Idk

hand_detection.py

@@ -3,12 +3,18 @@ import cv2, numpy as np, time, fitz
 from helper import compute_distance
 from config import PINCH_THRESHOLD, RELEASE_THRESHOLD
 
+# hand_state.py
+from dataclasses import dataclass, field
+from typing import Tuple, Dict, Optional
+
+from helper import finger_straight
+
 # ----- Initialize Hand Detector -----
 mp_hands = mp.solutions.hands
 mp_draw  = mp.solutions.drawing_utils
 hands = mp_hands.Hands(
     static_image_mode=False,
-    max_num_hands=2,
+    max_num_hands=8,
     min_detection_confidence=0.7,
     min_tracking_confidence=0.5
 )
@@ -16,10 +22,106 @@ hands = mp_hands.Hands(
 start_pt = None               # measurement start point
 measuring = False             # measurement in progress
 
+@dataclass
+class HandState:
+    id: int                              # a persistent identifier for this hand
+    landmarks: Dict[str, Tuple[int,int]] = field(default_factory=dict)
+    finger_straightness: Dict[str, float] = field(default_factory=dict)
+    gesture: Optional[str] = None        # e.g. "pinch", "fist", "open"
+    gesture_persistence: int = 0         # how many frames the current gesture has held
+
+#handList = Dict[int, HandState] = {}
+next_id = 0 
+
+def update(mp_results, frame_vis):
+    h, w, _ = frame_vis.shape
+    detected_centroids = []
+    landmarks_list     = []
+
+    # 1) pull out centroids & raw landmarks
+    if mp_results.multi_hand_landmarks:
+        for hand in mp_results.multi_hand_landmarks:
+            pts = []
+            for lm in hand.landmark:
+                pts.append((int(lm.x*w), int(lm.y*h)))
+            centroid = np.mean(pts, axis=0)
+            detected_centroids.append(tuple(centroid.astype(int)))
+            landmarks_list.append((hand, pts))
+
+    # 2) match to existing by nearest centroid
+    new_hands = {}
+    used_ids  = set()
+    for (hand, pts), centroid in zip(landmarks_list, detected_centroids):
+        # find best existing hand
+        best_id, best_dist = None, 1e9
+        for hid, state in handList.items():
+            dx, dy = np.array(state.landmarks['centroid']) - centroid
+            d = np.hypot(dx, dy)
+            if d < best_dist and d < 100:  # 100px max match distance
+                best_dist, best_id = d, hid
+
+        if best_id is None:
+            hid = next_id
+            next_id += 1
+            state = HandState(id=hid)
+        else:
+            hid   = best_id
+            state = handList[hid]
+
+        used_ids.add(hid)
+
+        # 3) update state
+        state.persistence += 1
+        state.landmarks['centroid'] = centroid
+
+        # compute fingertip positions
+        idx_tip = pts[mp.solutions.hands.HandLandmark.INDEX_FINGER_TIP]
+        mid_tip = pts[mp.solutions.hands.HandLandmark.MIDDLE_FINGER_TIP]
+        state.landmarks['index_tip']  = idx_tip
+        state.landmarks['middle_tip'] = mid_tip
+
+        for name, tip_i, pip_i, mcp_i in [
+            ('index', mp.solutions.hands.HandLandmark.INDEX_FINGER_TIP,
+                        mp.solutions.hands.HandLandmark.INDEX_FINGER_PIP,
+                        mp.solutions.hands.HandLandmark.INDEX_FINGER_MCP),
+            ('middle', mp.solutions.hands.HandLandmark.MIDDLE_FINGER_TIP,
+                        mp.solutions.hands.HandLandmark.MIDDLE_FINGER_PIP,
+                        mp.solutions.hands.HandLandmark.MIDDLE_FINGER_MCP),
+            # add ring, pinky, thumb similarly...
+        ]:
+            p_tip = pts[tip_i]
+            p_pip = pts[pip_i]
+            p_mcp = pts[mcp_i]
+            state.finger_straightness[name] = finger_straight(p_tip, p_pip, p_mcp)
+
+        # 5) simple gesture detection
+        if state.finger_straightness['index'] < 0.2 and \
+            state.finger_straightness['middle'] < 0.2:
+            gesture = 'fist'
+        elif state.finger_straightness['index'] > 0.8 and \
+                state.finger_straightness['middle'] > 0.8:
+            gesture = 'open'
+        else:
+            gesture = None
+
+        if gesture == state.gesture:
+            state.gesture_persistence += 1
+        else:
+            state.gesture = gesture
+            state.gesture_persistence = 0
+
+        new_hands[hid] = state
+
+    # 6) drop hands not seen this frame
+    handList = new_hands
+    return list(hands.values())
+
+
+
 def detect_hands(frame):
     rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
     rgb.flags.writeable = False
-    results = hands.process(rgb)
+    results = hands.process(rgb)    
 
     return results
 
@@ -47,6 +149,8 @@ def check_hand(hand, frame_vis, object_calibrating):
 
     return fingertip_idx, measuring, start_pt
 
+
+
 def is_measuring(frame_vis, hand, object_calibrating):
     global measuring, start_pt
     h, w, _ = frame_vis.shape