mediapipe: Unable to open file at \test\hand_landmarker.task

Have I written custom code (as opposed to using a stock example script provided in MediaPipe)

None

OS Platform and Distribution

Windows 10

Python Version

3.9

MediaPipe Model Maker version

最新

Task name (e.g. Image classification, Gesture recognition etc.)

手势识别

Describe the actual behavior

代码无法跑通

Describe the expected behaviour

代码应该可以跑通

Standalone code/steps you may have used to try to get what you need

import cv2
import mediapipe as mp
from mediapipe import solutions
from mediapipe.framework.formats import landmark_pb2
import numpy as np
from mediapipe.tasks import python
from mediapipe.tasks.python import vision


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

img = cv2.imread("photo.jpg")
cv2.imshow('photo', img)

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("photo.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)
cv2.imshow(cv2.cvtColor(annotated_image, cv2.COLOR_RGB2BGR))

Other info / Complete Logs

No response