AttributeError：模块“dlib”没有属性“shape_predictor”

Question

如何纠正这个问题？我在以下版本 python 3.6、3.8 和 3.9 中尝试过同样的方法

Answer 1

• 在终端中，尝试：

  python3 -c "import dlib; print(dir(dlib))"
  

  如果输出如下所示：

  ['__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__']
  

  这意味着，dlib 并没有真正安装。你的 dlib 模块是空的。

• 正确安装 dlib 后，输出应如下所示（其中 shape_predictor 存在）：

   ['DLIB_USE_BLAS', 'DLIB_USE_CUDA', 'DLIB_USE_LAPACK', 'KBD_MOD_ALT', 'KBD_MOD_CAPS_LOCK', 'KBD_MOD_CONTROL',
  'KBD_MOD_META', 'KBD_MOD_NONE', 'KBD_MOD_NUM_LOCK',
  'KBD_MOD_SCROLL_LOCK', 'KBD_MOD_SHIFT', 'KEY_ALT', 'KEY_BACKSPACE',
  'KEY_CAPS_LOCK', 'KEY_CTRL', 'KEY_DELETE', 'KEY_DOWN', 'KEY_END',
  'KEY_ESC', 'KEY_F1', 'KEY_F10', 'KEY_F11', 'KEY_F12', 'KEY_F2',
  'KEY_F3', 'KEY_F4', 'KEY_F5', 'KEY_F6', 'KEY_F7', 'KEY_F8', 'KEY_F9',
  'KEY_HOME', 'KEY_INSERT', 'KEY_LEFT', 'KEY_PAGE_DOWN', 'KEY_PAGE_UP',
  'KEY_PAUSE', 'KEY_RIGHT', 'KEY_SCROLL_LOCK', 'KEY_SHIFT', 'KEY_UP',
  'USE_AVX_INSTRUCTIONS', 'USE_NEON_INSTRUCTIONS', '__builtins__',
  '__cached__', '__doc__', '__file__', '__loader__', '__name__',
  '__package__', '__path__', '__spec__', '__time_compiled__',
  '__version__', 'add_lib_to_dll_path', 'angle_between_lines',
  'apply_cca_transform', 'array', 'as_grayscale', 'assignment_cost',
  'auto_train_rbf_classifier', 'cca', 'cca_outputs', 'center',
  'centered_rect', 'centered_rects', 'chinese_whispers',
  'chinese_whispers_clustering', 'chip_details', 'chip_dims',
  'cnn_face_detection_model_v1', 'convert_image',
  'convert_image_scaled', 'correlation_tracker',
  'count_points_between_lines', 'count_points_on_side_of_line',
  'count_steps_without_decrease',
  'count_steps_without_decrease_robust',
  'cross_validate_ranking_trainer',
  'cross_validate_sequence_segmenter', 'cross_validate_trainer',
  'cross_validate_trainer_threaded', 'cuda', 'distance_to_line', 'dot',
  'dpoint', 'dpoints', 'drectangle', 'equalize_histogram',
  'extract_image_4points', 'extract_image_chip', 'extract_image_chips',
  'face_recognition_model_v1', 'fhog_object_detector',
  'find_bright_keypoints', 'find_bright_lines',
  'find_candidate_object_locations', 'find_dark_keypoints',
  'find_dark_lines', 'find_line_endpoints', 'find_max_global',
  'find_min_global', 'find_optimal_rect_filter', 'find_peaks',
  'find_projective_transform', 'full_object_detection',
  'full_object_detections', 'function_evaluation',
  'function_evaluation_request', 'function_spec', 'gaussian_blur',
  'get_face_chip', 'get_face_chips', 'get_frontal_face_detector',
  'get_histogram', 'get_rect', 'global_function_search', 'grow_rect',
  'hit_enter_to_continue', 'hough_transform', 'hysteresis_threshold',
  'image_dataset_metadata', 'image_gradients', 'image_window',
  'intersect', 'inv', 'jet', 'jitter_image', 'keyboard_mod_keys',
  'label_connected_blobs', 'label_connected_blobs_watershed', 'length',
  'line', 'load_grayscale_image', 'load_libsvm_formatted_data',
  'load_rgb_image', 'make_bounding_box_regression_training_data',
  'make_sparse_vector', 'matrix', 'max_cost_assignment',
  'max_index_plus_one', 'max_point', 'max_point_interpolated',
  'min_barrier_distance', 'mmod_rectangle', 'mmod_rectangles',
  'mmod_rectangless', 'no_convex_quadrilateral',
  'non_printable_keyboard_keys', 'normalize_image_gradients',
  'num_separable_filters', 'pair', 'partition_pixels', 'point',
  'point_transform_projective', 'points', 'polygon_area',
  'probability_that_sequence_is_increasing', 'pyramid_down',
  'randomly_color_image', 'range', 'ranges', 'rangess', 'ranking_pair',
  'ranking_pairs', 'rect_filter', 'rectangle', 'rectangles',
  'rectangless', 'reduce', 'remove_incoherent_edge_pixels',
  'resize_image', 'reverse', 'rgb_pixel',
  'rvm_trainer_histogram_intersection', 'rvm_trainer_linear',
  'rvm_trainer_radial_basis',
  'rvm_trainer_sparse_histogram_intersection',
  'rvm_trainer_sparse_linear', 'rvm_trainer_sparse_radial_basis',
  'save_face_chip', 'save_face_chips', 'save_image',
  'save_libsvm_formatted_data', 'scale_rect', 'segmenter_params',
  'segmenter_test', 'segmenter_type',
  'set_dnn_prefer_smallest_algorithms', 'shape_predictor',
  'shape_predictor_training_options', 'shrink_rect',
  'signed_distance_to_line', 'simple_object_detector',
  'simple_object_detector_training_options', 'simple_test_results',
  'skeleton', 'sobel_edge_detector', 'solve_structural_svm_problem',
  'sparse_ranking_pair', 'sparse_ranking_pairs', 'sparse_vector',
  'sparse_vectors', 'sparse_vectorss', 'spatially_filter_image',
  'spatially_filter_image_separable', 'sub_image',
  'suppress_non_maximum_edges', 'svm_c_trainer_histogram_intersection',
  'svm_c_trainer_linear', 'svm_c_trainer_radial_basis',
  'svm_c_trainer_sparse_histogram_intersection',
  'svm_c_trainer_sparse_linear', 'svm_c_trainer_sparse_radial_basis',
  'svm_rank_trainer', 'svm_rank_trainer_sparse',
  'test_binary_decision_function', 'test_ranking_function',
  'test_regression_function', 'test_sequence_segmenter',
  'test_shape_predictor', 'test_simple_object_detector',
  'threshold_filter_singular_values', 'threshold_image', 'tile_images',
  'train_sequence_segmenter', 'train_shape_predictor',
  'train_simple_object_detector', 'transform_image', 'translate_rect',
  'vector', 'vectors', 'vectorss', 'zero_border_pixels']
  

• 您使用什么操作系统？如果是 Ubuntu，则在您的系统上查找 libssl.so.1.0.0。