Merge pull request #101 from HuangJunJie2017/master

Add a new demo for estimating optical flow on a single image pair

Author: fitsumreda

run_a_pair.py

@@ -0,0 +1,44 @@
+import torch
+import numpy as np
+import argparse
+
+from Networks.FlowNet2 import FlowNet2  # the path is depended on where you create this module
+from frame_utils import read_gen  # the path is depended on where you create this module
+
+if __name__ == '__main__':
+    # obtain the necessary args for construct the flownet framework
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--fp16', action='store_true', help='Run model in pseudo-fp16 mode (fp16 storage fp32 math).')
+    parser.add_argument("--rgb_max", type=float, default=255.)
+    args = parser.parse_args()
+
+    # initial a Net
+    net = FlowNet2(args).cuda()
+    # load the state_dict
+    dict = torch.load("/home/hjj/PycharmProjects/flownet2_pytorch/FlowNet2_checkpoint.pth.tar")
+    net.load_state_dict(dict["state_dict"])
+
+    # load the image pair, you can find this operation in dataset.py
+    pim1 = read_gen("/home/hjj/flownet2-master/data/FlyingChairs_examples/0000007-img0.ppm")
+    pim2 = read_gen("/home/hjj/flownet2-master/data/FlyingChairs_examples/0000007-img1.ppm")
+    images = [pim1, pim2]
+    images = np.array(images).transpose(3, 0, 1, 2)
+    im = torch.from_numpy(images.astype(np.float32)).unsqueeze(0).cuda()
+
+    # process the image pair to obtian the flow
+    result = net(im).squeeze()
+
+
+    # save flow, I reference the code in scripts/run-flownet.py in flownet2-caffe project
+    def writeFlow(name, flow):
+        f = open(name, 'wb')
+        f.write('PIEH'.encode('utf-8'))
+        np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
+        flow = flow.astype(np.float32)
+        flow.tofile(f)
+        f.flush()
+        f.close()
+
+
+    data = result.data.cpu().numpy().transpose(1, 2, 0)
+    writeFlow("/home/hjj/flownet2-master/data/FlyingChairs_examples/0000007-img.flo", data)