working

.gitignore

@@ -4,5 +4,6 @@
 /.gpu-3d/
 /.venv/
 /venv/
+*.mp4
 
 yolo11*

.vscode/settings.json

@@ -0,0 +1,3 @@
+{
+    "liveServer.settings.port": 5501
+}

main.py

@@ -11,7 +11,7 @@ from draw import draw_new
 from utils import find_closest
 from video_methods import initialize_method
 
-model = YOLO("yolo11x-pose.pt")
+model = YOLO("yolo11s-pose.pt")
 
 if len(sys.argv) == 2:
     method_type = sys.argv[1]

moves_3d.py

@@ -0,0 +1,43 @@
+import cv2
+import mediapipe as mp
+import cv2
+import mediapipe as mp
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+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.")
+      # If loading a video, use 'break' instead of 'continue'.
+      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)
+
+    print(f"\r{results.pose_world_landmarks[0]}", end="")
+
+    # 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.
+
+    landmarks = results.pose_world_landmarks.landmark
+
+    print(landmark)
+cap.release()

moves_3d_mp4.py

@@ -0,0 +1,92 @@
+import cv2
+import mediapipe as mp
+import matplotlib
+matplotlib.use("Agg")  # <-- ważne: wyłącza GUI
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+import numpy as np
+
+# ---------------------
+# Wideo wejściowe
+# ---------------------
+cap = cv2.VideoCapture("input.mp4")
+fps = cap.get(cv2.CAP_PROP_FPS)
+width = 640
+height = 640
+
+# ---------------------
+# Wideo wyjściowe
+# ---------------------
+fourcc = cv2.VideoWriter_fourcc(*"MJPG")
+
+
+out = cv2.VideoWriter("output.mp4", fourcc, fps, (width, height))
+
+# ---------------------
+# MediaPipe Pose
+# ---------------------
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose(static_image_mode=False, model_complexity=1)
+
+frame_id = 0
+
+while True:
+    ok, frame = cap.read()
+    if not ok:
+        break
+
+    rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    results = pose.process(rgb)
+
+    # -----------------------------------------
+    # 3D landmarki: pose_world_landmarks
+    # -----------------------------------------
+    if results.pose_world_landmarks:
+        lm = results.pose_world_landmarks.landmark
+
+        xs = np.array([p.x for p in lm])
+        ys = np.array([p.y for p in lm])
+        zs = np.array([p.z for p in lm])
+
+        # -----------------------------
+        # RYSOWANIE 3D w Matplotlib
+        # -----------------------------
+        fig = plt.figure(figsize=(6.4, 6.4), dpi=100)
+        ax = fig.add_subplot(111, projection="3d")
+
+        ax.scatter(xs, zs, ys, s=20)
+
+        ax.set_xlim([-1, 1])
+        ax.set_ylim([-1, 1])
+        ax.set_zlim([-1, 1])
+
+        ax.set_xlabel("X")
+        ax.set_ylabel("Y")
+        ax.set_zlabel("Z")
+        ax.invert_zaxis()
+
+        # -----------------------------------------
+        # Konwersja wykresu Matplotlib → klatka do MP4
+        # -----------------------------------------
+        fig.canvas.draw()
+        renderer = fig.canvas.get_renderer()
+
+        w, h = fig.canvas.get_width_height()
+
+        buf = renderer.buffer_rgba()
+        plot_img = np.frombuffer(buf, dtype=np.uint8).reshape((h, w, 4))[:, :, :3]
+
+
+        plt.close(fig)
+
+        # Dopasowanie rozmiaru do wideo
+        plot_img = cv2.resize(plot_img, (width, height))
+        plot_img = cv2.cvtColor(plot_img, cv2.COLOR_RGB2BGR)
+
+        out.write(plot_img)
+
+    frame_id += 1
+
+cap.release()
+out.release()
+print("Zapisano: output.mp4")