Facializando com Machine Learning

Este exemplo mostra o uso de vários algoritmos de aprendizado de máquina para concluir imagens. O objetivo é prever a metade inferior da face, dada a metade superior.

A primeira coluna de imagens mostra faces reais. As colunas a seguir ilustram como as árvores (árvores extremamente aleatórias), o método k vizinhos mais próximos / K-nn, a regressão linear e o RidgeCV (regressão da crista concluída) completam a metade inferior dessas faces.

#   import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import fetch_olivetti_faces from sklearn.utils.validation import check_random_state from sklearn.ensemble import ExtraTreesRegressor from sklearn.neighbors import KNeighborsRegressor from sklearn.linear_model import LinearRegression from sklearn.linear_model import RidgeCV #    data = fetch_olivetti_faces() targets = data.target data = data.images.reshape((len(data.images), -1)) train = data[targets < 30] test = data[targets >= 30] # Test on independent people #     n_faces = 5 rng = check_random_state(4) face_ids = rng.randint(test.shape[0], size=(n_faces, )) test = test[face_ids, :] n_pixels = data.shape[1] #    X_train = train[:, :(n_pixels + 1) // 2] #    y_train = train[:, n_pixels // 2:] X_test = test[:, :(n_pixels + 1) // 2] y_test = test[:, n_pixels // 2:] #   ESTIMATORS = { "Extra trees": ExtraTreesRegressor(n_estimators=10, max_features=32, random_state=0), "K-nn": KNeighborsRegressor(), "Linear regression": LinearRegression(), "Ridge": RidgeCV(), } y_test_predict = dict() for name, estimator in ESTIMATORS.items(): estimator.fit(X_train, y_train) y_test_predict[name] = estimator.predict(X_test) #  image_shape = (64, 64) n_cols = 1 + len(ESTIMATORS) plt.figure(figsize=(2. * n_cols, 2.26 * n_faces)) plt.suptitle("       ", size=16) for i in range(n_faces): true_face = np.hstack((X_test[i], y_test[i])) if i: sub = plt.subplot(n_faces, n_cols, i * n_cols + 1) else: sub = plt.subplot(n_faces, n_cols, i * n_cols + 1, title="true faces") sub.axis("off") sub.imshow(true_face.reshape(image_shape), cmap=plt.cm.gray, interpolation="nearest") for j, est in enumerate(sorted(ESTIMATORS)): completed_face = np.hstack((X_test[i], y_test_predict[est][i])) if i: sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j) else: sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j, title=est) sub.axis("off") sub.imshow(completed_face.reshape(image_shape), cmap=plt.cm.gray, interpolation="nearest") plt.show()