
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prediction_buffer = deque(maxlen=15)
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cap = cv2.VideoCapture(0)
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if not cap.isOpened():
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print("Cannot open webcam")
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exit()
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print("\nASL Recognition running - Press ESC to quit")
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print("Controls: ESC = quit | SPACE = clear | H = toggle landmarks\n")
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show_landmarks = True
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frame_timestamp_ms = 0
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while cap.isOpened():
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success, image = cap.read()
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if not success:
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break
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image = cv2.flip(image, 1)
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h, w = image.shape[:2]
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mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=image)
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frame_timestamp_ms += 33
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results = landmarker.detect_for_video(mp_image, frame_timestamp_ms)
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overlay = image.copy()
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cv2.rectangle(overlay, (10, 10), (520, 340), (0, 0, 0), -1)
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cv2.addWeighted(overlay, 0.65, image, 0.35, 0, image)
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if results.hand_landmarks:
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hand_landmarks_list = results.hand_landmarks[0]
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if show_landmarks:
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draw_hand_landmarks(image, hand_landmarks_list)
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current_frame = np.array(
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[[lm.x, lm.y, lm.z] for lm in hand_landmarks_list],
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dtype=np.float32
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)
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sequence_buffer.append(current_frame)
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if len(sequence_buffer) > MAX_FRAMES:
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sequence_buffer = sequence_buffer[-MAX_FRAMES:]
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if len(sequence_buffer) >= 10:
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seq_np = np.array(sequence_buffer)
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feats, mask = get_features_sequence(seq_np, MAX_FRAMES)
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if feats is not None:
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x = torch.from_numpy(feats).float().unsqueeze(0)
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key_padding_mask = torch.from_numpy(~mask).unsqueeze(0)
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with torch.no_grad():
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logits = model(x, key_padding_mask=key_padding_mask)
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probs = F.softmax(logits, dim=-1)[0]
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pred_idx = torch.argmax(probs).item()
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conf = probs[pred_idx].item()
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# Now using real sign names!
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sign = label_encoder_classes[pred_idx]
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if conf > 0.40:
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prediction_buffer.append(sign)
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final_sign = sign
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final_conf = conf
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if len(prediction_buffer) >= 6:
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final_sign = Counter(prediction_buffer).most_common(1)[0][0]
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try:
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final_conf = probs[label_encoder_classes.index(final_sign)].item()
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except:
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pass
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color = (0, 255, 100) if final_conf > 0.75 else (0, 220, 220)
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cv2.putText(image, f"Sign: {final_sign}", (25, 60),
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cv2.FONT_HERSHEY_SIMPLEX, 1.8, color, 4)
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cv2.putText(image, f"Conf: {final_conf:.1%}", (25, 110),
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cv2.FONT_HERSHEY_SIMPLEX, 0.9, (220, 220, 220), 2)
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top3_p, top3_i = torch.topk(probs, 3)
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for i, (p, idx) in enumerate(zip(top3_p, top3_i)):
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s = label_encoder_classes[idx.item()]
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cv2.putText(image, f"{i + 1}. {s:<18} {p:.1%}",
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(25, 155 + i * 40), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (200, 200, 200), 2)
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else:
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if len(sequence_buffer) < 25:
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sequence_buffer.clear()
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cv2.putText(image, "No hand detected", (25, 60),
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cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 0, 255), 3)
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cv2.putText(image, "ESC:quit SPACE:clear H:landmarks",
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(w - 480, h - 25), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (180, 180, 180), 1)
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cv2.imshow("ASL Recognition", image)
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key = cv2.waitKey(1) & 0xFF
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if key == 27:
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break
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elif key == 32:
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sequence_buffer.clear()
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prediction_buffer.clear()
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print("Buffers cleared")
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elif key in (ord('h'), ord('H')):
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show_landmarks = not show_landmarks
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print(f"Landmarks display: {'ON' if show_landmarks else 'OFF'}")
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cap.release()
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cv2.destroyAllWindows()
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landmarker.close()
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print("Recognition stopped.") |