Add pytorch/predict.py, fix webroot in pytorch/models/drn.py

pytorch/models/drn.py

@@ -1,5 +1,3 @@
-import pdb
-
 import torch.nn as nn
 import math
 import torch.utils.model_zoo as model_zoo
@@ -9,6 +7,8 @@
 
 
 webroot = 'https://tigress-web.princeton.edu/~fy/drn/models/'
+webroot = 'http://dl.yf.io/drn/'
+# https://github.com/fyu/drn/issues/45
 
 model_urls = {
     'drn-c-26': webroot + 'drn_c_26-ddedf421.pth',

pytorch/predict.py

@@ -0,0 +1,104 @@
+import imutils
+import torch
+import cv2
+import numpy as np
+from torch.utils.data import DataLoader
+
+from utils import NUM_WALL_CORNERS
+from options import parse_args
+from models.model import Model
+from IP import reconstructFloorplan
+import matplotlib.pyplot as plt
+
+
+def load_image(img_path):
+    image = cv2.imread(img_path, 0)
+    # image = imutils.resize(image, 256, 256)
+    image = cv2.resize(image, (256, 256), interpolation=cv2.INTER_AREA)
+    image = np.stack((image,) * 3, axis=-1)
+    image = (image.astype(np.float32) / 255 - 0.5).transpose((2, 0, 1))
+    image = image[np.newaxis, ...]
+    np_print({'image': image})
+    return image
+
+
+def np_print(arrays):
+    for name, array in arrays.items():
+        print('*** ', name, array.shape, (array.min(), array.max()), np.unique(array).size)
+
+
+def plot_images(images):
+    subx, suby = {
+        1: (1, 1),
+        2: (1, 2),
+        3: (1, 3),
+        4: (2, 2),
+        5: (2, 3),
+        6: (2, 3),
+        7: (3, 3),
+        8: (3, 3),
+        9: (3, 3),
+        10: (3, 4),
+        11: (3, 4),
+        12: (3, 4),
+        13: (3, 5),
+        14: (3, 5),
+        15: (3, 5),
+        16: (4, 4),
+    }[len(images)]
+
+    fig = plt.figure()
+    count = 0
+    for title, image in images.items():
+        count += 1
+        ax = fig.add_subplot(subx, suby, count)
+        ax.set_title(title)
+        mappable = ax.imshow(image, cmap='jet')
+        fig.colorbar(mappable, ax=ax)
+    plt.show()
+
+
+def main(img_path):
+    options = parse_args()
+    model = Model(options)
+    model.load_state_dict(torch.load('checkpoint.pth', map_location=torch.device('cpu')))
+
+    corner_pred, icon_pred, room_pred = model(torch.tensor(load_image(img_path)))
+
+    corner_heatmaps = corner_pred[0].detach().cpu().numpy()
+    icon_heatmaps = torch.nn.functional.softmax(icon_pred[0], dim=-1).detach().cpu().numpy()
+    room_heatmaps = torch.nn.functional.softmax(room_pred[0], dim=-1).detach().cpu().numpy()
+
+    wallCornerHeatmaps = corner_heatmaps[:, :, :NUM_WALL_CORNERS]
+    doorCornerHeatmaps = corner_heatmaps[:, :, NUM_WALL_CORNERS:NUM_WALL_CORNERS + 4]
+    iconCornerHeatmaps = corner_heatmaps[:, :, -4:]
+
+    maps = {
+        'original': cv2.imread(img_path),
+        'corner_heatmaps': corner_heatmaps.max(-1),
+        'icon_heatmaps': icon_heatmaps.max(-1),
+        'room_heatmaps': room_heatmaps.max(-1),
+        'corner_pred': np.squeeze(corner_pred.max(-1)[1].detach().cpu().numpy()),
+        'icon_pred': np.squeeze(icon_pred.max(-1)[1].detach().cpu().numpy()),
+        'room_pred': np.squeeze(room_pred.max(-1)[1].detach().cpu().numpy()),
+        'wallCornerHeatmaps': wallCornerHeatmaps.max(-1),
+        'doorCornerHeatmaps': doorCornerHeatmaps.max(-1),
+        'iconCornerHeatmaps': iconCornerHeatmaps.max(-1),
+    }
+    np_print(maps)
+    plot_images(maps)
+
+    reconstructFloorplan(wallCornerHeatmaps, doorCornerHeatmaps, iconCornerHeatmaps,
+                         icon_heatmaps, room_heatmaps,
+                         output_prefix='output-', densityImage=None,
+                         gt_dict=None, gt=False, gap=-1, distanceThreshold=-1, lengthThreshold=-1,
+                         debug_prefix='test', heatmapValueThresholdWall=None,
+                         heatmapValueThresholdDoor=None, heatmapValueThresholdIcon=None,
+                         enableAugmentation=True)
+
+if __name__ == '__main__':
+    if len(sys.argv) > 1:
+        img_path = sys.argv[1]
+    else:
+        img_path = 'input.jpg'
+    main(img_path)