add test single image

detect.py

@@ -0,0 +1,162 @@
+from __future__ import print_function
+import os
+import argparse
+import torch
+import torch.backends.cudnn as cudnn
+import numpy as np
+from data import cfg
+from layers.functions.prior_box import PriorBox
+from utils.nms.py_cpu_nms import py_cpu_nms
+import cv2
+from models.retinaface import RetinaFace
+from utils.box_utils import decode, decode_landm
+import time
+
+parser = argparse.ArgumentParser(description='Retinaface')
+
+parser.add_argument('-m', '--trained_model', default='./weights/Final_Retinaface.pth',
+                    type=str, help='Trained state_dict file path to open')
+parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference')
+parser.add_argument('--confidence_threshold', default=0.05, type=float, help='confidence_threshold')
+parser.add_argument('--top_k', default=5000, type=int, help='top_k')
+parser.add_argument('--nms_threshold', default=0.3, type=float, help='nms_threshold')
+parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
+parser.add_argument('-s', '--save_image', action="store_true", default=True, help='show detection results')
+parser.add_argument('--vis_thres', default=0.6, type=float, help='visualization_threshold')
+args = parser.parse_args()
+
+
+def check_keys(model, pretrained_state_dict):
+    ckpt_keys = set(pretrained_state_dict.keys())
+    model_keys = set(model.state_dict().keys())
+    used_pretrained_keys = model_keys & ckpt_keys
+    unused_pretrained_keys = ckpt_keys - model_keys
+    missing_keys = model_keys - ckpt_keys
+    print('Missing keys:{}'.format(len(missing_keys)))
+    print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
+    print('Used keys:{}'.format(len(used_pretrained_keys)))
+    assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
+    return True
+
+
+def remove_prefix(state_dict, prefix):
+    ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
+    print('remove prefix \'{}\''.format(prefix))
+    f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
+    return {f(key): value for key, value in state_dict.items()}
+
+
+def load_model(model, pretrained_path, load_to_cpu):
+    print('Loading pretrained model from {}'.format(pretrained_path))
+    if load_to_cpu:
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
+    else:
+        device = torch.cuda.current_device()
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
+    if "state_dict" in pretrained_dict.keys():
+        pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
+    else:
+        pretrained_dict = remove_prefix(pretrained_dict, 'module.')
+    check_keys(model, pretrained_dict)
+    model.load_state_dict(pretrained_dict, strict=False)
+    return model
+
+
+if __name__ == '__main__':
+    torch.set_grad_enabled(False)
+    # net and model
+    net = RetinaFace(phase="test")
+    net = load_model(net, args.trained_model, args.cpu)
+    net.eval()
+    print('Finished loading model!')
+    print(net)
+    cudnn.benchmark = True
+    device = torch.device("cpu" if args.cpu else "cuda")
+    net = net.to(device)
+
+    resize = 1
+
+    # testing begin
+    for i in range(100):
+        image_path = "./curve/test.jpg"
+        img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
+
+        img = np.float32(img_raw)
+
+        im_height, im_width, _ = img.shape
+        scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
+        img -= (104, 117, 123)
+        img = img.transpose(2, 0, 1)
+        img = torch.from_numpy(img).unsqueeze(0)
+        img = img.to(device)
+        scale = scale.to(device)
+
+        tic = time.time()
+        loc, conf, landms = net(img)  # forward pass
+        print('net forward time: {:.4f}'.format(time.time() - tic))
+
+        priorbox = PriorBox(cfg, image_size=(im_height, im_width))
+        priors = priorbox.forward()
+        priors = priors.to(device)
+        prior_data = priors.data
+        boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
+        boxes = boxes * scale / resize
+        boxes = boxes.cpu().numpy()
+        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
+        landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance'])
+        scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                               img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                               img.shape[3], img.shape[2]])
+        scale1 = scale1.to(device)
+        landms = landms * scale1 / resize
+        landms = landms.cpu().numpy()
+
+        # ignore low scores
+        inds = np.where(scores > args.confidence_threshold)[0]
+        boxes = boxes[inds]
+        landms = landms[inds]
+        scores = scores[inds]
+
+        # keep top-K before NMS
+        order = scores.argsort()[::-1][:args.top_k]
+        boxes = boxes[order]
+        landms = landms[order]
+        scores = scores[order]
+
+        # do NMS
+        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+        keep = py_cpu_nms(dets, args.nms_threshold)
+        # keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
+        dets = dets[keep, :]
+        landms = landms[keep]
+
+        # keep top-K faster NMS
+        dets = dets[:args.keep_top_k, :]
+        landms = landms[:args.keep_top_k, :]
+
+        dets = np.concatenate((dets, landms), axis=1)
+
+        # show image
+        if args.save_image:
+            for b in dets:
+                if b[4] < args.vis_thres:
+                    continue
+                text = "{:.4f}".format(b[4])
+                b = list(map(int, b))
+                cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
+                cx = b[0]
+                cy = b[1] + 12
+                cv2.putText(img_raw, text, (cx, cy),
+                            cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
+
+                # landms
+                cv2.circle(img_raw, (b[5], b[6]), 4, (0, 0, 255), 4)
+                cv2.circle(img_raw, (b[7], b[8]), 4, (0, 255, 255), 4)
+                cv2.circle(img_raw, (b[9], b[10]), 4, (255, 0, 255), 4)
+                cv2.circle(img_raw, (b[11], b[12]), 4, (0, 255, 0), 4)
+                cv2.circle(img_raw, (b[13], b[14]), 4, (255, 0, 0), 4)
+            # save image
+
+            name = "test.jpg"
+            cv2.imwrite(name, img_raw)
+

test_widerface.py

@@ -25,7 +25,7 @@
 parser.add_argument('--top_k', default=5000, type=int, help='top_k')
 parser.add_argument('--nms_threshold', default=0.3, type=float, help='nms_threshold')
 parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
-parser.add_argument('-s', '--show_image', action="store_true", default=True, help='show detection results')
+parser.add_argument('-s', '--save_image', action="store_true", default=True, help='show detection results')
 parser.add_argument('--vis_thres', default=0.5, type=float, help='visualization_threshold')
 args = parser.parse_args()
 
@@ -171,8 +171,8 @@ def load_model(model, pretrained_path, load_to_cpu):
 
         print('im_detect: {:d}/{:d} forward_pass_time: {:.4f}s misc: {:.4f}s'.format(i + 1, num_images, _t['forward_pass'].average_time, _t['misc'].average_time))
 
-        # show image
-        if args.show_image:
+        # save image
+        if args.save_image:
             for b in dets:
                 if b[4] < args.vis_thres:
                     continue