Upload 24 files

api_inference.py

@@ -1,18 +1,20 @@
-from PIL import Image
-import numpy as np
-from deepfloorplan_inference import DeepFloorPlanModel
-
-class EndpointModel:
-    def __init__(self):
-        self.model = DeepFloorPlanModel(model_dir='pretrained')
-
-    def __call__(self, image):
-        if isinstance(image, np.ndarray):
-            image = Image.fromarray(image)
-        result = self.model.predict(image)
-        return Image.fromarray(result.astype(np.uint8))
-
-model = EndpointModel()
-
-def predict(image):
-    return model(image)
+from PIL import Image
+import numpy as np
+from deepfloorplan_inference import DeepFloorPlanModel
+
+class EndpointModel:
+    def __init__(self):
+        self.model = DeepFloorPlanModel(model_dir='pretrained')
+
+    def __call__(self, image):
+        # image: PIL Image or numpy array
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
+        result = self.model.predict(image)
+        return Image.fromarray(result.astype(np.uint8))
+
+# For Hugging Face Inference Endpoints
+model = EndpointModel()
+
+def predict(image):
+    return model(image) 

app.py

@@ -1,22 +1,25 @@
-import gradio as gr
-import numpy as np
-from PIL import Image
-from deepfloorplan_inference import DeepFloorPlanModel
-
-model = DeepFloorPlanModel(model_dir='pretrained')
-
-def predict_floorplan(image):
-    result = model.predict(image)
-    return Image.fromarray(result.astype(np.uint8))
-
-iface = gr.Interface(
-    fn=predict_floorplan,
-    inputs=gr.Image(type="pil", label="Upload Floorplan Image"),
-    outputs=gr.Image(type="pil", label="Predicted Segmentation"),
-    title="Deep Floor Plan Segmentation",
-    description="Upload a floorplan image to get the predicted segmentation using the Deep Floor Plan model.",
-    allow_flagging="never"
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import gradio as gr
+import numpy as np
+from PIL import Image
+from deepfloorplan_inference import DeepFloorPlanModel
+
+# Load model once at startup
+model = DeepFloorPlanModel(model_dir='pretrained')
+
+def predict_floorplan(image):
+    # image: PIL Image from Gradio
+    result = model.predict(image)
+    # Convert numpy array to PIL Image for Gradio output
+    return Image.fromarray(result.astype(np.uint8))
+
+iface = gr.Interface(
+    fn=predict_floorplan,
+    inputs=gr.Image(type="pil", label="Upload Floorplan Image"),
+    outputs=gr.Image(type="pil", label="Predicted Segmentation"),
+    title="Deep Floor Plan Segmentation",
+    description="Upload a floorplan image to get the predicted segmentation using the Deep Floor Plan model.",
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch() 

dataset/download_links.txt

@@ -0,0 +1 @@
+https://mycuhk-my.sharepoint.com/:f:/g/personal/1155052510_link_cuhk_edu_hk/EseSIeHQgPxArPlNpGdVp38BIjUg70jMiAO-w4f3s8B_dg?e=UXKbYO

dataset/newyork

@@ -0,0 +1 @@
+/home/zlzeng/floorplan_v2/dataset/newyork

dataset/r2v_test.txt

@@ -0,0 +1,100 @@
+../dataset/jp/test/100_input.jpg	../dataset/jp/test/100_wall.png	../dataset/jp/test/100_close.png	../dataset/jp/test/100_rooms.png	../dataset/jp/test/100_close_wall.png
+../dataset/jp/test/10_input.jpg	../dataset/jp/test/10_wall.png	../dataset/jp/test/10_close.png	../dataset/jp/test/10_rooms.png	../dataset/jp/test/10_close_wall.png
+../dataset/jp/test/11_input.jpg	../dataset/jp/test/11_wall.png	../dataset/jp/test/11_close.png	../dataset/jp/test/11_rooms.png	../dataset/jp/test/11_close_wall.png
+../dataset/jp/test/12_input.jpg	../dataset/jp/test/12_wall.png	../dataset/jp/test/12_close.png	../dataset/jp/test/12_rooms.png	../dataset/jp/test/12_close_wall.png
+../dataset/jp/test/13_input.jpg	../dataset/jp/test/13_wall.png	../dataset/jp/test/13_close.png	../dataset/jp/test/13_rooms.png	../dataset/jp/test/13_close_wall.png
+../dataset/jp/test/14_input.jpg	../dataset/jp/test/14_wall.png	../dataset/jp/test/14_close.png	../dataset/jp/test/14_rooms.png	../dataset/jp/test/14_close_wall.png
+../dataset/jp/test/15_input.jpg	../dataset/jp/test/15_wall.png	../dataset/jp/test/15_close.png	../dataset/jp/test/15_rooms.png	../dataset/jp/test/15_close_wall.png
+../dataset/jp/test/16_input.jpg	../dataset/jp/test/16_wall.png	../dataset/jp/test/16_close.png	../dataset/jp/test/16_rooms.png	../dataset/jp/test/16_close_wall.png
+../dataset/jp/test/17_input.jpg	../dataset/jp/test/17_wall.png	../dataset/jp/test/17_close.png	../dataset/jp/test/17_rooms.png	../dataset/jp/test/17_close_wall.png
+../dataset/jp/test/18_input.jpg	../dataset/jp/test/18_wall.png	../dataset/jp/test/18_close.png	../dataset/jp/test/18_rooms.png	../dataset/jp/test/18_close_wall.png
+../dataset/jp/test/19_input.jpg	../dataset/jp/test/19_wall.png	../dataset/jp/test/19_close.png	../dataset/jp/test/19_rooms.png	../dataset/jp/test/19_close_wall.png
+../dataset/jp/test/1_input.jpg	../dataset/jp/test/1_wall.png	../dataset/jp/test/1_close.png	../dataset/jp/test/1_rooms.png	../dataset/jp/test/1_close_wall.png
+../dataset/jp/test/20_input.jpg	../dataset/jp/test/20_wall.png	../dataset/jp/test/20_close.png	../dataset/jp/test/20_rooms.png	../dataset/jp/test/20_close_wall.png
+../dataset/jp/test/21_input.jpg	../dataset/jp/test/21_wall.png	../dataset/jp/test/21_close.png	../dataset/jp/test/21_rooms.png	../dataset/jp/test/21_close_wall.png
+../dataset/jp/test/22_input.jpg	../dataset/jp/test/22_wall.png	../dataset/jp/test/22_close.png	../dataset/jp/test/22_rooms.png	../dataset/jp/test/22_close_wall.png
+../dataset/jp/test/23_input.jpg	../dataset/jp/test/23_wall.png	../dataset/jp/test/23_close.png	../dataset/jp/test/23_rooms.png	../dataset/jp/test/23_close_wall.png
+../dataset/jp/test/24_input.jpg	../dataset/jp/test/24_wall.png	../dataset/jp/test/24_close.png	../dataset/jp/test/24_rooms.png	../dataset/jp/test/24_close_wall.png
+../dataset/jp/test/25_input.jpg	../dataset/jp/test/25_wall.png	../dataset/jp/test/25_close.png	../dataset/jp/test/25_rooms.png	../dataset/jp/test/25_close_wall.png
+../dataset/jp/test/26_input.jpg	../dataset/jp/test/26_wall.png	../dataset/jp/test/26_close.png	../dataset/jp/test/26_rooms.png	../dataset/jp/test/26_close_wall.png
+../dataset/jp/test/27_input.jpg	../dataset/jp/test/27_wall.png	../dataset/jp/test/27_close.png	../dataset/jp/test/27_rooms.png	../dataset/jp/test/27_close_wall.png
+../dataset/jp/test/28_input.jpg	../dataset/jp/test/28_wall.png	../dataset/jp/test/28_close.png	../dataset/jp/test/28_rooms.png	../dataset/jp/test/28_close_wall.png
+../dataset/jp/test/29_input.jpg	../dataset/jp/test/29_wall.png	../dataset/jp/test/29_close.png	../dataset/jp/test/29_rooms.png	../dataset/jp/test/29_close_wall.png
+../dataset/jp/test/2_input.jpg	../dataset/jp/test/2_wall.png	../dataset/jp/test/2_close.png	../dataset/jp/test/2_rooms.png	../dataset/jp/test/2_close_wall.png
+../dataset/jp/test/30_input.jpg	../dataset/jp/test/30_wall.png	../dataset/jp/test/30_close.png	../dataset/jp/test/30_rooms.png	../dataset/jp/test/30_close_wall.png
+../dataset/jp/test/31_input.jpg	../dataset/jp/test/31_wall.png	../dataset/jp/test/31_close.png	../dataset/jp/test/31_rooms.png	../dataset/jp/test/31_close_wall.png
+../dataset/jp/test/32_input.jpg	../dataset/jp/test/32_wall.png	../dataset/jp/test/32_close.png	../dataset/jp/test/32_rooms.png	../dataset/jp/test/32_close_wall.png
+../dataset/jp/test/33_input.jpg	../dataset/jp/test/33_wall.png	../dataset/jp/test/33_close.png	../dataset/jp/test/33_rooms.png	../dataset/jp/test/33_close_wall.png
+../dataset/jp/test/34_input.jpg	../dataset/jp/test/34_wall.png	../dataset/jp/test/34_close.png	../dataset/jp/test/34_rooms.png	../dataset/jp/test/34_close_wall.png
+../dataset/jp/test/35_input.jpg	../dataset/jp/test/35_wall.png	../dataset/jp/test/35_close.png	../dataset/jp/test/35_rooms.png	../dataset/jp/test/35_close_wall.png
+../dataset/jp/test/36_input.jpg	../dataset/jp/test/36_wall.png	../dataset/jp/test/36_close.png	../dataset/jp/test/36_rooms.png	../dataset/jp/test/36_close_wall.png
+../dataset/jp/test/37_input.jpg	../dataset/jp/test/37_wall.png	../dataset/jp/test/37_close.png	../dataset/jp/test/37_rooms.png	../dataset/jp/test/37_close_wall.png
+../dataset/jp/test/38_input.jpg	../dataset/jp/test/38_wall.png	../dataset/jp/test/38_close.png	../dataset/jp/test/38_rooms.png	../dataset/jp/test/38_close_wall.png
+../dataset/jp/test/39_input.jpg	../dataset/jp/test/39_wall.png	../dataset/jp/test/39_close.png	../dataset/jp/test/39_rooms.png	../dataset/jp/test/39_close_wall.png
+../dataset/jp/test/3_input.jpg	../dataset/jp/test/3_wall.png	../dataset/jp/test/3_close.png	../dataset/jp/test/3_rooms.png	../dataset/jp/test/3_close_wall.png
+../dataset/jp/test/40_input.jpg	../dataset/jp/test/40_wall.png	../dataset/jp/test/40_close.png	../dataset/jp/test/40_rooms.png	../dataset/jp/test/40_close_wall.png
+../dataset/jp/test/41_input.jpg	../dataset/jp/test/41_wall.png	../dataset/jp/test/41_close.png	../dataset/jp/test/41_rooms.png	../dataset/jp/test/41_close_wall.png
+../dataset/jp/test/42_input.jpg	../dataset/jp/test/42_wall.png	../dataset/jp/test/42_close.png	../dataset/jp/test/42_rooms.png	../dataset/jp/test/42_close_wall.png
+../dataset/jp/test/43_input.jpg	../dataset/jp/test/43_wall.png	../dataset/jp/test/43_close.png	../dataset/jp/test/43_rooms.png	../dataset/jp/test/43_close_wall.png
+../dataset/jp/test/44_input.jpg	../dataset/jp/test/44_wall.png	../dataset/jp/test/44_close.png	../dataset/jp/test/44_rooms.png	../dataset/jp/test/44_close_wall.png
+../dataset/jp/test/45_input.jpg	../dataset/jp/test/45_wall.png	../dataset/jp/test/45_close.png	../dataset/jp/test/45_rooms.png	../dataset/jp/test/45_close_wall.png
+../dataset/jp/test/46_input.jpg	../dataset/jp/test/46_wall.png	../dataset/jp/test/46_close.png	../dataset/jp/test/46_rooms.png	../dataset/jp/test/46_close_wall.png
+../dataset/jp/test/47_input.jpg	../dataset/jp/test/47_wall.png	../dataset/jp/test/47_close.png	../dataset/jp/test/47_rooms.png	../dataset/jp/test/47_close_wall.png
+../dataset/jp/test/48_input.jpg	../dataset/jp/test/48_wall.png	../dataset/jp/test/48_close.png	../dataset/jp/test/48_rooms.png	../dataset/jp/test/48_close_wall.png
+../dataset/jp/test/49_input.jpg	../dataset/jp/test/49_wall.png	../dataset/jp/test/49_close.png	../dataset/jp/test/49_rooms.png	../dataset/jp/test/49_close_wall.png
+../dataset/jp/test/4_input.jpg	../dataset/jp/test/4_wall.png	../dataset/jp/test/4_close.png	../dataset/jp/test/4_rooms.png	../dataset/jp/test/4_close_wall.png
+../dataset/jp/test/50_input.jpg	../dataset/jp/test/50_wall.png	../dataset/jp/test/50_close.png	../dataset/jp/test/50_rooms.png	../dataset/jp/test/50_close_wall.png
+../dataset/jp/test/51_input.jpg	../dataset/jp/test/51_wall.png	../dataset/jp/test/51_close.png	../dataset/jp/test/51_rooms.png	../dataset/jp/test/51_close_wall.png
+../dataset/jp/test/52_input.jpg	../dataset/jp/test/52_wall.png	../dataset/jp/test/52_close.png	../dataset/jp/test/52_rooms.png	../dataset/jp/test/52_close_wall.png
+../dataset/jp/test/53_input.jpg	../dataset/jp/test/53_wall.png	../dataset/jp/test/53_close.png	../dataset/jp/test/53_rooms.png	../dataset/jp/test/53_close_wall.png
+../dataset/jp/test/54_input.jpg	../dataset/jp/test/54_wall.png	../dataset/jp/test/54_close.png	../dataset/jp/test/54_rooms.png	../dataset/jp/test/54_close_wall.png
+../dataset/jp/test/55_input.jpg	../dataset/jp/test/55_wall.png	../dataset/jp/test/55_close.png	../dataset/jp/test/55_rooms.png	../dataset/jp/test/55_close_wall.png
+../dataset/jp/test/56_input.jpg	../dataset/jp/test/56_wall.png	../dataset/jp/test/56_close.png	../dataset/jp/test/56_rooms.png	../dataset/jp/test/56_close_wall.png
+../dataset/jp/test/57_input.jpg	../dataset/jp/test/57_wall.png	../dataset/jp/test/57_close.png	../dataset/jp/test/57_rooms.png	../dataset/jp/test/57_close_wall.png
+../dataset/jp/test/58_input.jpg	../dataset/jp/test/58_wall.png	../dataset/jp/test/58_close.png	../dataset/jp/test/58_rooms.png	../dataset/jp/test/58_close_wall.png
+../dataset/jp/test/59_input.jpg	../dataset/jp/test/59_wall.png	../dataset/jp/test/59_close.png	../dataset/jp/test/59_rooms.png	../dataset/jp/test/59_close_wall.png
+../dataset/jp/test/5_input.jpg	../dataset/jp/test/5_wall.png	../dataset/jp/test/5_close.png	../dataset/jp/test/5_rooms.png	../dataset/jp/test/5_close_wall.png
+../dataset/jp/test/60_input.jpg	../dataset/jp/test/60_wall.png	../dataset/jp/test/60_close.png	../dataset/jp/test/60_rooms.png	../dataset/jp/test/60_close_wall.png
+../dataset/jp/test/61_input.jpg	../dataset/jp/test/61_wall.png	../dataset/jp/test/61_close.png	../dataset/jp/test/61_rooms.png	../dataset/jp/test/61_close_wall.png
+../dataset/jp/test/62_input.jpg	../dataset/jp/test/62_wall.png	../dataset/jp/test/62_close.png	../dataset/jp/test/62_rooms.png	../dataset/jp/test/62_close_wall.png
+../dataset/jp/test/63_input.jpg	../dataset/jp/test/63_wall.png	../dataset/jp/test/63_close.png	../dataset/jp/test/63_rooms.png	../dataset/jp/test/63_close_wall.png
+../dataset/jp/test/64_input.jpg	../dataset/jp/test/64_wall.png	../dataset/jp/test/64_close.png	../dataset/jp/test/64_rooms.png	../dataset/jp/test/64_close_wall.png
+../dataset/jp/test/65_input.jpg	../dataset/jp/test/65_wall.png	../dataset/jp/test/65_close.png	../dataset/jp/test/65_rooms.png	../dataset/jp/test/65_close_wall.png
+../dataset/jp/test/66_input.jpg	../dataset/jp/test/66_wall.png	../dataset/jp/test/66_close.png	../dataset/jp/test/66_rooms.png	../dataset/jp/test/66_close_wall.png
+../dataset/jp/test/67_input.jpg	../dataset/jp/test/67_wall.png	../dataset/jp/test/67_close.png	../dataset/jp/test/67_rooms.png	../dataset/jp/test/67_close_wall.png
+../dataset/jp/test/68_input.jpg	../dataset/jp/test/68_wall.png	../dataset/jp/test/68_close.png	../dataset/jp/test/68_rooms.png	../dataset/jp/test/68_close_wall.png
+../dataset/jp/test/69_input.jpg	../dataset/jp/test/69_wall.png	../dataset/jp/test/69_close.png	../dataset/jp/test/69_rooms.png	../dataset/jp/test/69_close_wall.png
+../dataset/jp/test/6_input.jpg	../dataset/jp/test/6_wall.png	../dataset/jp/test/6_close.png	../dataset/jp/test/6_rooms.png	../dataset/jp/test/6_close_wall.png
+../dataset/jp/test/70_input.jpg	../dataset/jp/test/70_wall.png	../dataset/jp/test/70_close.png	../dataset/jp/test/70_rooms.png	../dataset/jp/test/70_close_wall.png
+../dataset/jp/test/71_input.jpg	../dataset/jp/test/71_wall.png	../dataset/jp/test/71_close.png	../dataset/jp/test/71_rooms.png	../dataset/jp/test/71_close_wall.png
+../dataset/jp/test/72_input.jpg	../dataset/jp/test/72_wall.png	../dataset/jp/test/72_close.png	../dataset/jp/test/72_rooms.png	../dataset/jp/test/72_close_wall.png
+../dataset/jp/test/73_input.jpg	../dataset/jp/test/73_wall.png	../dataset/jp/test/73_close.png	../dataset/jp/test/73_rooms.png	../dataset/jp/test/73_close_wall.png
+../dataset/jp/test/74_input.jpg	../dataset/jp/test/74_wall.png	../dataset/jp/test/74_close.png	../dataset/jp/test/74_rooms.png	../dataset/jp/test/74_close_wall.png
+../dataset/jp/test/75_input.jpg	../dataset/jp/test/75_wall.png	../dataset/jp/test/75_close.png	../dataset/jp/test/75_rooms.png	../dataset/jp/test/75_close_wall.png
+../dataset/jp/test/76_input.jpg	../dataset/jp/test/76_wall.png	../dataset/jp/test/76_close.png	../dataset/jp/test/76_rooms.png	../dataset/jp/test/76_close_wall.png
+../dataset/jp/test/77_input.jpg	../dataset/jp/test/77_wall.png	../dataset/jp/test/77_close.png	../dataset/jp/test/77_rooms.png	../dataset/jp/test/77_close_wall.png
+../dataset/jp/test/78_input.jpg	../dataset/jp/test/78_wall.png	../dataset/jp/test/78_close.png	../dataset/jp/test/78_rooms.png	../dataset/jp/test/78_close_wall.png
+../dataset/jp/test/79_input.jpg	../dataset/jp/test/79_wall.png	../dataset/jp/test/79_close.png	../dataset/jp/test/79_rooms.png	../dataset/jp/test/79_close_wall.png
+../dataset/jp/test/7_input.jpg	../dataset/jp/test/7_wall.png	../dataset/jp/test/7_close.png	../dataset/jp/test/7_rooms.png	../dataset/jp/test/7_close_wall.png
+../dataset/jp/test/80_input.jpg	../dataset/jp/test/80_wall.png	../dataset/jp/test/80_close.png	../dataset/jp/test/80_rooms.png	../dataset/jp/test/80_close_wall.png
+../dataset/jp/test/81_input.jpg	../dataset/jp/test/81_wall.png	../dataset/jp/test/81_close.png	../dataset/jp/test/81_rooms.png	../dataset/jp/test/81_close_wall.png
+../dataset/jp/test/82_input.jpg	../dataset/jp/test/82_wall.png	../dataset/jp/test/82_close.png	../dataset/jp/test/82_rooms.png	../dataset/jp/test/82_close_wall.png
+../dataset/jp/test/83_input.jpg	../dataset/jp/test/83_wall.png	../dataset/jp/test/83_close.png	../dataset/jp/test/83_rooms.png	../dataset/jp/test/83_close_wall.png
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+../dataset/jp/test/85_input.jpg	../dataset/jp/test/85_wall.png	../dataset/jp/test/85_close.png	../dataset/jp/test/85_rooms.png	../dataset/jp/test/85_close_wall.png
+../dataset/jp/test/86_input.jpg	../dataset/jp/test/86_wall.png	../dataset/jp/test/86_close.png	../dataset/jp/test/86_rooms.png	../dataset/jp/test/86_close_wall.png
+../dataset/jp/test/87_input.jpg	../dataset/jp/test/87_wall.png	../dataset/jp/test/87_close.png	../dataset/jp/test/87_rooms.png	../dataset/jp/test/87_close_wall.png
+../dataset/jp/test/88_input.jpg	../dataset/jp/test/88_wall.png	../dataset/jp/test/88_close.png	../dataset/jp/test/88_rooms.png	../dataset/jp/test/88_close_wall.png
+../dataset/jp/test/89_input.jpg	../dataset/jp/test/89_wall.png	../dataset/jp/test/89_close.png	../dataset/jp/test/89_rooms.png	../dataset/jp/test/89_close_wall.png
+../dataset/jp/test/8_input.jpg	../dataset/jp/test/8_wall.png	../dataset/jp/test/8_close.png	../dataset/jp/test/8_rooms.png	../dataset/jp/test/8_close_wall.png
+../dataset/jp/test/90_input.jpg	../dataset/jp/test/90_wall.png	../dataset/jp/test/90_close.png	../dataset/jp/test/90_rooms.png	../dataset/jp/test/90_close_wall.png
+../dataset/jp/test/91_input.jpg	../dataset/jp/test/91_wall.png	../dataset/jp/test/91_close.png	../dataset/jp/test/91_rooms.png	../dataset/jp/test/91_close_wall.png
+../dataset/jp/test/92_input.jpg	../dataset/jp/test/92_wall.png	../dataset/jp/test/92_close.png	../dataset/jp/test/92_rooms.png	../dataset/jp/test/92_close_wall.png
+../dataset/jp/test/93_input.jpg	../dataset/jp/test/93_wall.png	../dataset/jp/test/93_close.png	../dataset/jp/test/93_rooms.png	../dataset/jp/test/93_close_wall.png
+../dataset/jp/test/94_input.jpg	../dataset/jp/test/94_wall.png	../dataset/jp/test/94_close.png	../dataset/jp/test/94_rooms.png	../dataset/jp/test/94_close_wall.png
+../dataset/jp/test/95_input.jpg	../dataset/jp/test/95_wall.png	../dataset/jp/test/95_close.png	../dataset/jp/test/95_rooms.png	../dataset/jp/test/95_close_wall.png
+../dataset/jp/test/96_input.jpg	../dataset/jp/test/96_wall.png	../dataset/jp/test/96_close.png	../dataset/jp/test/96_rooms.png	../dataset/jp/test/96_close_wall.png
+../dataset/jp/test/97_input.jpg	../dataset/jp/test/97_wall.png	../dataset/jp/test/97_close.png	../dataset/jp/test/97_rooms.png	../dataset/jp/test/97_close_wall.png
+../dataset/jp/test/98_input.jpg	../dataset/jp/test/98_wall.png	../dataset/jp/test/98_close.png	../dataset/jp/test/98_rooms.png	../dataset/jp/test/98_close_wall.png
+../dataset/jp/test/99_input.jpg	../dataset/jp/test/99_wall.png	../dataset/jp/test/99_close.png	../dataset/jp/test/99_rooms.png	../dataset/jp/test/99_close_wall.png
+../dataset/jp/test/9_input.jpg	../dataset/jp/test/9_wall.png	../dataset/jp/test/9_close.png	../dataset/jp/test/9_rooms.png	../dataset/jp/test/9_close_wall.png

