import cv2
import sys
import numpy as np
import datetime
import os
import glob
from RetinaFace_linux.retinaface import RetinaFace

thresh = 0.8
scales = [1024, 1980]

count = 1

gpuid = 0
detector = RetinaFace('./model/resnet-50', 0, gpuid, 'net3')

img = cv2.imread('t1.jpg')
print(img.shape)
im_shape = img.shape
target_size = scales[0]
max_size = scales[1]
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
#im_scale = 1.0
#if im_size_min>target_size or im_size_max>max_size:
im_scale = float(target_size) / float(im_size_min)
# prevent bigger axis from being more than max_size:
if np.round(im_scale * im_size_max) > max_size:
    im_scale = float(max_size) / float(im_size_max)

print('im_scale', im_scale)

scales = [im_scale]
flip = False

for c in range(count):
  faces, landmarks = detector.detect(img, thresh, scales=scales, do_flip=flip)
  print(c, faces.shape, landmarks.shape)

if faces is not None:
  print('find', faces.shape[0], 'faces')
  for i in range(faces.shape[0]):
    #print('score', faces[i][4])
    box = faces[i].astype(np.int)
    #color = (255,0,0)
    color = (0,0,255)
    cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), color, 2)
    if landmarks is not None:
      landmark5 = landmarks[i].astype(np.int)
      #print(landmark.shape)
      for l in range(landmark5.shape[0]):
        color = (0,0,255)
        if l==0 or l==3:
          color = (0,255,0)
        cv2.circle(img, (landmark5[l][0], landmark5[l][1]), 1, color, 2)

  filename = './detector_test.jpg'
  print('writing', filename)
  cv2.imwrite(filename, img)

