pipeline_test_super_pixel_1.py
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import os
import numpy as np
import MNN
import cv2
import logging
from skimage import transform as trans
from face_landmark import Landmark_Detector
from face_detecter import Face_Detector
from face_id import Face_Recognizer
def preprocess(image, landmarks):
src = np.array([[38.2946, 51.6963],
[73.5318, 51.6963],
[56.0252, 71.7366],
[41.5493, 92.3655],
[70.7299, 92.3655] ], dtype=np.float32)
landmarks = np.array(landmarks)
dst = landmarks.astype(np.float32)
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2,:]
warped = cv2.warpAffine(image, M, (112, 112), borderValue=0.0)
return warped
def get_face(image, bbox):
face_area = image[bbox[1]:bbox[3], bbox[0]:bbox[2], ]
norm_image = cv2.cvtColor(face_area, cv2.COLOR_BGR2RGB).astype(np.float32)
norm_image = cv2.resize(norm_image, (112, 112))
return norm_image
def get_norm_face(image, landmarks):
norm_image = preprocess(image, landmarks)
# norm_image = cv2.cvtColor(norm_image, cv2.COLOR_BGR2RGB).astype(np.float32)
# norm_image = cv2.resize(norm_image, (112, 112))
# norm_image = norm_image.transpose((2, 0, 1))
# norm_image = norm_image.transpose((1,2,0))
# norm_image = cv2.resize(norm_image, (112, 112))[:,:,::-1]
return norm_image
def landmarks_process(all_landmarks, all_bboxes):
processed_landmarks = []
for l, landmarks in enumerate(all_landmarks):
bbox = all_bboxes[l]
face_w, face_h = bbox[3] - bbox[1], bbox[2] - bbox[0]
w_r, h_r = face_w / 112, face_h / 112
point_0 = ((landmarks[0][0] - bbox[0])/w_r, (landmarks[0][1] - bbox[1])/h_r)
point_1 = ((landmarks[1][0] - bbox[0])/w_r, (landmarks[1][1] - bbox[1])/h_r)
point_2 = ((landmarks[2][0] - bbox[0])/w_r, (landmarks[2][1] - bbox[1])/h_r)
point_3 = ((landmarks[3][0] - bbox[0])/w_r, (landmarks[3][1] - bbox[1])/h_r)
point_4 = ((landmarks[4][0] - bbox[0])/w_r, (landmarks[4][1] - bbox[1])/h_r)
processed_landmarks.append([point_0, point_1, point_2, point_3, point_4])
return processed_landmarks
det_face_model_path = r'/home/jwq/PycharmProjects/situ/src/face_det/Pytorch_Retinaface/FaceDetector3.mnn'
reg_face_id_model_path = r'/home/jwq/PycharmProjects/situ/src/face_id/insightface/recognition/arcface_torch/work_dirs/ms1mv3_r18/ms1mv3_r18_0.96200/ms1mv3_r18.mnn'
# reg_face_id_model_path = r'models/cls_face_mnn_1.0.0_v0.0.2.mnn'
det_face_landmark_path = r'models/det_landmarks_106_v0.0.1.mnn'
image_dir = r'/data2/face_id/situ_other/pipeline_test/'
image_list_txt_path = r'/data2/face_id/situ_other/test2.txt'
save_dir = r'results'
face_detector = Face_Detector(det_face_model_path)
face_recognizer = Face_Recognizer(reg_face_id_model_path)
face_landmark_detector = Landmark_Detector(det_face_landmark_path)
image_list_txt = open(image_list_txt_path, 'r')
image_list_txt_lines = image_list_txt.readlines()
hit = 0.01
hit_pos = 0.01
pre_pos = 0.01
pre_all = 0.01
positive_num = 0.01
for line_idx, image_list_txt_line in enumerate(image_list_txt_lines):
arr = image_list_txt_line.strip().split(' ')
label = arr[-1]
pre_all += 1
if label == '1':
positive_num += 1
id_image_name_arr = arr[0].split('/')
id_image_name = id_image_name_arr[-1]
id_image_name = id_image_name[2:]
id_image_dir = id_image_name_arr[-2]
id_image_path = os.path.join(image_dir, id_image_dir, id_image_name)
life_image_name_arr = arr[1].split('/')
life_image_name = life_image_name_arr[-1]
life_image_name = life_image_name[2:]
life_image_dir = life_image_name_arr[-2]
life_image_path = os.path.join(image_dir, life_image_dir, life_image_name)
face_det_thr = 0.5
face_recongnize_thr = 0.10
id_image = cv2.imread(id_image_path)
life_image = cv2.imread(life_image_path)
id_face_bboxes, _, id_max_idx = face_detector.detect(id_image, face_det_thr)
life_face_bboxes, _, life_max_idx = face_detector.detect(life_image, face_det_thr)
sub_dir = os.path.join(save_dir, str(line_idx))
if os.path.exists(sub_dir) is False:
os.mkdir(sub_dir)
if len(id_face_bboxes) == 0 or len(life_face_bboxes) == 0:
continue
id_landmark, id_face = face_landmark_detector.detect(id_image, id_face_bboxes[id_max_idx])
id_landmark = [id_landmark[104], id_landmark[105], id_landmark[46], id_landmark[84], id_landmark[90]]
id_norm_image = get_norm_face(id_face, id_landmark)
id_path = os.path.join(sub_dir, 'id.jpg')
cv2.imwrite(id_path, id_norm_image)
sub_life_dir = os.path.join(sub_dir, 'life')
if os.path.exists(sub_life_dir) is False:
os.mkdir(sub_life_dir)
for f, life_face_bbox in enumerate(life_face_bboxes):
# life_norm_image = get_norm_face(life_faces[f], life_face_landmarks[f])
life_landmark, life_face = face_landmark_detector.detect(life_image, life_face_bbox)
life_landmark = [life_landmark[104], life_landmark[105], life_landmark[46], life_landmark[84], life_landmark[90]]
life_norm_image = get_norm_face(life_face, life_landmark)
life_name = 'life_' + str(f) + '.jpg'
life_path = os.path.join(sub_life_dir, life_name)
cv2.imwrite(life_path, life_norm_image)