dataset/r3d_test.txt

@@ -0,0 +1,53 @@
+../dataset/newyork/test/21.jpg	../dataset/newyork/test/21_wall.png	../dataset/newyork/test/21_close.png	../dataset/newyork/test/21_rooms.png	../dataset/newyork/test/21_close_wall.png
+../dataset/newyork/test/30691830.jpg	../dataset/newyork/test/30691830_wall.png	../dataset/newyork/test/30691830_close.png	../dataset/newyork/test/30691830_rooms.png	../dataset/newyork/test/30691830_close_wall.png
+../dataset/newyork/test/31074492.jpg	../dataset/newyork/test/31074492_wall.png	../dataset/newyork/test/31074492_close.png	../dataset/newyork/test/31074492_rooms.png	../dataset/newyork/test/31074492_close_wall.png
+../dataset/newyork/test/31837524.jpg	../dataset/newyork/test/31837524_wall.png	../dataset/newyork/test/31837524_close.png	../dataset/newyork/test/31837524_rooms.png	../dataset/newyork/test/31837524_close_wall.png
+../dataset/newyork/test/31851141.jpg	../dataset/newyork/test/31851141_wall.png	../dataset/newyork/test/31851141_close.png	../dataset/newyork/test/31851141_rooms.png	../dataset/newyork/test/31851141_close_wall.png
+../dataset/newyork/test/31873188.jpg	../dataset/newyork/test/31873188_wall.png	../dataset/newyork/test/31873188_close.png	../dataset/newyork/test/31873188_rooms.png	../dataset/newyork/test/31873188_close_wall.png
+../dataset/newyork/test/31889856.jpg	../dataset/newyork/test/31889856_wall.png	../dataset/newyork/test/31889856_close.png	../dataset/newyork/test/31889856_rooms.png	../dataset/newyork/test/31889856_close_wall.png
+../dataset/newyork/test/43949851.jpg	../dataset/newyork/test/43949851_wall.png	../dataset/newyork/test/43949851_close.png	../dataset/newyork/test/43949851_rooms.png	../dataset/newyork/test/43949851_close_wall.png
+../dataset/newyork/test/44777104.jpg	../dataset/newyork/test/44777104_wall.png	../dataset/newyork/test/44777104_close.png	../dataset/newyork/test/44777104_rooms.png	../dataset/newyork/test/44777104_close_wall.png
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+../dataset/newyork/test/45765448.jpg	../dataset/newyork/test/45765448_wall.png	../dataset/newyork/test/45765448_close.png	../dataset/newyork/test/45765448_rooms.png	../dataset/newyork/test/45765448_close_wall.png
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+../dataset/newyork/test/46543250.jpg	../dataset/newyork/test/46543250_wall.png	../dataset/newyork/test/46543250_close.png	../dataset/newyork/test/46543250_rooms.png	../dataset/newyork/test/46543250_close_wall.png
+../dataset/newyork/test/47464145.jpg	../dataset/newyork/test/47464145_wall.png	../dataset/newyork/test/47464145_close.png	../dataset/newyork/test/47464145_rooms.png	../dataset/newyork/test/47464145_close_wall.png
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+../dataset/newyork/test/47505362.jpg	../dataset/newyork/test/47505362_wall.png	../dataset/newyork/test/47505362_close.png	../dataset/newyork/test/47505362_rooms.png	../dataset/newyork/test/47505362_close_wall.png
+../dataset/newyork/test/47525504.jpg	../dataset/newyork/test/47525504_wall.png	../dataset/newyork/test/47525504_close.png	../dataset/newyork/test/47525504_rooms.png	../dataset/newyork/test/47525504_close_wall.png
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+../dataset/newyork/test/47541860.jpg	../dataset/newyork/test/47541860_wall.png	../dataset/newyork/test/47541860_close.png	../dataset/newyork/test/47541860_rooms.png	../dataset/newyork/test/47541860_close_wall.png
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+../dataset/newyork/test/47541866.jpg	../dataset/newyork/test/47541866_wall.png	../dataset/newyork/test/47541866_close.png	../dataset/newyork/test/47541866_rooms.png	../dataset/newyork/test/47541866_close_wall.png
+../dataset/newyork/test/47542733.jpg	../dataset/newyork/test/47542733_wall.png	../dataset/newyork/test/47542733_close.png	../dataset/newyork/test/47542733_rooms.png	../dataset/newyork/test/47542733_close_wall.png
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+../dataset/newyork/test/47548484.jpg	../dataset/newyork/test/47548484_wall.png	../dataset/newyork/test/47548484_close.png	../dataset/newyork/test/47548484_rooms.png	../dataset/newyork/test/47548484_close_wall.png
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+../dataset/newyork/test/55.jpg	../dataset/newyork/test/55_wall.png	../dataset/newyork/test/55_close.png	../dataset/newyork/test/55_rooms.png	../dataset/newyork/test/55_close_wall.png
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dataset/r3d_train.txt

