def predict_common_func1(final, image_paths, params, out_dir):
pred_od_bboxs = final.data.cpu().numpy()
for image_file in image_paths:
raw = Image.open(image_paths[0])
raw, _, _, _ = scale_image(raw, 512)
raw = np.array(raw)
im2show = np.copy(raw)
im2show = cv2.cvtColor(im2show, cv2.COLOR_RGB2BGR)
bbox = final.data[0].cpu().numpy()
bbox = [int(x) for x in bbox]
param = [params[0][0], params[1][0], params[2][0]]
# bbox = pts_trans_inv(bbox, param[0], param[1], param[2])
cv2.rectangle(im2show, (bbox[0], bbox[1]), (bbox[2], bbox[3]),
(255, 255, 0), 4)
# width = bbox[2] - bbox[0]
# height = bbox[3] - bbox[1]
#
# cv2.rectangle(im2show, (bbox[4]-width//2, bbox[5]-height//2), (bbox[4]+width//2, bbox[5]+height//2), (0, 255, 255), 4)
cv2.rectangle(im2show, (bbox[4] - 20, bbox[5] - 20),
(bbox[4] + 20, bbox[5] + 20), (255, 255, 0), 4)
cv2.circle(im2show, (bbox[4], bbox[5]), 4, (0, 255, 255))
cv2.imshow('test', im2show)
os.makedirs(out_dir, exist_ok=True)
out_file = os.path.join(
out_dir,
os.path.basename(image_file).split('.')[0] + '_det.jpg')
cv2.imwrite(out_file, im2show)
def cls_predict(val_data_loader, model, criterion, display):
model.eval()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
result = np.array([], dtype=int)
ious = np.array([], dtype=float)
images_list = []
end = time.time()
logger = []
trans = ToPILImage()
for num_iter, (images, _, image_paths, bboxs, bboxs_c, params) in enumerate(val_data_loader):
for image_file in image_paths:
images_list.append(image_file)
data_time.update(time.time() - end)
final, map = model(Variable(images))
# loss = criterion(final, bbox_od)
batch_time.update(time.time() - end)
end = time.time()
# im2show = np.copy(np.array(trans(images[0])))
# raw = cv2.imread(image_paths[0])
pred_od_bboxs = final.data.cpu().numpy()
gt_od_bboxs = bboxs.numpy()
tmp, tmp_ious = get_detect_od_array(pred_od_bboxs, gt_od_bboxs)
result = np.append(result, tmp)
ious = np.append(ious, tmp_ious)
raw = Image.open(image_paths[0])
raw,_,_,_ = scale_image(raw, 512)
raw = np.array(raw)
im2show = np.copy(raw)
im2show = cv2.cvtColor(im2show, cv2.COLOR_RGB2BGR)
bbox = final.data[0].cpu().numpy()
bbox = [int(x) for x in bbox]
param = [params[0][0], params[1][0], params[2][0]]
# bbox = pts_trans_inv(bbox, param[0], param[1], param[2])
cv2.rectangle(im2show, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 255, 0), 4)
# width = bbox[2] - bbox[0]
# height = bbox[3] - bbox[1]
#
# cv2.rectangle(im2show, (bbox[4]-width//2, bbox[5]-height//2), (bbox[4]+width//2, bbox[5]+height//2), (0, 255, 255), 4)
cv2.rectangle(im2show, (bbox[4] - 20, bbox[5] - 20), (bbox[4] + 20, bbox[5] + 20), (255, 255, 0), 4)
cv2.circle(im2show, (bbox[4], bbox[5]), 4, (0, 255, 255))
cv2.imshow('test', im2show)
cv2.waitKey(2000)
print_info = '[optic_disc detection]:\tthreshold:{}\tdetection accuracy:{}'.format(0.5, result.sum() / len(result))
print(print_info)
logger.append(print_info)
assert len(ious) == len(images_list)
error_image_list = []
error_thres = 0.5
for i in range(len(ious)):
if ious[i] < error_thres:
error_image_list.append(images_list[i])
print(error_image_list)
logger.append(''.format(error_image_list))
return logger
def single_processing(image_file, data_root, p_data_root):
image_file = image_file
xml_file = os.path.join(
data_root,
os.path.basename(image_file).split('.')[0] + '.xml')
if not os.path.exists(xml_file):
return
p_data_root = p_data_root
p_image_file = os.path.join(
p_data_root,
os.path.basename(image_file).split('.')[0] + '.png')
p_xml_file = os.path.join(
p_data_root,
os.path.basename(image_file).split('.')[0] + '.xml')
tree = ET.parse(xml_file)
pil_img = Image.open(image_file)
pil_img, l, u, ratio = scale_image(pil_img, 512)
for obj in tree.getiterator('object'):
if (obj.find('name').text == 'optic_disk'
or obj.find('name').text == 'optic_disc'
or obj.find('name').text == 'optic-disc'
or obj.find('name').text == 'macular'):
ann = {}
# ann['cls_id'] = obj.find('name').text
ann['ordered_id'] = 1 if (
obj.find('name').text == 'optic_disk'
or obj.find('name').text == 'optic_disc') else 2
# ann['bbox'] = [0] * 4
xmin = obj.find('bndbox').find('xmin')
ymin = obj.find('bndbox').find('ymin')
xmax = obj.find('bndbox').find('xmax')
ymax = obj.find('bndbox').find('ymax')
tmp = np.array([], dtype=int)
tmp = np.append(
tmp,
np.array([
int(xmin.text),
int(ymin.text),
int(xmax.text),
int(ymax.text)
]))
tmp = pts_trans(tmp, l, u, ratio)
tmp = [int(i) for i in tmp]
obj.find('bndbox').find('xmin').text = str(tmp[0])
obj.find('bndbox').find('ymin').text = str(tmp[1])
obj.find('bndbox').find('xmax').text = str(tmp[2])
obj.find('bndbox').find('ymax').text = str(tmp[3])
pil_img.save(p_image_file)
tree.write(p_xml_file)