AttributeError: module 'mediapipe' has no attribute 'solutions'

[TadeasDitte]

Hello, i get the error in title when using this code snippet from examples:

import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands

# For static images:
IMAGE_FILES = []
with mp_hands.Hands(
    static_image_mode=True,
    max_num_hands=2,
    min_detection_confidence=0.5) as hands:
  for idx, file in enumerate(IMAGE_FILES):
    # Read an image, flip it around y-axis for correct handedness output (see
    # above).
    image = cv2.flip(cv2.imread(file), 1)
    # Convert the BGR image to RGB before processing.
    results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

    # Print handedness and draw hand landmarks on the image.
    print('Handedness:', results.multi_handedness)
    if not results.multi_hand_landmarks:
      continue
    image_height, image_width, _ = image.shape
    annotated_image = image.copy()
    for hand_landmarks in results.multi_hand_landmarks:
      print('hand_landmarks:', hand_landmarks)
      print(
          f'Index finger tip coordinates: (',
          f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width}, '
          f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height})'
      )
      mp_drawing.draw_landmarks(
          annotated_image,
          hand_landmarks,
          mp_hands.HAND_CONNECTIONS,
          mp_drawing_styles.get_default_hand_landmarks_style(),
          mp_drawing_styles.get_default_hand_connections_style())
    cv2.imwrite(
        '/tmp/annotated_image' + str(idx) + '.png', cv2.flip(annotated_image, 1))
    # Draw hand world landmarks.
    if not results.multi_hand_world_landmarks:
      continue
    for hand_world_landmarks in results.multi_hand_world_landmarks:
      mp_drawing.plot_landmarks(
        hand_world_landmarks, mp_hands.HAND_CONNECTIONS, azimuth=5)

# For webcam input:
cap = cv2.VideoCapture(0)
with mp_hands.Hands(
    model_complexity=0,
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5) as hands:
  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 = hands.process(image)

    # Draw the hand annotations on the image.
    image.flags.writeable = True
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
    if results.multi_hand_landmarks:
      for hand_landmarks in results.multi_hand_landmarks:
        mp_drawing.draw_landmarks(
            image,
            hand_landmarks,
            mp_hands.HAND_CONNECTIONS,
            mp_drawing_styles.get_default_hand_landmarks_style(),
            mp_drawing_styles.get_default_hand_connections_style())
    # Flip the image horizontally for a selfie-view display.
    cv2.imshow('MediaPipe Hands', cv2.flip(image, 1))
    if cv2.waitKey(5) & 0xFF == 27:
      break
cap.release()

python: 3.13.11
mediapipe from pip: 0.10.31
filename: main.py
I would be extremely thankful for any help with this, the only issue i saw with this error was the filename being mediapipe.py but that is not my case...

[akashvverma1995]

Hi @TadeasDitte

With the release of MediaPipe 0.10.31, the Solutions API has been removed. Please migrate to MediaPipe Tasks and refer to the official documentation for guidance.

[Diode-exe]

@akashvverma1995 How long ago was it removed? Even versions less than 0.11 have this error.

[akshitaaa03]

@akashvverma1995 How long ago was it removed? Even versions less than 0.11 have this error.

true, I tried this code on an older version and still encountering an attribute error

[kaganakcelik]

@akshitaaa03 were you able to figure it out?

[akshitaaa03]

@akshitaaa03 were you able to figure it out?

yes, since solutions method has been removed, now we have to use tasks instead. you can refer to the official documentation for more information

[HarishPermal]

Hi @akshitaaa03, @Diode-exe, @kaganakcelik and @TadeasDitte

Below i've attached a simple handlandmarker example using mediapipe tasks, and the drawing utils is temporarily not available so i've hardcoded the landmarks.

i've tested it on colab

pip install mediapipe
wget -q https://storage.googleapis.com/mediapipe-models/hand_landmarker/hand_landmarker/float16/1/hand_landmarker.task

pip list | grep mediapipe
mediapipe                                0.10.31

import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision
from google.colab.patches import cv2_imshow
import cv2
import numpy as np

model_path = "hand_landmarker.task"
HAND_CONNECTIONS = [
    (0, 1), (1, 2), (2, 3), (3, 4),        # Thumb
    (0, 5), (5, 6), (6, 7), (7, 8),        # Index
    (0, 9), (9, 10), (10, 11), (11, 12),   # Middle
    (0, 13), (13, 14), (14, 15), (15, 16), # Ring
    (0, 17), (17, 18), (18, 19), (19, 20), # Pinky
    (5, 9), (9, 13), (13, 17)              # Palm connections
]

def to_pixel(x_norm: float, y_norm: float, w: int, h: int) -> tuple[int, int]:
    # keep to [0,1] to avoid occasional out-of-range artifacts
    x = min(max(x_norm, 0.0), 1.0)
    y = min(max(y_norm, 0.0), 1.0)
    return int(x * w), int(y * h)

def draw_hand_landmarks_tasks_only(
    image_bgr: np.ndarray,
    hand_landmarks_list,
    connections=HAND_CONNECTIONS,
    draw_points=True,
    draw_connections=True,
    point_radius=3,
    point_thickness=-1,
    line_thickness=2,
):
    annotated = image_bgr.copy()
    h, w = annotated.shape[:2]

    for hand_landmarks in hand_landmarks_list:
        # Convert normalized landmarks to pixel coords
        pts = [to_pixel(lm.x, lm.y, w, h) for lm in hand_landmarks]

        if draw_connections:
            for a, b in connections:
                cv2.line(annotated, pts[a], pts[b], (0, 255, 0), line_thickness)

        if draw_points:
            for (x, y) in pts:
                cv2.circle(annotated, (x, y), point_radius, (0, 0, 255), point_thickness)

    return annotated

def run_hand_landmarker_on_image_tasks_only(image_path: str):
    base_options = python.BaseOptions(model_asset_path=model_path)
    options = vision.HandLandmarkerOptions(
        base_options=base_options,
        num_hands=2,
        min_hand_detection_confidence=0.5,
        running_mode=vision.RunningMode.IMAGE
    )

    with vision.HandLandmarker.create_from_options(options) as landmarker:
        cv_image = cv2.imread(image_path)
        if cv_image is None:
            print("Image not found:", image_path)
            return

        rgb_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
        mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_image)

        result = landmarker.detect(mp_image)

    if result.hand_landmarks:
        annotated = draw_hand_landmarks_tasks_only(cv_image, result.hand_landmarks)
        cv2_imshow(annotated)
    else:
        print("No hands detected.")

sample run

run_hand_landmarker_on_image_tasks_only("hi.jpg")

[Diode-exe]

Thank you very much