@@ -0,0 +1,179 @@
+../dataset/newyork/train/10.jpg	../dataset/newyork/train/10_wall.png	../dataset/newyork/train/10_close.png	../dataset/newyork/train/10_rooms.png	../dataset/newyork/train/10_close_wall.png
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+../dataset/newyork/train/2.jpg	../dataset/newyork/train/2_wall.png	../dataset/newyork/train/2_close.png	../dataset/newyork/train/2_rooms.png	../dataset/newyork/train/2_close_wall.png
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+../dataset/newyork/train/31036152.jpg	../dataset/newyork/train/31036152_wall.png	../dataset/newyork/train/31036152_close.png	../dataset/newyork/train/31036152_rooms.png	../dataset/newyork/train/31036152_close_wall.png
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+../dataset/newyork/train/45774964.jpg	../dataset/newyork/train/45774964_wall.png	../dataset/newyork/train/45774964_close.png	../dataset/newyork/train/45774964_rooms.png	../dataset/newyork/train/45774964_close_wall.png
+../dataset/newyork/train/45775138.jpg	../dataset/newyork/train/45775138_wall.png	../dataset/newyork/train/45775138_close.png	../dataset/newyork/train/45775138_rooms.png	../dataset/newyork/train/45775138_close_wall.png
+../dataset/newyork/train/45775222.jpg	../dataset/newyork/train/45775222_wall.png	../dataset/newyork/train/45775222_close.png	../dataset/newyork/train/45775222_rooms.png	../dataset/newyork/train/45775222_close_wall.png
+../dataset/newyork/train/45775225.jpg	../dataset/newyork/train/45775225_wall.png	../dataset/newyork/train/45775225_close.png	../dataset/newyork/train/45775225_rooms.png	../dataset/newyork/train/45775225_close_wall.png
+../dataset/newyork/train/45775354.jpg	../dataset/newyork/train/45775354_wall.png	../dataset/newyork/train/45775354_close.png	../dataset/newyork/train/45775354_rooms.png	../dataset/newyork/train/45775354_close_wall.png
+../dataset/newyork/train/45775501.jpg	../dataset/newyork/train/45775501_wall.png	../dataset/newyork/train/45775501_close.png	../dataset/newyork/train/45775501_rooms.png	../dataset/newyork/train/45775501_close_wall.png
+../dataset/newyork/train/45775504.jpg	../dataset/newyork/train/45775504_wall.png	../dataset/newyork/train/45775504_close.png	../dataset/newyork/train/45775504_rooms.png	../dataset/newyork/train/45775504_close_wall.png
+../dataset/newyork/train/45775894.jpg	../dataset/newyork/train/45775894_wall.png	../dataset/newyork/train/45775894_close.png	../dataset/newyork/train/45775894_rooms.png	../dataset/newyork/train/45775894_close_wall.png
+../dataset/newyork/train/45777601.jpg	../dataset/newyork/train/45777601_wall.png	../dataset/newyork/train/45777601_close.png	../dataset/newyork/train/45777601_rooms.png	../dataset/newyork/train/45777601_close_wall.png
+../dataset/newyork/train/45777694.jpg	../dataset/newyork/train/45777694_wall.png	../dataset/newyork/train/45777694_close.png	../dataset/newyork/train/45777694_rooms.png	../dataset/newyork/train/45777694_close_wall.png
+../dataset/newyork/train/45780106.jpg	../dataset/newyork/train/45780106_wall.png	../dataset/newyork/train/45780106_close.png	../dataset/newyork/train/45780106_rooms.png	../dataset/newyork/train/45780106_close_wall.png
+../dataset/newyork/train/45780376.jpg	../dataset/newyork/train/45780376_wall.png	../dataset/newyork/train/45780376_close.png	../dataset/newyork/train/45780376_rooms.png	../dataset/newyork/train/45780376_close_wall.png
+../dataset/newyork/train/45781483.jpg	../dataset/newyork/train/45781483_wall.png	../dataset/newyork/train/45781483_close.png	../dataset/newyork/train/45781483_rooms.png	../dataset/newyork/train/45781483_close_wall.png
+../dataset/newyork/train/45783298.jpg	../dataset/newyork/train/45783298_wall.png	../dataset/newyork/train/45783298_close.png	../dataset/newyork/train/45783298_rooms.png	../dataset/newyork/train/45783298_close_wall.png
+../dataset/newyork/train/45783466.jpg	../dataset/newyork/train/45783466_wall.png	../dataset/newyork/train/45783466_close.png	../dataset/newyork/train/45783466_rooms.png	../dataset/newyork/train/45783466_close_wall.png
+../dataset/newyork/train/45.jpg	../dataset/newyork/train/45_wall.png	../dataset/newyork/train/45_close.png	../dataset/newyork/train/45_rooms.png	../dataset/newyork/train/45_close_wall.png
+../dataset/newyork/train/46452431.jpg	../dataset/newyork/train/46452431_wall.png	../dataset/newyork/train/46452431_close.png	../dataset/newyork/train/46452431_rooms.png	../dataset/newyork/train/46452431_close_wall.png
+../dataset/newyork/train/46678955.jpg	../dataset/newyork/train/46678955_wall.png	../dataset/newyork/train/46678955_close.png	../dataset/newyork/train/46678955_rooms.png	../dataset/newyork/train/46678955_close_wall.png
+../dataset/newyork/train/46781618.jpg	../dataset/newyork/train/46781618_wall.png	../dataset/newyork/train/46781618_close.png	../dataset/newyork/train/46781618_rooms.png	../dataset/newyork/train/46781618_close_wall.png
+../dataset/newyork/train/46807061.jpg	../dataset/newyork/train/46807061_wall.png	../dataset/newyork/train/46807061_close.png	../dataset/newyork/train/46807061_rooms.png	../dataset/newyork/train/46807061_close_wall.png
+../dataset/newyork/train/46.jpg	../dataset/newyork/train/46_wall.png	../dataset/newyork/train/46_close.png	../dataset/newyork/train/46_rooms.png	../dataset/newyork/train/46_close_wall.png
+../dataset/newyork/train/47073524.jpg	../dataset/newyork/train/47073524_wall.png	../dataset/newyork/train/47073524_close.png	../dataset/newyork/train/47073524_rooms.png	../dataset/newyork/train/47073524_close_wall.png
+../dataset/newyork/train/47185109.jpg	../dataset/newyork/train/47185109_wall.png	../dataset/newyork/train/47185109_close.png	../dataset/newyork/train/47185109_rooms.png	../dataset/newyork/train/47185109_close_wall.png
+../dataset/newyork/train/47236967.jpg	../dataset/newyork/train/47236967_wall.png	../dataset/newyork/train/47236967_close.png	../dataset/newyork/train/47236967_rooms.png	../dataset/newyork/train/47236967_close_wall.png
+../dataset/newyork/train/47325578.jpg	../dataset/newyork/train/47325578_wall.png	../dataset/newyork/train/47325578_close.png	../dataset/newyork/train/47325578_rooms.png	../dataset/newyork/train/47325578_close_wall.png
+../dataset/newyork/train/47360870.jpg	../dataset/newyork/train/47360870_wall.png	../dataset/newyork/train/47360870_close.png	../dataset/newyork/train/47360870_rooms.png	../dataset/newyork/train/47360870_close_wall.png
+../dataset/newyork/train/47429369.jpg	../dataset/newyork/train/47429369_wall.png	../dataset/newyork/train/47429369_close.png	../dataset/newyork/train/47429369_rooms.png	../dataset/newyork/train/47429369_close_wall.png
+../dataset/newyork/train/47464136.jpg	../dataset/newyork/train/47464136_wall.png	../dataset/newyork/train/47464136_close.png	../dataset/newyork/train/47464136_rooms.png	../dataset/newyork/train/47464136_close_wall.png
+../dataset/newyork/train/47464142.jpg	../dataset/newyork/train/47464142_wall.png	../dataset/newyork/train/47464142_close.png	../dataset/newyork/train/47464142_rooms.png	../dataset/newyork/train/47464142_close_wall.png
+../dataset/newyork/train/47464151.jpg	../dataset/newyork/train/47464151_wall.png	../dataset/newyork/train/47464151_close.png	../dataset/newyork/train/47464151_rooms.png	../dataset/newyork/train/47464151_close_wall.png
+../dataset/newyork/train/47465963.jpg	../dataset/newyork/train/47465963_wall.png	../dataset/newyork/train/47465963_close.png	../dataset/newyork/train/47465963_rooms.png	../dataset/newyork/train/47465963_close_wall.png
+../dataset/newyork/train/47484836.jpg	../dataset/newyork/train/47484836_wall.png	../dataset/newyork/train/47484836_close.png	../dataset/newyork/train/47484836_rooms.png	../dataset/newyork/train/47484836_close_wall.png
+../dataset/newyork/train/47489621.jpg	../dataset/newyork/train/47489621_wall.png	../dataset/newyork/train/47489621_close.png	../dataset/newyork/train/47489621_rooms.png	../dataset/newyork/train/47489621_close_wall.png
+../dataset/newyork/train/47489648.jpg	../dataset/newyork/train/47489648_wall.png	../dataset/newyork/train/47489648_close.png	../dataset/newyork/train/47489648_rooms.png	../dataset/newyork/train/47489648_close_wall.png
+../dataset/newyork/train/47490062.jpg	../dataset/newyork/train/47490062_wall.png	../dataset/newyork/train/47490062_close.png	../dataset/newyork/train/47490062_rooms.png	../dataset/newyork/train/47490062_close_wall.png
+../dataset/newyork/train/47492936.jpg	../dataset/newyork/train/47492936_wall.png	../dataset/newyork/train/47492936_close.png	../dataset/newyork/train/47492936_rooms.png	../dataset/newyork/train/47492936_close_wall.png
+../dataset/newyork/train/47499269.jpg	../dataset/newyork/train/47499269_wall.png	../dataset/newyork/train/47499269_close.png	../dataset/newyork/train/47499269_rooms.png	../dataset/newyork/train/47499269_close_wall.png
+../dataset/newyork/train/47499620.jpg	../dataset/newyork/train/47499620_wall.png	../dataset/newyork/train/47499620_close.png	../dataset/newyork/train/47499620_rooms.png	../dataset/newyork/train/47499620_close_wall.png
+../dataset/newyork/train/47503913.jpg	../dataset/newyork/train/47503913_wall.png	../dataset/newyork/train/47503913_close.png	../dataset/newyork/train/47503913_rooms.png	../dataset/newyork/train/47503913_close_wall.png
+../dataset/newyork/train/47505359.jpg	../dataset/newyork/train/47505359_wall.png	../dataset/newyork/train/47505359_close.png	../dataset/newyork/train/47505359_rooms.png	../dataset/newyork/train/47505359_close_wall.png
+../dataset/newyork/train/47508827.jpg	../dataset/newyork/train/47508827_wall.png	../dataset/newyork/train/47508827_close.png	../dataset/newyork/train/47508827_rooms.png	../dataset/newyork/train/47508827_close_wall.png
+../dataset/newyork/train/47514899.jpg	../dataset/newyork/train/47514899_wall.png	../dataset/newyork/train/47514899_close.png	../dataset/newyork/train/47514899_rooms.png	../dataset/newyork/train/47514899_close_wall.png
+../dataset/newyork/train/47514920.jpg	../dataset/newyork/train/47514920_wall.png	../dataset/newyork/train/47514920_close.png	../dataset/newyork/train/47514920_rooms.png	../dataset/newyork/train/47514920_close_wall.png
+../dataset/newyork/train/47534687.jpg	../dataset/newyork/train/47534687_wall.png	../dataset/newyork/train/47534687_close.png	../dataset/newyork/train/47534687_rooms.png	../dataset/newyork/train/47534687_close_wall.png
+../dataset/newyork/train/4.jpg	../dataset/newyork/train/4_wall.png	../dataset/newyork/train/4_close.png	../dataset/newyork/train/4_rooms.png	../dataset/newyork/train/4_close_wall.png
+../dataset/newyork/train/50.jpg	../dataset/newyork/train/50_wall.png	../dataset/newyork/train/50_close.png	../dataset/newyork/train/50_rooms.png	../dataset/newyork/train/50_close_wall.png
+../dataset/newyork/train/52.jpg	../dataset/newyork/train/52_wall.png	../dataset/newyork/train/52_close.png	../dataset/newyork/train/52_rooms.png	../dataset/newyork/train/52_close_wall.png
+../dataset/newyork/train/57.jpg	../dataset/newyork/train/57_wall.png	../dataset/newyork/train/57_close.png	../dataset/newyork/train/57_rooms.png	../dataset/newyork/train/57_close_wall.png
+../dataset/newyork/train/7.jpg	../dataset/newyork/train/7_wall.png	../dataset/newyork/train/7_close.png	../dataset/newyork/train/7_rooms.png	../dataset/newyork/train/7_close_wall.png

