| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| from scipy import misc |
| import os |
| import tensorflow.compat.v1 as tf |
|
|
| import numpy as np |
| import facenet |
| import detect_face |
| import imageio |
| from PIL import Image |
|
|
| class preprocesses: |
| def __init__(self, input_datadir, output_datadir): |
| self.input_datadir = input_datadir |
| self.output_datadir = output_datadir |
|
|
| def collect_data(self): |
| output_dir = os.path.expanduser(self.output_datadir) |
| if not os.path.exists(output_dir): |
| os.makedirs(output_dir) |
|
|
| dataset = facenet.get_dataset(self.input_datadir) |
| with tf.Graph().as_default(): |
| gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5) |
| 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, './npy') |
|
|
| minsize = 20 |
| threshold = [0.5, 0.6, 0.6] |
| factor = 0.709 |
| margin = 44 |
| image_size = 182 |
|
|
| 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 |
| 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) |
| 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: %s" % 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) |
| print('to_rgb data dimension: ', img.ndim) |
| img = img[:, :, 0:3] |
|
|
| bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, |
| factor) |
| nrof_faces = bounding_boxes.shape[0] |
| print('No of Detected Face: %d' % nrof_faces) |
| 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) |
| det = det[index, :] |
| det = np.squeeze(det) |
| bb_temp = np.zeros(4, dtype=np.int32) |
|
|
| |
| bb_temp[0] = np.maximum(det[0], 0) |
| bb_temp[1] = np.maximum(det[1], 0) |
| bb_temp[2] = np.minimum(det[2], img_size[1]) |
| bb_temp[3] = np.minimum(det[3], img_size[0]) |
|
|
| cropped_temp = img[bb_temp[1]:bb_temp[3], bb_temp[0]:bb_temp[2], :] |
|
|
| |
| if cropped_temp.shape[0] > 0 and cropped_temp.shape[1] > 0: |
| scaled_temp = np.array(Image.fromarray(cropped_temp).resize((image_size, image_size))) |
| nrof_successfully_aligned += 1 |
| imageio.imwrite(output_filename, scaled_temp) |
| text_file.write('%s %d %d %d %d\n' % (output_filename, bb_temp[0], bb_temp[1], bb_temp[2], bb_temp[3])) |
| else: |
| print(f"Skipped resizing for image {image_path} due to invalid crop size") |
| text_file.write('%s\n' % (output_filename)) |
| else: |
| print('Unable to align "%s"' % image_path) |
| text_file.write('%s\n' % (output_filename)) |
|
|
| return (nrof_images_total, nrof_successfully_aligned) |
|
|
