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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+hand_landmarker.task filter=lfs diff=lfs merge=lfs -text

classes.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cf20d80fd50ea14a57bc51e0b1084f37ccd6afc379ed8ad31a946e4c920b1bdc
+size 377

hand_landmarker.task

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fbc2a30080c3c557093b5ddfc334698132eb341044ccee322ccf8bcf3607cde1
+size 7819105

nsl_model_v0-5.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9736a2d0ffdccf2d96a0745b87f46e80d5d143d0a0cb68b7be2ddfa277d1fcdd
+size 241688

nsl_model_v0-9.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c1a58332f83c112a8ba77e4c64bbc5640f2e80cb0bfbfb6ce855432f5be63b6
+size 250476

nsl_model_v1.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9ddd7872f2affe87a481f40645eeaa5ad36575f89e0f5ef95f03326b023e00bf
+size 250476

run-live-alpha.py

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+import cv2
+import mediapipe as mp
+import numpy as np
+import tensorflow as tf
+import time
+import os
+
+# --- Config ---
+MODEL_PATH = 'hand_landmarker.task'
+THRESHOLD = 0.7 # confidence threshold (70%)
+
+BaseOptions = mp.tasks.BaseOptions
+HandLandmarker = mp.tasks.vision.HandLandmarker
+HandLandmarkerOptions = mp.tasks.vision.HandLandmarkerOptions
+VisionRunningMode = mp.tasks.vision.RunningMode
+
+# Loading model
+if not os.path.exists('nsl_model_v1.h5'):
+    print("Run training script first!")
+    exit()
+
+model = tf.keras.models.load_model('nsl_model_v1.h5')
+classes = np.load('classes.npy', allow_pickle=True)
+print(f"Loaded classes: {classes}")
+
+# Helper to draw
+def draw_landmarks(image, landmarks):
+    h, w, _ = image.shape
+    # Draw connections (standard MP)
+    HAND_CONNECTIONS = [
+        (0, 1), (1, 2), (2, 3), (3, 4),           # thumb
+        (0, 5), (5, 6), (6, 7), (7, 8),           # index
+        (5, 9), (9, 10), (10, 11), (11, 12),      # middle
+        (9, 13), (13, 14), (14, 15), (15, 16),    # ring
+        (13, 17), (17, 18), (18, 19), (19, 20),   # pinky
+        (0, 17)                                   # wrist to pinky base
+    ]
+
+    for start_idx, end_idx in HAND_CONNECTIONS:
+        start = landmarks[start_idx]
+        end = landmarks[end_idx]
+        cv2.line(image, (int(start.x * w), int(start.y * h)), (int(end.x * w), int(end.y * h)), (200, 200, 200), 2)
+    # Draw points
+    for lm in landmarks:
+        cv2.circle(image, (int(lm.x * w), int(lm.y * h)), 4, (0, 0, 255), -1)
+
+# Initialize Landmarker
+options = HandLandmarkerOptions(
+    base_options=BaseOptions(model_asset_path=MODEL_PATH),
+    running_mode=VisionRunningMode.VIDEO,
+    num_hands=1,
+    min_hand_detection_confidence=0.5
+)
+
+with HandLandmarker.create_from_options(options) as landmarker:
+    cap = cv2.VideoCapture(0)
+    start_time = time.time()
+
+    while True:
+        ret, frame = cap.read()
+        if not ret: break
+        
+        frame = cv2.flip(frame, 1)
+        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_frame)
+        
+        timestamp = int((time.time() - start_time) * 1000)
+        detection_result = landmarker.detect_for_video(mp_image, timestamp)
+        
+        # Output info
+        display_text = "Waiting..."
+        color = (200, 200, 200)
+
+        if detection_result.hand_landmarks:
+            hand_landmarks = detection_result.hand_landmarks[0]
+            
+            # --- Draw ---
+            draw_landmarks(frame, hand_landmarks)
+
+            # --- Preprocess (MUST MATCH COLLECTION LOGIC) ---
+            wrist = hand_landmarks[0]
+            middle_mcp = hand_landmarks[9]
+
+            # Calculate scale
+            scale = np.sqrt(
+                (middle_mcp.x - wrist.x)**2 + 
+                (middle_mcp.y - wrist.y)**2 + 
+                (middle_mcp.z - wrist.z)**2
+            )
+            if scale == 0: scale = 1.0
+
+            # Normalization*
+            coords = []
+            for lm in hand_landmarks:
+                rel_x = (lm.x - wrist.x) / scale
+                rel_y = (lm.y - wrist.y) / scale
+                rel_z = (lm.z - wrist.z) / scale
+                coords.extend([rel_x, rel_y, rel_z])
+
+            # --- Predict ---
+            input_data = np.array([coords])
+            prediction = model.predict(input_data, verbose=0)
+            class_id = np.argmax(prediction)
+            confidence = np.max(prediction)
+
+            predicted_char = classes[class_id]
+
+            # --- Display logic ---
+            if confidence > THRESHOLD:
+                display_text = f"Sign: {predicted_char}"
+                color = (0, 255, 0)
+                
+                # Dynamic visual bar
+                bar_width = int(confidence * 200)
+                cv2.rectangle(frame, (50, 90), (50 + bar_width, 110), color, -1)
+                cv2.rectangle(frame, (50, 90), (250, 110), (255, 255, 255), 2)
+            else:
+                display_text = f"Unsure ({predicted_char}?)"
+                color = (0, 165, 255) # orange
+
+            cv2.putText(frame, f"{confidence:.2f}", (260, 108), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1)
+
+        # on screen result
+        cv2.rectangle(frame, (0, 0), (640, 60), (0, 0, 0), -1)
+        cv2.putText(frame, display_text, (20, 45), cv2.FONT_HERSHEY_SIMPLEX, 1.5, color, 3)
+
+        cv2.imshow('NSL Live Test', frame)
+        if cv2.waitKey(1) & 0xFF == 27:
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()