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 = 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,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()
