def test_guess_spatial_dimensions(): im1 = np.zeros((5, 5)) im2 = np.zeros((5, 5, 5)) im3 = np.zeros((5, 5, 3)) im4 = np.zeros((5, 5, 5, 3)) im5 = np.zeros((5,)) assert_equal(guess_spatial_dimensions(im1), 2) assert_equal(guess_spatial_dimensions(im2), 3) assert_equal(guess_spatial_dimensions(im3), None) assert_equal(guess_spatial_dimensions(im4), 3) assert_raises(ValueError, guess_spatial_dimensions, im5)
def test_guess_spatial_dimensions(): im1 = np.zeros((5, 5)) im2 = np.zeros((5, 5, 5)) im3 = np.zeros((5, 5, 3)) im4 = np.zeros((5, 5, 5, 3)) im5 = np.zeros((5, )) assert_equal(guess_spatial_dimensions(im1), 2) assert_equal(guess_spatial_dimensions(im2), 3) assert_equal(guess_spatial_dimensions(im3), None) assert_equal(guess_spatial_dimensions(im4), 3) assert_raises(ValueError, guess_spatial_dimensions, im5)
def read_test_data(IMG_WIDTH=256, IMG_HEIGHT=256, IMG_CHANNELS=3): X_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8) sizes_test = [] print('\nGetting and resizing test images ... ') sys.stdout.flush() if os.path.isfile("test_img.npy") and os.path.isfile("test_size.npy"): print("Test file loaded from memory") X_test = np.load("test_img.npy") sizes_test = np.load("test_size.npy") return X_test, sizes_test b = Progbar(len(test_ids)) for n, id_ in enumerate(test_ids): path = TEST_PATH + id_ # img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS] img = imread(path + '/images/' + id_ + '.png') if (guess_spatial_dimensions(img) == 2): img = gray2rgb(img) else: img = img[:, :, :IMG_CHANNELS] sizes_test.append([img.shape[0], img.shape[1]]) img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True) X_test[n] = img b.update(n) np.save("test_img", X_test) np.save("test_size", sizes_test) return X_test, sizes_test
def convert_rgb(img): nchannel = color.guess_spatial_dimensions(img) res = img if nchannel == 1 or nchannel == None: res = color.gray2rgb(img) elif nchannel == 4: res = img[:, :, 0:3] nchannel = res.shape[2] if nchannel == 4: res = img[:, :, 0:3] print res.shape return res
fig = plt.figure(figsize=(6, 6)) ani = animation.FuncAnimation(fig, animate, np.arange(0, 1, 0.05), init_func=init, interval=20) ani.save("ejercicio1.gif", fps=5) ######################################### # EJERCICIO 2 ######################################### img = io.imread('arbol.png') dimensions = color.guess_spatial_dimensions(img) print(dimensions) io.show() #io.imsave('arbol2.png',img) #https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html fig = plt.figure(figsize=(5, 5)) p = plt.contourf(img[:, :, 0], cmap=plt.cm.get_cmap('viridis'), levels=np.arange(0, 240, 2)) plt.axis('off') #fig.colorbar(p) xyz = img.shape x = np.arange(0, xyz[0], 1)
import os from skimage import io, data, color import skimage import os print(skimage.data_dir) filename = os.path.join(skimage.data_dir, "camera.png") camera = io.imread(filename) print(color.guess_spatial_dimensions(camera)) # io.imshow(camera) # io.show()