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widerface_val_torch140.py
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widerface_val_torch140.py
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from __future__ import print_function
import argparse
import math
import os
import pdb
import sys
import time
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.utils.data as data
import torchvision.transforms as transforms
from PIL import Image
from torch.autograd import Variable
from data import *
from data import BaseTransform, TestBaseTransform
from data import WIDERFace_CLASSES as labelmap
from data import (WIDERFace_ROOT, WIDERFaceAnnotationTransform,
WIDERFaceDetection)
from face_ssd import build_ssd
plt.switch_backend('agg')
parser = argparse.ArgumentParser(description='DSFD: Dual Shot Face Detector')
parser.add_argument('--trained_model', default='weights/WIDERFace_DSFD_RES152.pth',
type=str, help='Trained state_dict file path to open')
parser.add_argument('--save_folder', default='eval_tools/WIDERFace_DSFD_RES152_results/', type=str,
help='Dir to save results')
parser.add_argument('--visual_threshold', default=0.01, type=float,
help='Final confidence threshold')
parser.add_argument('--cuda', default=True, type=bool,
help='Use cuda to train model')
parser.add_argument('--widerface_root', default=WIDERFace_ROOT, help='Location of WIDERFACE root directory')
args = parser.parse_args()
if args.cuda and torch.cuda.is_available():
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
torch.set_default_tensor_type('torch.FloatTensor')
if not os.path.exists(args.save_folder):
os.mkdir(args.save_folder)
def detect_face(image, shrink):
cuda = args.cuda
x = image
if shrink != 1:
x = cv2.resize(image, None, None, fx=shrink, fy=shrink, interpolation=cv2.INTER_LINEAR)
#print('shrink:{}'.format(shrink))
width = x.shape[1]
height = x.shape[0]
x = x.astype(np.float32)
x -= np.array([104, 117, 123],dtype=np.float32)
x = torch.from_numpy(x).permute(2, 0, 1)
with torch.no_grad():
x = Variable(x.unsqueeze(0))
if cuda:
x = x.cuda()
#net.priorbox = PriorBoxLayer(width,height)
y = net(x)
detections = y.data
scale = torch.Tensor([width, height, width, height])
boxes=[]
scores = []
for i in range(detections.size(1)):
j = 0
while detections[0,i,j,0] >= 0.01:
score = detections[0,i,j,0].cpu().numpy()
pt = (detections[0, i, j, 1:]*scale).cpu().numpy()
boxes.append([pt[0],pt[1],pt[2],pt[3]])
scores.append(score)
j += 1
if j >= detections.size(2):
break
det_conf = np.array(scores)
boxes = np.array(boxes)
if boxes.shape[0] == 0:
return np.array([[0,0,0,0,0.001]])
det_xmin = boxes[:,0] / shrink
det_ymin = boxes[:,1] / shrink
det_xmax = boxes[:,2] / shrink
det_ymax = boxes[:,3] / shrink
det = np.column_stack((det_xmin, det_ymin, det_xmax, det_ymax, det_conf))
keep_index = np.where(det[:, 4] >= 0)[0]
det = det[keep_index, :]
return det
def multi_scale_test(image, max_im_shrink):
# shrink detecting and shrink only detect big face
st = 0.5 if max_im_shrink >= 0.75 else 0.5 * max_im_shrink
det_s = detect_face(image, st)
if max_im_shrink > 0.75:
det_s = np.row_stack((det_s,detect_face(image,0.75)))
index = np.where(np.maximum(det_s[:, 2] - det_s[:, 0] + 1, det_s[:, 3] - det_s[:, 1] + 1) > 30)[0]
det_s = det_s[index, :]
# enlarge one times
bt = min(2, max_im_shrink) if max_im_shrink > 1 else (st + max_im_shrink) / 2
det_b = detect_face(image, bt)
# enlarge small iamge x times for small face
if max_im_shrink > 1.5:
det_b = np.row_stack((det_b,detect_face(image,1.5)))
if max_im_shrink > 2:
bt *= 2
while bt < max_im_shrink: # and bt <= 2:
det_b = np.row_stack((det_b, detect_face(image, bt)))
bt *= 2
det_b = np.row_stack((det_b, detect_face(image, max_im_shrink)))
# enlarge only detect small face
if bt > 1:
index = np.where(np.minimum(det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1) < 100)[0]
det_b = det_b[index, :]
else:
index = np.where(np.maximum(det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1) > 30)[0]
det_b = det_b[index, :]
return det_s, det_b
def multi_scale_test_pyramid(image, max_shrink):
# shrink detecting and shrink only detect big face
det_b = detect_face(image, 0.25)
index = np.where(
np.maximum(det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1)
> 30)[0]
det_b = det_b[index, :]
st = [1.25, 1.75, 2.25]
for i in range(len(st)):
if (st[i] <= max_shrink):
det_temp = detect_face(image, st[i])
# enlarge only detect small face
if st[i] > 1:
index = np.where(
np.minimum(det_temp[:, 2] - det_temp[:, 0] + 1,
det_temp[:, 3] - det_temp[:, 1] + 1) < 100)[0]
