def show_images(images, holdon=False, title=None, nmax=49): # use matplotlib to display profile images n = len(images) if n > nmax: n = nmax n_col = 7 else: n_col = 3 if n % n_col == 0: n_row = n // n_col else: n_row = n // 3 + 1 if title is None: plt.figure() else: plt.figure(title) plt.tight_layout() for j, i in enumerate(images): if j == nmax: print('\n\nToo many images to show... \n\n') break temp_image = imageio.imread(i) if len(temp_image.shape) < 3: # needs to be converted to rgb temp_image = to_rgb(temp_image) plt.subplot(n_row, n_col, j+1) plt.imshow(temp_image) plt.axis('off') plt.subplots_adjust(wspace=0, hspace=0) if holdon is False: plt.show(block=False) plt.pause(0.1)
def show_images(images): # use matplotlib to display profile images n = len(images) n_col = 3 if n % n_col == 0: n_row = n // n_col else: n_row = n // 3 + 1 plt.figure() plt.tight_layout() for j, i in enumerate(images): temp_image = imageio.imread(i) if len(temp_image.shape) < 3: # needs to be converted to rgb temp_image = to_rgb(temp_image) plt.subplot(n_row, n_col, j + 1) plt.imshow(temp_image) plt.axis('off') plt.subplots_adjust(wspace=0, hspace=0) plt.show(block=False) plt.pause(0.1)
def main(input_dir='database', output_dir='database_aligned', image_size=182, margin=44, gpu_memory_fraction=1, random_order=False): sleep(random.random()) output_dir = os.path.expanduser(output_dir) if not os.path.exists(output_dir): os.makedirs(output_dir) # Store some git revision info in a text file in the log directory src_path, _ = os.path.split(os.path.realpath(__file__)) cwd = os.getcwd() facenet.store_revision_info(cwd, output_dir, ' '.join(sys.argv)) dataset = facenet.get_dataset(input_dir) print('Creating networks and loading parameters') with tf.Graph().as_default(): gpu_options = tf.GPUOptions( per_process_gpu_memory_fraction=gpu_memory_fraction) sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False)) with sess.as_default(): pnet, rnet, onet = detect_face.create_mtcnn(sess, None) minsize = 20 # minimum size of face threshold = [0.6, 0.7, 0.7] # three steps's threshold factor = 0.709 # scale factor # Add a random key to the filename to allow alignment using multiple processes random_key = np.random.randint(0, high=99999) bounding_boxes_filename = os.path.join( output_dir, 'bounding_boxes_%05d.txt' % random_key) with open(bounding_boxes_filename, "w") as text_file: nrof_images_total = 0 nrof_successfully_aligned = 0 if random_order: random.shuffle(dataset) for cls in dataset: output_class_dir = os.path.join(output_dir, cls.name) if not os.path.exists(output_class_dir): os.makedirs(output_class_dir) if random_order: random.shuffle(cls.image_paths) for image_path in cls.image_paths: nrof_images_total += 1 filename = os.path.splitext(os.path.split(image_path)[1])[0] output_filename = os.path.join(output_class_dir, filename + '.png') print(image_path) if not os.path.exists(output_filename): try: img = imageio.imread(image_path) except (IOError, ValueError, IndexError) as e: errorMessage = '{}: {}'.format(image_path, e) print(errorMessage) else: if img.ndim < 2: print('Unable to align "%s"' % image_path) text_file.write('%s\n' % (output_filename)) continue if img.ndim == 2: img = facenet.to_rgb(img) img = img[:, :, 0:3] bounding_boxes, _ = detect_face.detect_face( img, minsize, pnet, rnet, onet, threshold, factor) nrof_faces = bounding_boxes.shape[0] if nrof_faces > 0: det = bounding_boxes[:, 0:4] img_size = np.asarray(img.shape)[0:2] if nrof_faces > 1: bounding_box_size = (det[:, 2] - det[:, 0]) * ( det[:, 3] - det[:, 1]) img_center = img_size / 2 offsets = np.vstack([ (det[:, 0] + det[:, 2]) / 2 - img_center[1], (det[:, 1] + det[:, 3]) / 2 - img_center[0] ]) offset_dist_squared = np.sum( np.power(offsets, 2.0), 0) index = np.argmax( bounding_box_size - offset_dist_squared * 2.0) # some extra weight on the centering det = det[index, :] det = np.squeeze(det) bb = np.zeros(4, dtype=np.int32) bb[0] = np.maximum(det[0] - margin / 2, 0) bb[1] = np.maximum(det[1] - margin / 2, 0) bb[2] = np.minimum(det[2] + margin / 2, img_size[1]) bb[3] = np.minimum(det[3] + margin / 2, img_size[0]) cropped = img[bb[1]:bb[3], bb[0]:bb[2], :] scaled = resize(cropped, (image_size, image_size), mode='constant') nrof_successfully_aligned += 1 imageio.imwrite(output_filename, scaled) text_file.write( '%s %d %d %d %d\n' % (output_filename, bb[0], bb[1], bb[2], bb[3])) else: print('Unable to align "%s"' % image_path) text_file.write('%s\n' % (output_filename)) print('Total number of images: %d' % nrof_images_total) print('Number of successfully aligned images: %d' % nrof_successfully_aligned)