deepfloorplan_inference.py

@@ -1,53 +1,56 @@
-import os
-import numpy as np
-import tensorflow.compat.v1 as tf
-tf.disable_v2_behavior()
-from PIL import Image
-import imageio
-from net import Network
-from utils.rgb_ind_convertor import ind2rgb, floorplan_fuse_map
-
-class DeepFloorPlanModel:
-    def __init__(self, model_dir='pretrained', input_size=(512, 512)):
-        self.input_size = input_size
-        self.model_dir = model_dir
-        self._build_graph()
-        self._load_weights()
-
-    def _build_graph(self):
-        tf.reset_default_graph()
-        self.sess = tf.Session()
-        self.x = tf.placeholder(shape=[1, self.input_size[0], self.input_size[1], 3], dtype=tf.float32, name='inputs')
-        self.network = Network()
-        logits1, logits2 = self.network.forward(self.x, init_with_pretrain_vgg=False)
-        self.rooms = self.network.convert_one_hot_to_image(logits1, act='softmax', dtype='int')
-        self.close_walls = self.network.convert_one_hot_to_image(logits2, act='softmax', dtype='int')
-        self.sess.run(tf.global_variables_initializer())
-        self.sess.run(tf.local_variables_initializer())
-        self.saver = tf.train.Saver()
-
-    def _load_weights(self):
-        ckpt = tf.train.latest_checkpoint(self.model_dir)
-        if ckpt is None:
-            raise FileNotFoundError(f"No checkpoint found in {self.model_dir}")
-        self.saver.restore(self.sess, ckpt)
-
-    def predict(self, image):
-        if isinstance(image, Image.Image):
-            image = np.array(image)
-        if image.shape[-1] == 4:
-            image = image[..., :3]
-        im_resized = np.array(Image.fromarray(image).resize(self.input_size, Image.BICUBIC)) / 255.0
-        im_resized = im_resized.astype(np.float32)
-        im_resized = np.reshape(im_resized, (1, self.input_size[0], self.input_size[1], 3))
-        out1, out2 = self.sess.run([self.rooms, self.close_walls], feed_dict={self.x: im_resized})
-        out1 = np.squeeze(out1)
-        out2 = np.squeeze(out2)
-        out1[out2==2] = 10
-        out1[out2==1] = 9
-        out_rgb = ind2rgb(out1, color_map=floorplan_fuse_map)
-        out_rgb = out_rgb.astype(np.uint8)
-        return out_rgb
-
-    def close(self):
-        self.sess.close()
+import os
+import numpy as np
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
+from PIL import Image
+import imageio
+from net import Network
+from utils.rgb_ind_convertor import ind2rgb, floorplan_fuse_map
+
+class DeepFloorPlanModel:
+    def __init__(self, model_dir='pretrained', input_size=(512, 512)):
+        self.input_size = input_size
+        self.model_dir = model_dir
+        self._build_graph()
+        self._load_weights()
+
+    def _build_graph(self):
+        tf.compat.v1.reset_default_graph()
+        self.sess = tf.compat.v1.Session()
+        self.x = tf.compat.v1.placeholder(shape=[1, self.input_size[0], self.input_size[1], 3], dtype=tf.float32, name='inputs')
+        self.network = Network()
+        logits1, logits2 = self.network.forward(self.x, init_with_pretrain_vgg=False)
+        self.rooms = self.network.convert_one_hot_to_image(logits1, act='softmax', dtype='int')
+        self.close_walls = self.network.convert_one_hot_to_image(logits2, act='softmax', dtype='int')
+        self.sess.run(tf.compat.v1.global_variables_initializer())
+        self.sess.run(tf.compat.v1.local_variables_initializer())
+        self.saver = tf.compat.v1.train.Saver()
+
+    def _load_weights(self):
+        ckpt = tf.train.latest_checkpoint(self.model_dir)
+        if ckpt is None:
+            raise FileNotFoundError(f"No checkpoint found in {self.model_dir}")
+        self.saver.restore(self.sess, ckpt)
+
+    def predict(self, image):
+        # Accepts a numpy array or PIL image, returns a numpy array (segmentation mask)
+        if isinstance(image, Image.Image):
+            image = np.array(image)
+        if image.shape[-1] == 4:
+            image = image[..., :3]
+        im_resized = np.array(Image.fromarray(image).resize(self.input_size, Image.BICUBIC)) / 255.0
+        im_resized = im_resized.astype(np.float32)
+        im_resized = np.reshape(im_resized, (1, self.input_size[0], self.input_size[1], 3))
+        out1, out2 = self.sess.run([self.rooms, self.close_walls], feed_dict={self.x: im_resized})
+        out1 = np.squeeze(out1)
+        out2 = np.squeeze(out2)
+        # Merge logic: set out1 pixels to 9/10 where out2==1/2
+        out1[out2==2] = 10
+        out1[out2==1] = 9
+        # Convert to RGB for visualization
+        out_rgb = ind2rgb(out1, color_map=floorplan_fuse_map)
+        out_rgb = out_rgb.astype(np.uint8)
+        return out_rgb
+
+    def close(self):
+        self.sess.close() 

demo.py

@@ -1,7 +1,8 @@
 import os
 import argparse
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
 
 import imageio
 from PIL import Image

main.py

@@ -4,7 +4,8 @@ import os
 import time
 import random
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
 import imageio
 from PIL import Image
 

net.py

@@ -1,5 +1,6 @@
 import numpy as np
-import tensorflow as tf # using tf 1.10.1
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior() # using tf 1.10.1
 
 from tensorflow.contrib.slim.nets import vgg
 

pretrained/download_links.txt

@@ -0,0 +1 @@
+download link: https://mycuhk-my.sharepoint.com/:f:/g/personal/1155052510_link_cuhk_edu_hk/EgyJhisy04hNnxKncWl5zksBf9zDKDpMJ7c0V-q53_pxuA?e=P0BjZd

requirements.txt

@@ -1,4 +1,4 @@
-tensorflow==1.15.5
+tensorflow==2.8.0
 numpy>=1.18.0
 Pillow>=8.0.0
 imageio>=2.9.0

utils/create_tfrecord.py

@@ -0,0 +1,65 @@
+"""
+Please prepare the raw image datas save to one folder, 
+makesure the path is match to the train_file/test_file.
+"""
+
+from tf_record import *
+import imageio
+from PIL import Image
+
+train_file = '../dataset/r2v_train.txt'
+test_file = '../dataset/r2v_test.txt'
+
+# debug
+if __name__ == '__main__':
+	# write to TFRecord
+	train_paths = open(train_file, 'r').read().splitlines()
+	# test_paths = open(test_file, 'r').read().splitlines()
+
+	# write_record(train_paths, name='../dataset/jp_train.tfrecords')
+	# write_record(test_paths, name='../dataset/newyork_test.tfrecords')
+
+	# write_seg_record(train_paths, name='../dataset/jp_seg_train.tfrecords')
+	# write_seg_record(train_paths, name='../dataset/newyork_seg_train.tfrecords')
+
+	write_bd_rm_record(train_paths, name='../dataset/jp_train.tfrecords')
+	# write_bd_rm_record(train_paths, name='../dataset/all_train3.tfrecords')
+
+	# read from TFRecord
+	# loader_list = read_record('../dataset/jp_train.tfrecords')
+	# loader_list = read_seg_record('../dataset/jp_seg_train.tfrecords')
+
+	# loader_list = read_bd_rm_record('../dataset/newyork_bd_rm_train.tfrecords')
+	# loader_list = read_bd_rm_record('../dataset/jp_bd_rm_train.tfrecords')
+
+	# images = loader_list['images']
+	# bd_ind = loader_list['label_boundaries']
+	# rm_ind = loader_list['label_rooms']
+
+	# with tf.Session() as sess:
+	# 	# init all variables in graph
+	# 	sess.run(tf.group(tf.global_variables_initializer(),
+	# 					tf.local_variables_initializer()))
+		
+	# 	coord = tf.train.Coordinator()
+	# 	threads = tf.train.start_queue_runners(sess=sess, coord=coord)
+
+	# 	image, bd, rm = sess.run([images, bd_ind, rm_ind])
+
+	# 	print 'sess run image shape = ',image.shape
+	# 	print 'sess run wall shape = ', bd.shape
+	# 	print 'sess run room shape =', rm.shape
+
+	# 	bd = np.argmax(np.squeeze(bd), axis=-1)
+	# 	rm = np.argmax(np.squeeze(rm), axis=-1)
+	# 	plt.subplot(231)
+	# 	plt.imshow(np.squeeze(image))
+	# 	plt.subplot(233)
+	# 	plt.imshow(bd)
+	# 	plt.subplot(234)
+	# 	plt.imshow(rm)
+	# 	plt.show()
+
+	# 	coord.request_stop()
+	# 	coord.join(threads)
+	# 	sess.close()

utils/rgb_ind_convertor.py

@@ -0,0 +1,79 @@
+import numpy as np
+from PIL import Image
+ 
+# use for index 2 rgb
+floorplan_room_map = {
+	0: [  0,  0,  0], # background
+	1: [192,192,224], # closet
+	2: [192,255,255], # bathroom/washroom
+	3: [224,255,192], # livingroom/kitchen/diningroom
+	4: [255,224,128], # bedroom
+	5: [255,160, 96], # hall
+	6: [255,224,224], # balcony
+	7: [224,224,224], # not used
+	8: [224,224,128]  # not used
+}
+
+# boundary label
+floorplan_boundary_map = {
+	0: [  0,  0,  0], # background
+	1: [255,60,128],  # opening (door&window)
+	2: [255,255,255]  # wall line	
+}
+
+# boundary label for presentation
+floorplan_boundary_map_figure = {
+	0: [255,255,255], # background
+	1: [255, 60,128],  # opening (door&window)
+	2: [  0,  0,  0]  # wall line	
+}
+
+# merge all label into one multi-class label
+floorplan_fuse_map = {
+	0: [  0,  0,  0], # background
+	1: [192,192,224], # closet
+	2: [192,255,255], # batchroom/washroom
+	3: [224,255,192], # livingroom/kitchen/dining room
+	4: [255,224,128], # bedroom
+	5: [255,160, 96], # hall
+	6: [255,224,224], # balcony
+	7: [224,224,224], # not used
+	8: [224,224,128], # not used
+	9: [255,60,128],  # extra label for opening (door&window)
+	10: [255,255,255]  # extra label for wall line
+}
+
+# invert the color of wall line and background for presentation
+floorplan_fuse_map_figure = {
+	0: [255,255,255], # background
+	1: [192,192,224], # closet
+	2: [192,255,255], # batchroom/washroom
+	3: [224,255,192], # livingroom/kitchen/dining room
+	4: [255,224,128], # bedroom
+	5: [255,160, 96], # hall
+	6: [255,224,224], # balcony
+	7: [224,224,224], # not used
+	8: [224,224,128], # not used
+	9: [255,60,128],  # extra label for opening (door&window)
+	10: [ 0, 0,  0]  # extra label for wall line
+}
+
+def rgb2ind(im, color_map=floorplan_room_map):
+	ind = np.zeros((im.shape[0], im.shape[1]))
+
+	for i, rgb in color_map.items():
+		ind[(im==rgb).all(2)] = i
+
+	# return ind.astype(int) # int => int64
+	return ind.astype(np.uint8) # force to uint8
+
+def ind2rgb(ind_im, color_map=floorplan_room_map):
+	rgb_im = np.zeros((ind_im.shape[0], ind_im.shape[1], 3))
+
+	for i, rgb in color_map.items():
+		rgb_im[(ind_im==i)] = rgb
+
+	return rgb_im
+
+def unscale_imsave(path, im, cmin=0, cmax=255):
+	Image.fromarray(im, 'L').save(path)

utils/tf_record.py

@@ -0,0 +1,358 @@
+import numpy as np
+
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
+
+import imageio
+from PIL import Image
+from matplotlib import pyplot as plt
+from rgb_ind_convertor import *
+
+import os
+import sys
+import glob 
+import time
+
+def load_raw_images(path):
+	paths = path.split('\t')
+
+	image = imageio.imread(paths[0], mode='RGB')
+	wall  = imageio.imread(paths[1], mode='L')
+	close = imageio.imread(paths[2], mode='L')
+	room  = imageio.imread(paths[3], mode='RGB')
+	close_wall = imageio.imread(paths[4], mode='L')
+
+	# NOTE: imresize will rescale the image to range [0, 255], also cast data into uint8 or uint32
+	image = PIL.Image.fromarray(image).resize((512, 512), Image.BICUBIC)
+	wall = PIL.Image.fromarray(wall).resize((512, 512), Image.BICUBIC)
+	close = PIL.Image.fromarray(close).resize((512, 512), Image.BICUBIC)
+	close_wall = PIL.Image.fromarray(close_wall).resize((512, 512), Image.BICUBIC)
+	room = PIL.Image.fromarray(room).resize((512, 512), Image.BICUBIC)
+
+	room_ind = rgb2ind(room)
+
+	# make sure the dtype is uint8
+	image = np.array(image).astype(np.uint8)
+	wall = np.array(wall).astype(np.uint8)
+	close = np.array(close).astype(np.uint8)
+	close_wall = np.array(close_wall).astype(np.uint8)
+	room_ind = room_ind.astype(np.uint8)
+
+	# debug
+	# plt.subplot(231)
+	# plt.imshow(image)
+	# plt.subplot(233)
+	# plt.imshow(wall, cmap='gray')
+	# plt.subplot(234)
+	# plt.imshow(close, cmap='gray')
+	# plt.subplot(235)
+	# plt.imshow(room_ind)
+	# plt.subplot(236)
+	# plt.imshow(close_wall, cmap='gray')
+	# plt.show()
+
+	return image, wall, close, room_ind, close_wall
+
+def _int64_feature(value):
+	return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
+
+def _bytes_feature(value):
+	return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
+
+def write_record(paths, name='dataset.tfrecords'):
+	writer = tf.python_io.TFRecordWriter(name)
+	
+	for i in range(len(paths)):
+		# Load the image
+		image, wall, close, room_ind, close_wall = load_raw_images(paths[i])
+
+		# Create a feature
+		feature = {'image': _bytes_feature(tf.compat.as_bytes(image.tostring())),
+					'wall': _bytes_feature(tf.compat.as_bytes(wall.tostring())),
+					'close': _bytes_feature(tf.compat.as_bytes(close.tostring())),
+					'room': _bytes_feature(tf.compat.as_bytes(room_ind.tostring())),
+					'close_wall': _bytes_feature(tf.compat.as_bytes(close_wall.tostring()))}
+		
+		# Create an example protocol buffer
+		example = tf.train.Example(features=tf.train.Features(feature=feature))
+    
+		# Serialize to string and write on the file
+		writer.write(example.SerializeToString())
+    
+	writer.close()
+
+def read_record(data_path, batch_size=1, size=512):
+	feature = {'image': tf.FixedLenFeature(shape=(), dtype=tf.string),
+				'wall': tf.FixedLenFeature(shape=(), dtype=tf.string),
+				'close': tf.FixedLenFeature(shape=(), dtype=tf.string),
+				'room': tf.FixedLenFeature(shape=(), dtype=tf.string),
+				'close_wall': tf.FixedLenFeature(shape=(), dtype=tf.string)}
+
+	# Create a list of filenames and pass it to a queue
+	filename_queue = tf.train.string_input_producer([data_path], num_epochs=None, shuffle=False, capacity=batch_size*128)
+	
+	# Define a reader and read the next record
+	reader = tf.TFRecordReader()
+	_, serialized_example = reader.read(filename_queue)
+
+	# Decode the record read by the reader
+	features = tf.parse_single_example(serialized_example, features=feature)
+
+	# Convert the image data from string back to the numbers
+	image = tf.decode_raw(features['image'], tf.uint8)
+	wall = tf.decode_raw(features['wall'], tf.uint8)
+	close = tf.decode_raw(features['close'], tf.uint8)
+	room = tf.decode_raw(features['room'], tf.uint8)
+	close_wall = tf.decode_raw(features['close_wall'], tf.uint8)
+
+	# Cast data
+	image = tf.cast(image, dtype=tf.float32)
+	wall = tf.cast(wall, dtype=tf.float32)
+	close = tf.cast(close, dtype=tf.float32)
+	# room = tf.cast(room, dtype=tf.float32)
+	close_wall = tf.cast(close_wall, dtype=tf.float32)
+
+	# Reshape image data into the original shape
+	image = tf.reshape(image, [size, size, 3])
+	wall = tf.reshape(wall, [size, size, 1])
+	close = tf.reshape(close, [size, size, 1])
+	room = tf.reshape(room, [size, size])
+	close_wall = tf.reshape(close_wall, [size, size, 1])
+
+
+	# Any preprocessing here ...
+	# normalize 
+	image = tf.divide(image, tf.constant(255.0))
+	wall = tf.divide(wall, tf.constant(255.0))
+	close = tf.divide(close, tf.constant(255.0))
+	close_wall = tf.divide(close_wall, tf.constant(255.0))
+
+	# Genereate one hot room label
+	room_one_hot = tf.one_hot(room, 9, axis=-1)
+
+	# Creates batches by randomly shuffling tensors
+	images, walls, closes, rooms, close_walls = tf.train.shuffle_batch([image, wall, close, room_one_hot, close_wall], 
+						batch_size=batch_size, capacity=batch_size*128, num_threads=1, min_after_dequeue=batch_size*32)	
+
+	# images, walls = tf.train.shuffle_batch([image, wall], 
+						# batch_size=batch_size, capacity=batch_size*128, num_threads=1, min_after_dequeue=batch_size*32)	
+
+	return {'images': images, 'walls': walls, 'closes': closes, 'rooms': rooms, 'close_walls': close_walls}
+	# return {'images': images, 'walls': walls}
+
+# ------------------------------------------------------------------------------------------------------------------------------------- *
+# Following are only for segmentation task, merge all label into one 
+
+def load_seg_raw_images(path):
+	paths = path.split('\t')
+
+	image = imageio.imread(paths[0], mode='RGB')
+	close = imageio.imread(paths[2], mode='L')
+	room  = imageio.imread(paths[3], mode='RGB')
+	close_wall = imageio.imread(paths[4], mode='L')
+
+	# NOTE: imresize will rescale the image to range [0, 255], also cast data into uint8 or uint32
+	image = PIL.Image.fromarray(image).resize((512, 512), Image.BICUBIC)
+	close = PIL.Image.fromarray(close).resize((512, 512), Image.BICUBIC) / 255
+	close_wall = PIL.Image.fromarray(close_wall).resize((512, 512), Image.BICUBIC) / 255
+	room = PIL.Image.fromarray(room).resize((512, 512), Image.BICUBIC)
+
+	room_ind = rgb2ind(room)
+
+	# merge result
+	d_ind = (close>0.5).astype(np.uint8)
+	cw_ind = (close_wall>0.5).astype(np.uint8)
+	room_ind[cw_ind==1] = 10
+	room_ind[d_ind==1] = 9
+
+	# make sure the dtype is uint8
+	image = np.array(image).astype(np.uint8)
+	room_ind = room_ind.astype(np.uint8)
+
+	# debug
+	# merge = ind2rgb(room_ind, color_map=floorplan_fuse_map)
+	# plt.subplot(131)
+	# plt.imshow(image)
+	# plt.subplot(132)
+	# plt.imshow(room_ind)
+	# plt.subplot(133)
+	# plt.imshow(merge/256.)
+	# plt.show()
+
+	return image, room_ind
+
+def write_seg_record(paths, name='dataset.tfrecords'):
+	writer = tf.python_io.TFRecordWriter(name)
+	
+	for i in range(len(paths)):
+		# Load the image
+		image, room_ind = load_seg_raw_images(paths[i])
+
+		# Create a feature
+		feature = {'image': _bytes_feature(tf.compat.as_bytes(image.tostring())),
+					'label': _bytes_feature(tf.compat.as_bytes(room_ind.tostring()))}
+		
+		# Create an example protocol buffer
+		example = tf.train.Example(features=tf.train.Features(feature=feature))
+    
+		# Serialize to string and write on the file
+		writer.write(example.SerializeToString())
+    
+	writer.close()
+
+def read_seg_record(data_path, batch_size=1, size=512):
+	feature = {'image': tf.FixedLenFeature(shape=(), dtype=tf.string),
+				'label': tf.FixedLenFeature(shape=(), dtype=tf.string)}
+
+	# Create a list of filenames and pass it to a queue
+	filename_queue = tf.train.string_input_producer([data_path], num_epochs=None, shuffle=False, capacity=batch_size*128)
+	
+	# Define a reader and read the next record
+	reader = tf.TFRecordReader()
+	_, serialized_example = reader.read(filename_queue)
+
+	# Decode the record read by the reader
+	features = tf.parse_single_example(serialized_example, features=feature)
+
+	# Convert the image data from string back to the numbers
+	image = tf.decode_raw(features['image'], tf.uint8)
+	label = tf.decode_raw(features['label'], tf.uint8)
+
+	# Cast data
+	image = tf.cast(image, dtype=tf.float32)
+
+	# Reshape image data into the original shape
+	image = tf.reshape(image, [size, size, 3])
+	label = tf.reshape(label, [size, size])
+
+
+	# Any preprocessing here ...
+	# normalize 
+	image = tf.divide(image, tf.constant(255.0))
+
+	# Genereate one hot room label
+	label_one_hot = tf.one_hot(label, 11, axis=-1)
+
+	# Creates batches by randomly shuffling tensors
+	images, labels = tf.train.shuffle_batch([image, label_one_hot], 
+						batch_size=batch_size, capacity=batch_size*128, num_threads=1, min_after_dequeue=batch_size*32)	
+
+	# images, walls = tf.train.shuffle_batch([image, wall], 
+						# batch_size=batch_size, capacity=batch_size*128, num_threads=1, min_after_dequeue=batch_size*32)	
+
+	return {'images': images, 'labels': labels}
+
+# --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- *
+# ------------------------------------------------------------------------------------------------------------------------------------- *
+# Following are only for multi-task network. Two labels(boundary and room.)
+
+def load_bd_rm_images(path):
+	paths = path.split('\t')
+
+	image = imageio.imread(paths[0], mode='RGB')
+	close = imageio.imread(paths[2], mode='L')
+	room  = imageio.imread(paths[3], mode='RGB')
+	close_wall = imageio.imread(paths[4], mode='L')
+
+	# NOTE: imresize will rescale the image to range [0, 255], also cast data into uint8 or uint32
+	image = PIL.Image.fromarray(image).resize((512, 512), Image.BICUBIC)
+	close = PIL.Image.fromarray(close).resize((512, 512), Image.BICUBIC) / 255.
+	close_wall = PIL.Image.fromarray(close_wall).resize((512, 512), Image.BICUBIC) / 255.
+	room = PIL.Image.fromarray(room).resize((512, 512), Image.BICUBIC)
+
+	room_ind = rgb2ind(room)
+
+	# merge result
+	d_ind = (close>0.5).astype(np.uint8)
+	cw_ind = (close_wall>0.5).astype(np.uint8)
+
+	cw_ind[cw_ind==1] = 2
+	cw_ind[d_ind==1] = 1
+
+	# make sure the dtype is uint8
+	image = np.array(image).astype(np.uint8)
+	room_ind = room_ind.astype(np.uint8)
+	cw_ind = cw_ind.astype(np.uint8)
+
+	# debugging
+	# merge = ind2rgb(room_ind, color_map=floorplan_fuse_map)
+	# rm = ind2rgb(room_ind)
+	# bd = ind2rgb(cw_ind, color_map=floorplan_boundary_map)
+	# plt.subplot(131)
+	# plt.imshow(image)
+	# plt.subplot(132)
+	# plt.imshow(rm/256.)
+	# plt.subplot(133)
+	# plt.imshow(bd/256.)
+	# plt.show()
+
+	return image, cw_ind, room_ind, d_ind
+
+def write_bd_rm_record(paths, name='dataset.tfrecords'):
+	writer = tf.python_io.TFRecordWriter(name)
+	
+	for i in range(len(paths)):
+		# Load the image
+		image, cw_ind, room_ind, d_ind = load_bd_rm_images(paths[i])
+
+		# Create a feature
+		feature = {'image': _bytes_feature(tf.compat.as_bytes(image.tostring())),
+					'boundary': _bytes_feature(tf.compat.as_bytes(cw_ind.tostring())),
+					'room': _bytes_feature(tf.compat.as_bytes(room_ind.tostring())),
+					'door': _bytes_feature(tf.compat.as_bytes(d_ind.tostring()))}
+		
+		# Create an example protocol buffer
+		example = tf.train.Example(features=tf.train.Features(feature=feature))
+    
+		# Serialize to string and write on the file
+		writer.write(example.SerializeToString())
+    
+	writer.close()
+
+def read_bd_rm_record(data_path, batch_size=1, size=512):
+	feature = {'image': tf.FixedLenFeature(shape=(), dtype=tf.string),
+				'boundary': tf.FixedLenFeature(shape=(), dtype=tf.string),
+				'room': tf.FixedLenFeature(shape=(), dtype=tf.string),
+				'door': tf.FixedLenFeature(shape=(), dtype=tf.string)}
+
+	# Create a list of filenames and pass it to a queue
+	filename_queue = tf.train.string_input_producer([data_path], num_epochs=None, shuffle=False, capacity=batch_size*128)
+	
+	# Define a reader and read the next record
+	reader = tf.TFRecordReader()
+	_, serialized_example = reader.read(filename_queue)
+
+	# Decode the record read by the reader
+	features = tf.parse_single_example(serialized_example, features=feature)
+
+	# Convert the image data from string back to the numbers
+	image = tf.decode_raw(features['image'], tf.uint8)
+	boundary = tf.decode_raw(features['boundary'], tf.uint8)
+	room = tf.decode_raw(features['room'], tf.uint8)
+	door = tf.decode_raw(features['door'], tf.uint8)
+
+	# Cast data
+	image = tf.cast(image, dtype=tf.float32)
+
+	# Reshape image data into the original shape
+	image = tf.reshape(image, [size, size, 3])
+	boundary = tf.reshape(boundary, [size, size])
+	room = tf.reshape(room, [size, size])
+	door = tf.reshape(door, [size, size])
+
+	# Any preprocessing here ...
+	# normalize 
+	image = tf.divide(image, tf.constant(255.0))
+
+	# Genereate one hot room label
+	label_boundary = tf.one_hot(boundary, 3, axis=-1)
+	label_room = tf.one_hot(room, 9, axis=-1)
+
+	# Creates batches by randomly shuffling tensors
+	images, label_boundaries, label_rooms, label_doors = tf.train.shuffle_batch([image, label_boundary, label_room, door], 
+						batch_size=batch_size, capacity=batch_size*128, num_threads=1, min_after_dequeue=batch_size*32)	
+
+	# images, walls = tf.train.shuffle_batch([image, wall], 
+						# batch_size=batch_size, capacity=batch_size*128, num_threads=1, min_after_dequeue=batch_size*32)	
+
+	return {'images': images, 'label_boundaries': label_boundaries, 'label_rooms': label_rooms, 'label_doors': label_doors}