det_temp = det_temp[index, :]
else:
index = np.where(
np.maximum(det_temp[:, 2] - det_temp[:, 0] + 1,
det_temp[:, 3] - det_temp[:, 1] + 1) > 30)[0]
det_temp = det_temp[index, :]
det_b = np.row_stack((det_b, det_temp))
return det_b
def flip_test(image, shrink):
image_f = cv2.flip(image, 1)
det_f = detect_face(image_f, shrink)
det_t = np.zeros(det_f.shape)
det_t[:, 0] = image.shape[1] - det_f[:, 2]
det_t[:, 1] = det_f[:, 1]
det_t[:, 2] = image.shape[1] - det_f[:, 0]
det_t[:, 3] = det_f[:, 3]
det_t[:, 4] = det_f[:, 4]
return det_t
def bbox_vote(det):
order = det[:, 4].ravel().argsort()[::-1]
det = det[order, :]
dets = np.zeros((0, 5),dtype=np.float32)
while det.shape[0] > 0:
# IOU
area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1)
xx1 = np.maximum(det[0, 0], det[:, 0])
yy1 = np.maximum(det[0, 1], det[:, 1])
xx2 = np.minimum(det[0, 2], det[:, 2])
yy2 = np.minimum(det[0, 3], det[:, 3])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
o = inter / (area[0] + area[:] - inter)
# get needed merge det and delete these det
merge_index = np.where(o >= 0.3)[0]
det_accu = det[merge_index, :]
det = np.delete(det, merge_index, 0)
if merge_index.shape[0] <= 1:
continue
det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4))
max_score = np.max(det_accu[:, 4])
det_accu_sum = np.zeros((1, 5))
det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4], axis=0) / np.sum(det_accu[:, -1:])
det_accu_sum[:, 4] = max_score
try:
dets = np.row_stack((dets, det_accu_sum))
except:
dets = det_accu_sum
dets = dets[0:750, :]
return dets
def write_to_txt(f, det , event, im_name):
f.write('{:s}\n'.format(str(event[0][0])[2:-1] + '/' + im_name + '.jpg'))
f.write('{:d}\n'.format(det.shape[0]))
for i in range(det.shape[0]):
xmin = det[i][0]
ymin = det[i][1]
xmax = det[i][2]
ymax = det[i][3]
score = det[i][4]
#f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'.
# format(xmin, ymin, (xmax - xmin + 1), (ymax - ymin + 1), score))
f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'.
format(np.floor(xmin), np.floor(ymin), np.ceil(xmax - xmin + 1), np.ceil(ymax - ymin + 1), score))
# load net
cfg = widerface_640
num_classes = len(WIDERFace_CLASSES) + 1 # +1 background
net = build_ssd('test', cfg['min_dim'], num_classes) # initialize SSD
net.load_state_dict(torch.load(args.trained_model))
net.cuda()
net.eval()
print('Finished loading model!')
# load data
testset = WIDERFaceDetection(args.widerface_root, 'val' , None, WIDERFaceAnnotationTransform())
#testset = WIDERFaceDetection(args.widerface_root, 'test' , None, WIDERFaceAnnotationTransform())
def vis_detections(imgid, im, dets, thresh=0.5):
'''Draw detected bounding boxes.'''
class_name = 'face'
inds = np.where(dets[:, -1] >= thresh)[0]
if len(inds) == 0:
return
im = im[:, :, (2, 1, 0)]
print (len(inds))
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='green', linewidth=2.5)
)
'''
ax.text(bbox[0], bbox[1] - 5,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=10, color='white')
'''
#ax.set_title(('{} detections with '
# 'p({} | box) >= {:.1f}').format(class_name, class_name,
# thresh),
# fontsize=10)
plt.axis('off')
plt.tight_layout()
plt.savefig('./val_pic_res/'+str(imgid), dpi=fig.dpi)
print('Finished loading data')
def test_widerface():
# evaluation
cuda = args.cuda
transform = TestBaseTransform((104, 117, 123))
thresh=cfg['conf_thresh']
save_path = args.save_folder
num_images = len(testset)
for i in range(0, num_images):
image = testset.pull_image(i)
img_id, annotation = testset.pull_anno(i)
event = testset.pull_event(i)
print('Testing image {:d}/{:d} {}....'.format(i+1, num_images , img_id))
#max_im_shrink = ( (2000.0*2000.0) / (img.shape[0] * img.shape[1])) ** 0.5
max_im_shrink = (0x7fffffff / 200.0 / (image.shape[0] * image.shape[1])) ** 0.5 # the max size of input image for caffe
max_im_shrink = 3 if max_im_shrink > 3 else max_im_shrink
shrink = max_im_shrink if max_im_shrink < 1 else 1
det0 = detect_face(image, shrink) # origin test
det1 = flip_test(image, shrink) # flip test
[det2, det3] = multi_scale_test(image, max_im_shrink)#min(2,1400/min(image.shape[0],image.shape[1]))) #multi-scale test
det4 = multi_scale_test_pyramid(image, max_im_shrink)
det = np.row_stack((det0, det1, det2, det3, det4))
dets = bbox_vote(det)
#vis_detections(i ,image, dets , 0.8)
if not os.path.exists(save_path + event):
os.makedirs(save_path + event)
f = open(save_path + event + '/' + img_id.split('.')[0] + '.txt', 'w')
write_to_txt(f, dets , event, img_id)
if __name__=='__main__':
test_widerface()