Проблемы с медиапайпом ⇐ Python

[Anonymous]

Студент-новичок-программист экспериментирует с руками медиапайпа для школьного проекта. Я пытался следовать их демонстрационному коду, но столкнулся с некоторыми проблемами, которые мне не удалось исправить. Это предупреждающее сообщение, которое я получаю.

Код: Выделить всё

INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
W0000 00:00:1716651303.003734   18808 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.
W0000 00:00:1716651303.021867   18416 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.
C:\Users\Dylan\AppData\Local\Programs\Python\Python310\lib\site-packages\google\protobuf\symbol_database.py:55: UserWarning: SymbolDatabase.GetPrototype() is deprecated. Please use message_factory.GetMessageClass() instead. SymbolDatabase.GetPrototype() will be removed soon.
warnings.warn('SymbolDatabase.GetPrototype() is deprecated. Please '

Вот код, который я запускаю. Единственное отличие, на мой взгляд, от оригинальной демо-версии заключается в том, что я использую собственное изображение, сохраненное в том же каталоге.

Код: Выделить всё

# STEP 1: Import the necessary modules.
import mediapipe as mp
import cv2
from mediapipe.tasks import python
from mediapipe.tasks.python import vision

from mediapipe import solutions
from mediapipe.framework.formats import landmark_pb2
import numpy as np

MARGIN = 10  # pixels
FONT_SIZE = 1
FONT_THICKNESS = 1
HANDEDNESS_TEXT_COLOR = (88, 205, 54) # vibrant green

def draw_landmarks_on_image(rgb_image, detection_result):
hand_landmarks_list = detection_result.hand_landmarks
handedness_list = detection_result.handedness
annotated_image = np.copy(rgb_image)

# Loop through the detected hands to visualize.
for idx in range(len(hand_landmarks_list)):
hand_landmarks = hand_landmarks_list[idx]
handedness = handedness_list[idx]

# Draw the hand landmarks.
hand_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
hand_landmarks_proto.landmark.extend([
landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y, z=landmark.z) for landmark in hand_landmarks
])
solutions.drawing_utils.draw_landmarks(
annotated_image,
hand_landmarks_proto,
solutions.hands.HAND_CONNECTIONS,
solutions.drawing_styles.get_default_hand_landmarks_style(),
solutions.drawing_styles.get_default_hand_connections_style())

# Get the top left corner of the detected hand's bounding box.
height, width, _ = annotated_image.shape
x_coordinates = [landmark.x for landmark in hand_landmarks]
y_coordinates = [landmark.y for landmark in hand_landmarks]
text_x = int(min(x_coordinates) * width)
text_y = int(min(y_coordinates) * height) - MARGIN

# Draw handedness (left or right hand) on the image.
cv2.putText(annotated_image, f"{handedness[0].category_name}",
(text_x, text_y), cv2.FONT_HERSHEY_DUPLEX,
FONT_SIZE, HANDEDNESS_TEXT_COLOR, FONT_THICKNESS, cv2.LINE_AA)

return annotated_image

# STEP 2: Create an HandLandmarker object.
base_options = python.BaseOptions(model_asset_path='./hand_landmarker.task')
options = vision.HandLandmarkerOptions(base_options=base_options,
num_hands=2)
detector = vision.HandLandmarker.create_from_options(options)

# STEP 3: Load the input image.
image = mp.Image.create_from_file("test.jpg")

# STEP 4: Detect hand landmarks from the input image.
detection_result = detector.detect(image)

# STEP 5: Process the classification result. In this case, visualize it.
annotated_image = draw_landmarks_on_image(image.numpy_view(), detection_result)

Буду очень признателен за любую помощь! ваш текст

Подробнее здесь: https://stackoverflow.com/questions/785 ... -mediapipe