update links

Dockerfile

@@ -22,7 +22,7 @@ COPY ./midas ./midas
 COPY ./*.py ./
 
 # download model weights so the docker image can be used offline
-RUN cd weights && {curl -OL https://github.com/AlexeyAB/MiDaS/releases/download/midas_dpt/dpt_hybrid-midas-501f0c75.pt; cd -; }
+RUN cd weights && {curl -OL https://github.com/isl-org/MiDaS/releases/download/v3/dpt_hybrid-midas-501f0c75.pt; cd -; }
 RUN python3 run.py --model_type dpt_hybrid; exit 0
 
 # entrypoint (dont forget to mount input and output directories)

hubconf.py

@@ -20,7 +20,7 @@ def DPT_Large(pretrained=True, **kwargs):
 
     if pretrained:
         checkpoint = (
-            "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt"
+            "https://github.com/isl-org/MiDaS/releases/download/v3/dpt_large-midas-2f21e586.pt"
         )
         state_dict = torch.hub.load_state_dict_from_url(
             checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True
@@ -43,7 +43,7 @@ def DPT_Hybrid(pretrained=True, **kwargs):
 
     if pretrained:
         checkpoint = (
-            "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt"
+            "https://github.com/isl-org/MiDaS/releases/download/v3/dpt_hybrid-midas-501f0c75.pt"
         )
         state_dict = torch.hub.load_state_dict_from_url(
             checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True
@@ -62,7 +62,7 @@ def MiDaS(pretrained=True, **kwargs):
 
     if pretrained:
         checkpoint = (
-            "https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-f6b98070.pt"
+            "https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.pt"
         )
         state_dict = torch.hub.load_state_dict_from_url(
             checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True
@@ -81,7 +81,7 @@ def MiDaS_small(pretrained=True, **kwargs):
 
     if pretrained:
         checkpoint = (
-            "https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-small-70d6b9c8.pt"
+            "https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small-70d6b9c8.pt"
         )
         state_dict = torch.hub.load_state_dict_from_url(
             checkpoint, map_location=torch.device('cpu'), progress=True, check_hash=True

mobile/android/README.md

@@ -18,4 +18,4 @@ To use another model, you should convert it to `model_opt.tflite` and place it t
 
 ----
 
-Original repository: https://github.com/intel-isl/MiDaS
+Original repository: https://github.com/isl-org/MiDaS

mobile/android/models/download.gradle

@@ -1,4 +1,4 @@
-def modelFloatDownloadUrl = "https://github.com/intel-isl/MiDaS/releases/download/v2_1/model_opt.tflite"
+def modelFloatDownloadUrl = "https://github.com/isl-org/MiDaS/releases/download/v2_1/model_opt.tflite"
 def modelFloatFile = "model_opt.tflite"
 
 task downloadModelFloat(type: Download) {

mobile/ios/README.md

@@ -33,7 +33,7 @@ pip install tensorflow
 
 ### Install TensorFlowLiteSwift via Cocoapods
 
-Set required TensorFlowLiteSwift version in the file (`0.0.1-nightly` is recommended): https://github.com/AlexeyAB/midas_tf_ios/blob/main/Podfile#L9
+Set required TensorFlowLiteSwift version in the file (`0.0.1-nightly` is recommended): https://github.com/isl-org/MiDaS/blob/master/mobile/ios/Podfile#L9
 
 Install: brew, ruby, cocoapods
 
@@ -82,7 +82,7 @@ open(model_tflite_name, "wb").write("model.tflite")
 
 ----
 
-Original repository: https://github.com/intel-isl/MiDaS
+Original repository: https://github.com/isl-org/MiDaS
 
 
 ### Examples:

mobile/ios/RunScripts/download_models.sh

@@ -3,7 +3,7 @@
 
 TFLITE_MODEL="model_opt.tflite"
 TFLITE_FILE="Midas/Model/${TFLITE_MODEL}"
-MODEL_SRC="https://github.com/intel-isl/MiDaS/releases/download/v2/${TFLITE_MODEL}"
+MODEL_SRC="https://github.com/isl-org/MiDaS/releases/download/v2/${TFLITE_MODEL}"
 
 if test -f "${TFLITE_FILE}"; then
     echo "INFO: TF Lite model already exists. Skip downloading and use the local model."

ros/README.md

@@ -18,14 +18,14 @@ MiDaS is a neural network to compute depth from a single image.
 