utils/util.py

@@ -0,0 +1,65 @@
+import cv2
+import numpy as np
+from scipy import ndimage
+
+def fast_hist(im, gt, n=9):
+    """
+    n is num_of_classes
+    """
+    k = (gt >= 0) & (gt < n)
+    return np.bincount(n * gt[k].astype(int) + im[k], minlength=n**2).reshape(n, n)
+
+def flood_fill(test_array, h_max=255):
+	"""
+	fill in the hole 
+	"""
+	input_array = np.copy(test_array) 
+	el = ndimage.generate_binary_structure(2,2).astype(int)
+	inside_mask = ndimage.binary_erosion(~np.isnan(input_array), structure=el)
+	output_array = np.copy(input_array)
+	output_array[inside_mask]=h_max
+	output_old_array = np.copy(input_array)
+	output_old_array.fill(0)   
+	el = ndimage.generate_binary_structure(2,1).astype(int)
+	while not np.array_equal(output_old_array, output_array):
+		output_old_array = np.copy(output_array)
+		output_array = np.maximum(input_array,ndimage.grey_erosion(output_array, size=(3,3), footprint=el))
+	return output_array
+
+def fill_break_line(cw_mask):
+	broken_line_h = np.array([[0,0,0,0,0],
+							[0,0,0,0,0],
+							[1,0,0,0,1],
+							[0,0,0,0,0],
+							[0,0,0,0,0]], dtype=np.uint8)	
+	broken_line_h2 = np.array([[0,0,0,0,0],
+							[0,0,0,0,0],
+							[1,1,0,1,1],
+							[0,0,0,0,0],
+							[0,0,0,0,0]], dtype=np.uint8)			
+	broken_line_v = np.transpose(broken_line_h)
+	broken_line_v2 = np.transpose(broken_line_h2)
+	cw_mask = cv2.morphologyEx(cw_mask, cv2.MORPH_CLOSE, broken_line_h)
+	cw_mask = cv2.morphologyEx(cw_mask, cv2.MORPH_CLOSE, broken_line_v)
+	cw_mask = cv2.morphologyEx(cw_mask, cv2.MORPH_CLOSE, broken_line_h2)
+	cw_mask = cv2.morphologyEx(cw_mask, cv2.MORPH_CLOSE, broken_line_v2)
+
+	return cw_mask	
+
+def refine_room_region(cw_mask, rm_ind):
+	label_rm, num_label = ndimage.label((1-cw_mask))
+	new_rm_ind = np.zeros(rm_ind.shape)
+	for j in range(1, num_label+1):  
+		mask = (label_rm == j).astype(np.uint8)
+		ys, xs = np.where(mask!=0)
+		area = (np.amax(xs)-np.amin(xs))*(np.amax(ys)-np.amin(ys))
+		if area < 100:
+			continue
+		else:
+			room_types, type_counts = np.unique(mask*rm_ind, return_counts=True)
+			if len(room_types) > 1:
+				room_types = room_types[1:] # ignore background type which is zero
+				type_counts = type_counts[1:] # ignore background count
+			new_rm_ind += mask*room_types[np.argmax(type_counts)]
+
+	return new_rm_ind