def detect(img_path, model_path):
file_id = utils.get_file_id(img_path)
img, img_raw, scale = read_img(img_path)
head_detector = Head_Detector_VGG16(ratios=[1], anchor_scales=[2, 4])
trainer = Head_Detector_Trainer(head_detector).cuda()
trainer.load(model_path)
img = at.totensor(img)
img = img[None, :, :, :]
img = img.cuda().float()
st = time.time()
pred_bboxes_, _ = head_detector.predict(img,
scale,
mode='evaluate',
thresh=THRESH)
et = time.time()
tt = et - st
print("[INFO] Head detection over. Time taken: {:.4f} s".format(tt))
for i in range(pred_bboxes_.shape[0]):
ymin, xmin, ymax, xmax = pred_bboxes_[i, :]
utils.draw_bounding_box_on_image_array(img_raw, ymin, xmin, ymax, xmax)
plt.axis('off')
plt.imshow(img_raw)
if SAVE_FLAG == 1:
plt.savefig(os.path.join(opt.test_output_path, file_id + '.png'),
bbox_inches='tight',
pad_inches=0)
else:
plt.show()
def detect(img_path, model_path):
file_id = utils.get_file_id(img_path)
img, img_raw, scale = read_img(img_path)
head_detector = Head_Detector_VGG16(ratios=[1], anchor_scales=[2, 4])
dict = torch.load(opt.caffe_pretrain_path)
head_detector.load_state_dict(dict['model'])
trainer = Head_Detector_Trainer(head_detector).cuda()
trainer.load(model_path)
img = at.totensor(img)
img = img[None, :, :, :]
img = img.cuda().float()
st = time.time()
pred_bboxes_, _ = head_detector.predict(img,
scale,
mode='evaluate',
thresh=THRESH)
et = time.time()
tt = et - st
print("[INFO] Head detection over. Time taken: {:.4f} s".format(tt))
for i in range(pred_bboxes_.shape[0]):
ymin, xmin, ymax, xmax = pred_bboxes_[i, :]
utils.draw_bounding_box_on_image_array(img_raw, ymin, xmin, ymax, xmax)
cv2.imshow('asdf', img_raw)
cv2.waitKey()
def detect(img_path, model_path):
file_id = utils.get_file_id(img_path)
img, img_raw, scale,scale_ = read_img(img_path)
print("dim1",img_raw.ndim)
head_detector = Head_Detector_VGG16(ratios=[1], anchor_scales=[2,4])
trainer = Head_Detector_Trainer(head_detector).cuda()
trainer.load(model_path)
img = at.totensor(img)
img = img[None, : ,: ,:]
img = img.cuda().float()
st = time.time()
pred_bboxes_, _ = head_detector.predict(img, scale, mode='evaluate', thresh=THRESH)
et = time.time()
tt = et - st
print ("[INFO] Head detection over. Time taken: {:.4f} s".format(tt))
for i in range(pred_bboxes_.shape[0]):
print(i)
ymin, xmin, ymax, xmax = pred_bboxes_[i,:]
print(ymin, xmin, ymax, xmax)
image_raw=Image.fromarray(np.uint8(img_raw))
utils.draw_bounding_box_on_image(image_raw,ymin*scale_, xmin*scale_, ymax*scale_, xmax*scale_)
img_raw=np.array(image_raw)
image_raw=Image.fromarray(np.uint8(img_raw))
if SAVE_FLAG == 1:
#image_raw.save('/home/hx/Project/FCHD-Fully-Convolutional-Head-Detector-master/'+file_id+'_1.png')
image_raw.save(write_path+'/'+os.path.basename(img_path))
frame_end = cv2.imread(write_path+'/'+os.path.basename(img_path))
cv2.imshow("frame_end",frame_end)
key_end = cv2.waitKey(1) & 0xFF
else:
image_raw.show()
def detect2(img, img_raw, scale, model_path, file_file):
head_detector = Head_Detector_VGG16(ratios=[1], anchor_scales=[2, 4])
trainer = Head_Detector_Trainer(head_detector).cuda()
# load model
trainer.load(model_path)
img = at.totensor(img)
img = img[None, :, :, :]
img = img.cuda().float()
# predict model
pred_bboxes_, _ = head_detector.predict(img,
scale,
mode='evaluate',
thresh=THRESH)
for i in range(pred_bboxes_.shape[0]):
ymin, xmin, ymax, xmax = pred_bboxes_[i, :]
file_file.write(
str(int(xmin / scale)) + ',' + str(int(ymin / scale)) + ',' +
str(int(xmax / scale)) + ',' + str(int(ymax / scale)) + ',' + '0' +
' ')
def train():
# Get the dataset
for phase in phases:
if phase == 'train':
if dataset_name == 'hollywood':
train_data_list_path = os.path.join(
opt.hollywood_dataset_root_path, 'hollywood_train.idl')
train_data_list = utils.get_phase_data_list(
train_data_list_path, dataset_name)
if dataset_name == 'brainwash':
train_data_list_path = os.path.join(
opt.brainwash_dataset_root_path, 'brainwash_train.idl')
train_data_list = utils.get_phase_data_list(
train_data_list_path, dataset_name)
elif phase == 'val':
if dataset_name == 'hollywood':
val_data_list_path = os.path.join(
opt.hollywood_dataset_root_path, 'hollywood_val.idl')
val_data_list = utils.get_phase_data_list(
val_data_list_path, dataset_name)
if dataset_name == 'brainwash':
val_data_list_path = os.path.join(
opt.brainwash_dataset_root_path, 'brainwash_val.idl')
val_data_list = utils.get_phase_data_list(
val_data_list_path, dataset_name)
elif phase == 'test':
if dataset_name == 'hollywood':
test_data_list_path = os.path.join(
opt.hollywood_dataset_root_path, 'hollywood_test.idl')
test_data_list = utils.get_phase_data_list(
test_data_list_path, dataset_name)
if dataset_name == 'brainwash':
test_data_list_path = os.path.join(
opt.brainwash_dataset_root_path, 'brainwash_test.idl')
test_data_list = utils.get_phase_data_list(
test_data_list_path, dataset_name)
print("Number of images for training: %s" % (len(train_data_list)))
print("Number of images for val: %s" % (len(val_data_list)))
print("Number of images for test: %s" % (len(test_data_list)))
if data_check_flag:
utils.check_loaded_data(train_data_list[random.randint(
1, len(train_data_list))])
utils.check_loaded_data(val_data_list[random.randint(
1, len(val_data_list))])
utils.check_loaded_data(test_data_list[random.randint(
1, len(test_data_list))])
# Load the train dataset
train_dataset = Dataset(train_data_list)
test_dataset = Dataset(val_data_list)
print("Load data.")
train_dataloader = data_.DataLoader(train_dataset,
batch_size=1,
shuffle=True,
num_workers=1)
test_dataloader = data_.DataLoader(test_dataset,
batch_size=1,
shuffle=True,
num_workers=1)
# Initialize the head detector.
head_detector_vgg16 = Head_Detector_VGG16(ratios=[1],
anchor_scales=[8, 16])
print("model construct completed")
trainer = Head_Detector_Trainer(head_detector_vgg16).cuda()
lr_ = opt.lr
for epoch in range(opt.epoch):
trainer.reset_meters()
for ii, (img, bbox_, scale) in enumerate(train_dataloader):
scale = at.scalar(scale)
img, bbox = img.cuda().float(), bbox_.cuda()
img, bbox = Variable(img), Variable(bbox)
_, _, _ = trainer.train_step(img, bbox, scale)
print("Forward and backward pass done.")
if (ii + 1) % opt.plot_every == 0:
trainer.vis.plot_many(trainer.get_meter_data())
ori_img_ = inverse_normalize(at.tonumpy(img[0]))
gt_img = visdom_bbox(ori_img_, at.tonumpy(bbox_[0]))
trainer.vis.img('gt_img', gt_img)
rois, _ = trainer.head_detector.predict(img,
scale=scale,
mode='visualize')
pred_img = visdom_bbox(ori_img_, at.tonumpy(rois))
trainer.vis.img('pred_img', pred_img)
trainer.vis.text(str(trainer.rpn_cm.value().tolist()),
win='rpn_cm')
avg_test_CorrLoc = eval(test_dataloader, head_detector_vgg16)
print("Epoch {} of {}.".format(epoch + 1, opt.epoch))
print(" test average corrLoc accuracy:\t\t{:.3f}".format(
avg_test_CorrLoc))
model_save_path = trainer.save(best_map=avg_test_CorrLoc)
if epoch == 8:
trainer.load(model_save_path)
trainer.head_detector.scale_lr(opt.lr_decay)
lr_ = lr_ * opt.lr_decay
import cv2
from src.head_detector_vgg16 import Head_Detector_VGG16
from trainer import Head_Detector_Trainer
from PIL import Image
import numpy as np
from data.dataset import preprocess
import src.array_tool as at
THRESH = 0.0075
IM_RESIZE = True
model_path = "/home/zhang/FCHD-Fully-Convolutional-Head-Detector/checkpoints/head_detector_final"
head_detector = Head_Detector_VGG16(ratios=[1], anchor_scales=[2, 4])
trainer = Head_Detector_Trainer(head_detector).cuda()
trainer.load(model_path)
def read_img(path):
f = Image.fromarray(path)
if IM_RESIZE:
f = f.resize((640, 480), Image.ANTIALIAS)
f.convert('RGB')
img_raw = np.asarray(f, dtype=np.uint8)
img_raw_final = img_raw.copy()
img = np.asarray(f, dtype=np.float32)
_, H, W = img.shape
img = img.transpose((2, 0, 1))
img = preprocess(img)
_, o_H, o_W = img.shape
def __init__(self, model_path="/home/ubuntu/suraj/package/FCHD/checkpoints/head_detector_final"):
self.head_detector = Head_Detector_VGG16(ratios=[1], anchor_scales=[2,4])
trainer = Head_Detector_Trainer(self.head_detector).cuda()
trainer.load(model_path)
import numpy as np
from data.dataset import preprocess
import matplotlib.pyplot as plt
import src.array_tool as at
from src.vis_tool import visdom_bbox
import argparse
import src.utils as utils
from src.config import opt
#import simplejson as json
os.environ["CUDA_VISIBLE_DEVICES"]="0,1"
head_detector = Head_Detector_VGG16(ratios=[1], anchor_scales=[2,4])
trainer = Head_Detector_Trainer(head_detector).cuda()
trainer.load('./checkpoints/head_detector_final')
#data = {"taskId":"1", "roomId":"1", "liveUrl":"3.mp4", "recognitionDuration":30,"verifyCode":"xxxx"}
class IndexHandler(tornado.web.RequestHandler):
executor = ThreadPoolExecutor(200)
@tornado.web.asynchronous
@tornado.gen.coroutine
def post(self):
try:
#sends= json_decode(self.request.body)
sends = self.request.body
data = json.loads(self.request.body)
liveUrl = data['liveUrl']
recognitionDuration = data['recognitionDuration']