 * install ROS Melodic for Ubuntu 17.10 / 18.04:
 ```bash
-wget https://raw.githubusercontent.com/intel-isl/MiDaS/master/ros/additions/install_ros_melodic_ubuntu_17_18.sh
+wget https://raw.githubusercontent.com/isl-org/MiDaS/master/ros/additions/install_ros_melodic_ubuntu_17_18.sh
 ./install_ros_melodic_ubuntu_17_18.sh
 ```
 
 or Noetic for Ubuntu 20.04: 
 
 ```bash
-wget https://raw.githubusercontent.com/intel-isl/MiDaS/master/ros/additions/install_ros_noetic_ubuntu_20.sh
+wget https://raw.githubusercontent.com/isl-org/MiDaS/master/ros/additions/install_ros_noetic_ubuntu_20.sh
 ./install_ros_noetic_ubuntu_20.sh
 ```
 
@@ -61,7 +61,7 @@ source ~/.bashrc
 cd ~/
 mkdir catkin_ws
 cd catkin_ws
-git clone https://github.com/intel-isl/MiDaS
+git clone https://github.com/isl-org/MiDaS
 mkdir src
 cp -r MiDaS/ros/* src
 

ros/additions/downloads.sh

@@ -1,5 +1,5 @@
 mkdir ~/.ros
-wget https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-small-traced.pt
+wget https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small-traced.pt
 cp ./model-small-traced.pt ~/.ros/model-small-traced.pt
 
 

ros/midas_cpp/package.xml

@@ -6,7 +6,7 @@
 
   <maintainer email="alexeyab84@gmail.com">Alexey Bochkovskiy</maintainer>
   <license>MIT</license>
-  <url type="website">https://github.com/AlexeyAB/midas_ros</url>
+  <url type="website">https://github.com/isl-org/MiDaS/tree/master/ros</url>
   <!-- <author email="alexeyab84@gmail.com">Alexey Bochkovskiy</author> -->
 
 

tf/README.md

@@ -11,8 +11,8 @@
 
 ### Run inference on TensorFlow-model by using TensorFlow
 
-1) Download the model weights [model-f6b98070.pb](https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-f6b98070.pb) 
-and [model-small.pb](https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-small.pb) and place the
+1) Download the model weights [model-f6b98070.pb](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.pb) 
+and [model-small.pb](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small.pb) and place the
 file in the `/tf/` folder.
 
 2) Set up dependencies: 
@@ -47,8 +47,8 @@ pip install -I grpcio tensorflow==2.3.0 tensorflow-addons==0.11.2 numpy==1.18.0
 
 ### Run inference on ONNX-model by using ONNX-Runtime
 
-1) Download the model weights [model-f6b98070.onnx](https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-f6b98070.onnx) 
-and [model-small.onnx](https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-small.onnx) and place the
+1) Download the model weights [model-f6b98070.onnx](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.onnx) 
+and [model-small.onnx](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-small.onnx) and place the
 file in the `/tf/` folder.
 
 2) Set up dependencies: 
@@ -87,7 +87,7 @@ pip install onnxruntime==1.5.2
 
 ### Make ONNX model from downloaded Pytorch model file
 
-1) Download the model weights [model-f6b98070.pt](https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-f6b98070.pt) and place the
+1) Download the model weights [model-f6b98070.pt](https://github.com/isl-org/MiDaS/releases/download/v2_1/model-f6b98070.pt) and place the
 file in the root folder.
 
 2) Set up dependencies: