Spaces:
Running
Running
| import os | |
| import cv2 | |
| import numpy as np | |
| from scipy.io import loadmat | |
| import tensorflow as tf | |
| from util.preprocess import align_for_lm | |
| from shutil import move | |
| mean_face = np.loadtxt('util/test_mean_face.txt') | |
| mean_face = mean_face.reshape([68, 2]) | |
| def save_label(labels, save_path): | |
| np.savetxt(save_path, labels) | |
| def draw_landmarks(img, landmark, save_name): | |
| landmark = landmark | |
| lm_img = np.zeros([img.shape[0], img.shape[1], 3]) | |
| lm_img[:] = img.astype(np.float32) | |
| landmark = np.round(landmark).astype(np.int32) | |
| for i in range(len(landmark)): | |
| for j in range(-1, 1): | |
| for k in range(-1, 1): | |
| if img.shape[0] - 1 - landmark[i, 1]+j > 0 and \ | |
| img.shape[0] - 1 - landmark[i, 1]+j < img.shape[0] and \ | |
| landmark[i, 0]+k > 0 and \ | |
| landmark[i, 0]+k < img.shape[1]: | |
| lm_img[img.shape[0] - 1 - landmark[i, 1]+j, landmark[i, 0]+k, | |
| :] = np.array([0, 0, 255]) | |
| lm_img = lm_img.astype(np.uint8) | |
| cv2.imwrite(save_name, lm_img) | |
| def load_data(img_name, txt_name): | |
| return cv2.imread(img_name), np.loadtxt(txt_name) | |
| # create tensorflow graph for landmark detector | |
| def load_lm_graph(graph_filename): | |
| with tf.gfile.GFile(graph_filename, 'rb') as f: | |
| graph_def = tf.GraphDef() | |
| graph_def.ParseFromString(f.read()) | |
| with tf.Graph().as_default() as graph: | |
| tf.import_graph_def(graph_def, name='net') | |
| img_224 = graph.get_tensor_by_name('net/input_imgs:0') | |
| output_lm = graph.get_tensor_by_name('net/lm:0') | |
| lm_sess = tf.Session(graph=graph) | |
| return lm_sess,img_224,output_lm | |
| # landmark detection | |
| def detect_68p(img_path,sess,input_op,output_op): | |
| print('detecting landmarks......') | |
| names = [i for i in sorted(os.listdir( | |
| img_path)) if 'jpg' in i or 'png' in i or 'jpeg' in i or 'PNG' in i] | |
| vis_path = os.path.join(img_path, 'vis') | |
| remove_path = os.path.join(img_path, 'remove') | |
| save_path = os.path.join(img_path, 'landmarks') | |
| if not os.path.isdir(vis_path): | |
| os.makedirs(vis_path) | |
| if not os.path.isdir(remove_path): | |
| os.makedirs(remove_path) | |
| if not os.path.isdir(save_path): | |
| os.makedirs(save_path) | |
| for i in range(0, len(names)): | |
| name = names[i] | |
| print('%05d' % (i), ' ', name) | |
| full_image_name = os.path.join(img_path, name) | |
| txt_name = '.'.join(name.split('.')[:-1]) + '.txt' | |
| full_txt_name = os.path.join(img_path, 'detections', txt_name) # 5 facial landmark path for each image | |
| # if an image does not have detected 5 facial landmarks, remove it from the training list | |
| if not os.path.isfile(full_txt_name): | |
| move(full_image_name, os.path.join(remove_path, name)) | |
| continue | |
| # load data | |
| img, five_points = load_data(full_image_name, full_txt_name) | |
| input_img, scale, bbox = align_for_lm(img, five_points) # align for 68 landmark detection | |
| # if the alignment fails, remove corresponding image from the training list | |
| if scale == 0: | |
| move(full_txt_name, os.path.join( | |
| remove_path, txt_name)) | |
| move(full_image_name, os.path.join(remove_path, name)) | |
| continue | |
| # detect landmarks | |
| input_img = np.reshape( | |
| input_img, [1, 224, 224, 3]).astype(np.float32) | |
| landmark = sess.run( | |
| output_op, feed_dict={input_op: input_img}) | |
| # transform back to original image coordinate | |
| landmark = landmark.reshape([68, 2]) + mean_face | |
| landmark[:, 1] = 223 - landmark[:, 1] | |
| landmark = landmark / scale | |
| landmark[:, 0] = landmark[:, 0] + bbox[0] | |
| landmark[:, 1] = landmark[:, 1] + bbox[1] | |
| landmark[:, 1] = img.shape[0] - 1 - landmark[:, 1] | |
| if i % 100 == 0: | |
| draw_landmarks(img, landmark, os.path.join(vis_path, name)) | |
| save_label(landmark, os.path.join(save_path, txt_name)) | |