working

mac.py

@@ -0,0 +1,36 @@
+import cv2
+import mediapipe as mp
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+mp_pose = mp.solutions.pose
+
+cap = cv2.VideoCapture(0)
+with mp_pose.Pose(
+    min_detection_confidence=0.5,
+    min_tracking_confidence=0.5) as pose:
+  while cap.isOpened():
+    success, image = cap.read()
+    if not success:
+      print("Ignoring empty camera frame.")
+      continue
+
+    # To improve performance, optionally mark the image as not writeable to
+    # pass by reference.
+    image.flags.writeable = False
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    results = pose.process(image)
+
+    # Draw the pose annotation on the image.
+    image.flags.writeable = True
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    mp_drawing.draw_landmarks(
+        image,
+        results.pose_landmarks,
+        mp_pose.POSE_CONNECTIONS,
+        landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
+    # Flip the image horizontally for a selfie-view display.
+    cv2.imshow('MediaPipe Pose', cv2.flip(image, 1))
+    if cv2.waitKey(5) & 0xFF == 27:
+      break
+
+cap.release()

main.py

@@ -69,7 +69,10 @@ def main():
         fps = 1 / delta if delta > 0 else float('inf')
         # print(f"\rDelta: {delta:.4f}s, FPS: {fps:.2f}", end="")
 
-        for result in results:
+        if len(results) == 0:
+            continue
+
+        result = results[0]
         kpts = result.keypoints.data[0] if len(result.keypoints.data) else None
 
         if kpts is None:

moves_dump.py

@@ -35,7 +35,7 @@ for i, move in enumerate(moves):
 
 
     # Do rysowania (np. przesunięcie na ekran)
-    draw = utils.normalize(move[2])
+    draw = utils.normalize(move[2]) * 200 + 250
 
     cv2.imshow('you', draw_new(draw))
     cv2.waitKey(1)

utils.py

@@ -15,21 +15,26 @@ def recvall(sock, n):
 def distance(p1, p2):
     return math.sqrt((p2[0] - p1[0])**2 + (p2[1] - p1[1])**2)
 
+import numpy as np
+
 def normalize(move):
     left_hip = move[11]   # Left Hip
     right_hip = move[12]  # Right Hip
+    nose = move[0]        # Nose (głowa)
 
+    # Środek bioder
     center = (left_hip + right_hip) / 2
 
+    # Przesunięcie względem środka
     normalized_keypoints = move - center
-    distances = np.linalg.norm(normalized_keypoints[:, :2], axis=1)
-    max_dist = np.max(distances)
 
-    if max_dist > 0:
-        normalized_keypoints[:, :2] /= max_dist
+    # Zamiast max_dist używamy stałej miary "rozmiaru ciała"
+    body_height = np.linalg.norm(nose[:2] - center[:2])  # np. odległość biodra-głowa
+
+    if body_height > 0:
+        normalized_keypoints[:, :2] /= body_height
 
     draw = normalized_keypoints[:, :2]
-
     return draw
 
 def find_closest(moves, target):