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| import os | |
| from shutil import move | |
| import cv2 | |
| import numpy as np | |
| import tensorflow as tf | |
| from scipy.io import loadmat | |
| from util.preprocess import align_for_lm | |
| 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)) | |