aalexchengg/variance_subset · Datasets at Hugging Face

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repo string	path string	func_name string	original_string string	language string	code string	code_tokens sequence	docstring string	docstring_tokens sequence	sha string	url string	partition string	summary string	input_ids sequence	token_type_ids sequence	attention_mask sequence	labels sequence
ageitgey/face_recognition	examples/face_recognition_knn.py	train	def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False): """ Trains a k-nearest neighbors classifier for face recognition. :param train_dir: directory that contains a sub-directory for each known person, with its name. (View in source code to see train_dir example tree structure) Structure: <train_dir>/ ├── <person1>/ │ ├── <somename1>.jpeg │ ├── <somename2>.jpeg │ ├── ... ├── <person2>/ │ ├── <somename1>.jpeg │ └── <somename2>.jpeg └── ... :param model_save_path: (optional) path to save model on disk :param n_neighbors: (optional) number of neighbors to weigh in classification. Chosen automatically if not specified :param knn_algo: (optional) underlying data structure to support knn.default is ball_tree :param verbose: verbosity of training :return: returns knn classifier that was trained on the given data. """ X = [] y = [] # Loop through each person in the training set for class_dir in os.listdir(train_dir): if not os.path.isdir(os.path.join(train_dir, class_dir)): continue # Loop through each training image for the current person for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)): image = face_recognition.load_image_file(img_path) face_bounding_boxes = face_recognition.face_locations(image) if len(face_bounding_boxes) != 1: # If there are no people (or too many people) in a training image, skip the image. if verbose: print("Image {} not suitable for training: {}".format(img_path, "Didn't find a face" if len(face_bounding_boxes) < 1 else "Found more than one face")) else: # Add face encoding for current image to the training set X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0]) y.append(class_dir) # Determine how many neighbors to use for weighting in the KNN classifier if n_neighbors is None: n_neighbors = int(round(math.sqrt(len(X)))) if verbose: print("Chose n_neighbors automatically:", n_neighbors) # Create and train the KNN classifier knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance') knn_clf.fit(X, y) # Save the trained KNN classifier if model_save_path is not None: with open(model_save_path, 'wb') as f: pickle.dump(knn_clf, f) return knn_clf	python	def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False): """ Trains a k-nearest neighbors classifier for face recognition. :param train_dir: directory that contains a sub-directory for each known person, with its name. (View in source code to see train_dir example tree structure) Structure: <train_dir>/ ├── <person1>/ │ ├── <somename1>.jpeg │ ├── <somename2>.jpeg │ ├── ... ├── <person2>/ │ ├── <somename1>.jpeg │ └── <somename2>.jpeg └── ... :param model_save_path: (optional) path to save model on disk :param n_neighbors: (optional) number of neighbors to weigh in classification. Chosen automatically if not specified :param knn_algo: (optional) underlying data structure to support knn.default is ball_tree :param verbose: verbosity of training :return: returns knn classifier that was trained on the given data. """ X = [] y = [] # Loop through each person in the training set for class_dir in os.listdir(train_dir): if not os.path.isdir(os.path.join(train_dir, class_dir)): continue # Loop through each training image for the current person for img_path in image_files_in_folder(os.path.join(train_dir, class_dir)): image = face_recognition.load_image_file(img_path) face_bounding_boxes = face_recognition.face_locations(image) if len(face_bounding_boxes) != 1: # If there are no people (or too many people) in a training image, skip the image. if verbose: print("Image {} not suitable for training: {}".format(img_path, "Didn't find a face" if len(face_bounding_boxes) < 1 else "Found more than one face")) else: # Add face encoding for current image to the