add data visualization script

data_visualization.py

@@ -0,0 +1,133 @@
+import argparse
+import os
+import random
+
+import cv2
+import hdf5storage as h5
+import matplotlib.pyplot as plt
+import numpy as np
+import paddle
+import paddle.nn.functional as F
+
+os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--data_root",
+    nargs="?",
+    type=str,
+    default="~/datasets/doc3d/",
+    help="Path to the downloaded dataset",
+)
+parser.add_argument(
+    "--folder", nargs="?", type=int, default=1, help="Folder ID to read from"
+)
+parser.add_argument(
+    "--output",
+    nargs="?",
+    type=str,
+    default="output.png",
+    help="Output filename for the image",
+)
+
+args = parser.parse_args()
+
+root = os.path.expanduser(args.data_root)
+folder = args.folder
+dirname = os.path.join(root, "img", str(folder))
+
+choices = [f for f in os.listdir(dirname) if "png" in f]
+fname = random.choice(choices)
+
+##### Read Image #####
+img_path = os.path.join(dirname, fname)
+img = cv2.imread(img_path)
+
+##### Read 3D Coords #####
+wc_path = os.path.join(root, "wc", str(folder), fname[:-3] + "exr")
+wc = cv2.imread(wc_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
+# scale wc
+# value obtained from the entire dataset
+xmx, xmn, ymx, ymn, zmx, zmn = (
+    1.2539363,
+    -1.2442188,
+    1.2396319,
+    -1.2289206,
+    0.6436657,
+    -0.67492497,
+)
+wc[:, :, 0] = (wc[:, :, 0] - zmn) / (zmx - zmn)
+wc[:, :, 1] = (wc[:, :, 1] - ymn) / (ymx - ymn)
+wc[:, :, 2] = (wc[:, :, 2] - xmn) / (xmx - xmn)
+
+##### Read Backward Map #####
+bm_path = os.path.join(root, "bm", str(folder), fname[:-3] + "mat")
+bm = h5.loadmat(bm_path)["bm"]
+
+##### Read UV Map #####
+uv_path = os.path.join(root, "uv", str(folder), fname[:-3] + "exr")
+uv = cv2.imread(uv_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
+
+##### Read Depth Map #####
+dmap_path = os.path.join(root, "dmap", str(folder), fname[:-3] + "exr")
+dmap = cv2.imread(dmap_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)[:, :, 0]
+#### do some clipping and scaling to display it
+dmap[dmap > 30.0] = 30
+dmap = 1 - ((dmap - np.min(dmap)) / (np.max(dmap) - np.min(dmap)))
+
+##### Read Normal Map #####
+norm_path = os.path.join(root, "norm", str(folder), fname[:-3] + "exr")
+norm = cv2.imread(norm_path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
+
+##### Read Albedo #####
+alb_path = os.path.join(root, "alb", str(folder), fname[:-3] + "png")
+alb = cv2.imread(alb_path)
+
+##### Read Checkerboard Image #####
+recon_path = os.path.join(root, "recon", str(folder), fname[:-8] + "chess480001.png")
+recon = cv2.imread(recon_path)
+
+##### Display image and GTs #####
+
+# use the backward mapping to dewarp the image
+# scale bm to -1.0 to 1.0
+bm_ = bm / np.array([448, 448])
+bm_ = (bm_ - 0.5) * 2
+bm_ = np.reshape(bm_, (1, 448, 448, 2))
+bm_ = paddle.to_tensor(bm_, dtype="float32")
+img_ = alb.transpose((2, 0, 1)).astype(np.float32) / 255.0
+img_ = np.expand_dims(img_, 0)
+img_ = paddle.to_tensor(img_, dtype="float32")
+uw = F.grid_sample(img_, bm_)
+uw = uw[0].numpy().transpose((1, 2, 0))
+
+f, axrr = plt.subplots(2, 5)
+for ax in axrr:
+    for a in ax:
+        a.set_xticks([])
+        a.set_yticks([])
+
+axrr[0][0].imshow(img)
+axrr[0][0].title.set_text("image")
+axrr[0][1].imshow(wc)
+axrr[0][1].title.set_text("3D coords")
+axrr[0][2].imshow(bm[:, :, 0])
+axrr[0][2].title.set_text("bm 0")
+axrr[0][3].imshow(bm[:, :, 1])
+axrr[0][3].title.set_text("bm 1")
+if uv is None:
+    uv = np.zeros_like(img)
+axrr[0][4].imshow(uv)
+axrr[0][4].title.set_text("uv map")
+axrr[1][0].imshow(dmap)
+axrr[1][0].title.set_text("depth map")
+axrr[1][1].imshow(norm)
+axrr[1][1].title.set_text("normal map")
+axrr[1][2].imshow(alb)
+axrr[1][2].title.set_text("albedo")
+axrr[1][3].imshow(recon)
+axrr[1][3].title.set_text("checkerboard")
+axrr[1][4].imshow(uw)
+axrr[1][4].title.set_text("gt unwarped")
+plt.tight_layout()
+plt.savefig(args.output)

utils.py

@@ -52,7 +52,6 @@ def unwarp(img, bm, bm_data_format="NCHW"):
     Returns:
         out (paddle.Tensor): The output image.
     """
-    print(bm.shape)
     _, _, h, w = img.shape
 
     if bm_data_format == "NHWC":