training set X.append(face_recognition.face_encodings(image, known_face_locations=face_bounding_boxes)[0]) y.append(class_dir) # Determine how many neighbors to use for weighting in the KNN classifier if n_neighbors is None: n_neighbors = int(round(math.sqrt(len(X)))) if verbose: print("Chose n_neighbors automatically:", n_neighbors) # Create and train the KNN classifier knn_clf = neighbors.KNeighborsClassifier(n_neighbors=n_neighbors, algorithm=knn_algo, weights='distance') knn_clf.fit(X, y) # Save the trained KNN classifier if model_save_path is not None: with open(model_save_path, 'wb') as f: pickle.dump(knn_clf, f) return knn_clf	[ "def", "train", "(", "train_dir", ",", "model_save_path", "=", "None", ",", "n_neighbors", "=", "None", ",", "knn_algo", "=", "'ball_tree'", ",", "verbose", "=", "False", ")", ":", "X", "=", "[", "]", "y", "=", "[", "]", "# Loop through each person in the training set", "for", "class_dir", "in", "os", ".", "listdir", "(", "train_dir", ")", ":", "if", "not", "os", ".", "path", ".", "isdir", "(", "os", ".", "path", ".", "join", "(", "train_dir", ",", "class_dir", ")", ")", ":", "continue", "# Loop through each training image for the current person", "for", "img_path", "in", "image_files_in_folder", "(", "os", ".", "path", ".", "join", "(", "train_dir", ",", "class_dir", ")", ")", ":", "image", "=", "face_recognition", ".", "load_image_file", "(", "img_path", ")", "face_bounding_boxes", "=", "face_recognition", ".", "face_locations", "(", "image", ")", "if", "len", "(", "face_bounding_boxes", ")", "!=", "1", ":", "# If there are no people (or too many people) in a training image, skip the image.", "if", "verbose", ":", "print", "(", "\"Image {} not suitable for training: {}\"", ".", "format", "(", "img_path", ",", "\"Didn't find a face\"", "if", "len", "(", "face_bounding_boxes", ")", "<", "1", "else", "\"Found more than one face\"", ")", ")", "else", ":", "# Add face encoding for current image to the training set", "X", ".", "append", "(", "face_recognition", ".", "face_encodings", "(", "image", ",", "known_face_locations", "=", "face_bounding_boxes", ")", "[", "0", "]", ")", "y", ".", "append", "(", "class_dir", ")", "# Determine how many neighbors to use for weighting in the KNN classifier", "if", "n_neighbors", "is", "None", ":", "n_neighbors", "=", "int", "(", "round", "(", "math", ".", "sqrt", "(", "len", "(", "X", ")", ")", ")", ")", "if", "verbose", ":", "print", "(", "\"Chose n_neighbors automatically:\"", ",", "n_neighbors", ")", "# Create and train the KNN classifier", "knn_clf", "=", "neighbors", ".", "KNeighborsClassifier", "(", "n_neighbors", "=", "n_neighbors", ",", "algorithm", "=", "knn_algo", ",", "weights", "=", "'distance'", ")", "knn_clf", ".", "fit", "(", "X", ",", "y", ")", "# Save the trained KNN classifier", "if", "model_save_path", "is", "not", "None", ":", "with", "open", "(", "model_save_path", ",", "'wb'", ")", "as", "f", ":", "pickle", ".", "dump", "(", "knn_clf", ",", "f", ")", "return", "knn_clf" ]	Trains a k-nearest neighbors classifier for face recognition. :param train_dir: directory that contains a sub-directory for each known person, with its name. (View in source code to see train_dir example tree structure) Structure: <train_dir>/ ├── <person1>/ │ ├── <somename1>.jpeg │ ├── <somename2>.jpeg │ ├── ... ├── <person2>/ │ ├── <somename1>.jpeg │ └── <somename2>.jpeg └── ... :param model_save_path: (optional) path to save model on disk :param n_neighbors: (optional) number of neighbors to weigh in classification. Chosen automatically if not specified :param knn_algo: (optional) underlying data structure to support knn.default is ball_tree :param verbose: verbosity of training :return: returns knn classifier that was trained on the given data.	[ "Trains", "a", "k", "-", "nearest", "neighbors", "classifier", "for", "face", "recognition", "." ]	c96b010c02f15e8eeb0f71308c641179ac1f19bb	https://github.com/ageitgey/face_recognition/blob/c96b010c02f15e8eeb0f71308c641179ac1f19bb/examples/face_recognition_knn.py#L46-L108	train	Train a k - nearest neighbors classifier for face recognition.	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