support progress bar

.gitignore

@@ -3,4 +3,9 @@
 *.jpeg
 *.jpg
 *.mp4
-*.mpg
+*.mpg
+*.csv
+*.webp
+*.png
+
+flagged*/

gradio_web_ui.py

@@ -9,7 +9,11 @@
 
 def inference_image(image, mlmodel_name: str, confidence: float):
     model = RTDETR(mlmodel_name)
-    results = model.predict(image, conf=confidence / 100)
+    results = model.predict(
+        source=image,
+        conf=confidence / 100,
+        verbose=False
+        )
     annotated_frame = results[0].plot()
     results = results[0].cpu()
 
@@ -29,8 +33,9 @@ def inference_image(image, mlmodel_name: str, confidence: float):
     return annotated_frame, csv_data
 
 
-def infer_video(videos, mlmodel_name: str, confidence: float):
+def infer_video(videos, mlmodel_name: str, confidence: float, progress=gr.Progress()):
     model = RTDETR(mlmodel_name)
+    model.info()
     output_files = []
     boxes_info = []
     for video in videos:
@@ -40,6 +45,7 @@ def infer_video(videos, mlmodel_name: str, confidence: float):
         try:
             while cap.isOpened():
                 ret, frame = cap.read()
+                progress(cap.get(cv2.CAP_PROP_POS_FRAMES) / cap.get(cv2.CAP_PROP_FRAME_COUNT))
                 if not ret:
                     break
                 results = model.track(
@@ -96,9 +102,19 @@ def infer_video(videos, mlmodel_name: str, confidence: float):
             [
                 gr.Image(type="numpy", label="Upload an Image"),
                 gr.Dropdown(
-                    glob.glob("./ml_model/*"), value="rtdetr-l.pt", label="ML Model", info="Will add more animals later!"
+                    glob.glob("./ml_model/*"),
+                    value="rtdetr-l.pt",
+                    label="ML Model",
+                    info="Please place the RT-DETR model in the ml_model directory under the root directory of this project! It supports extensions like .pt, .onnx, and .engine!"
+                ),
+                gr.Slider(
+                    minimum=0,
+                    maximum=100,
+                    value=75,
+                    label="Confidence",
+                    step=5,
+                    info="Choose between 0% and 100%"
                 ),
-                gr.Slider(0, 100, value=75, label="Confidence", step=5, info="Choose between 0% and 100%"),
             ],
             [
                 gr.Image(type="numpy", label="result image"),
@@ -109,17 +125,26 @@ def infer_video(videos, mlmodel_name: str, confidence: float):
         gr.Interface(
             infer_video,
             [
-                gr.File(label="Upload a Video", file_count="multiple", file_types=["mp4", "mpg"]),
+                gr.File(label="Upload a Video", file_count="multiple", file_types=["mp4", "mpg", "MOV"]),
                 gr.Dropdown(
-                    glob.glob("./ml_model/*"), value="rtdetr-l.pt", label="ML Model", info="Will add more animals later!"
+                    glob.glob("./ml_model/*"),
+                    value="rtdetr-l.pt",
+                    label="ML Model",
+                    info="Please place the RT-DETR model in the ml_model directory under the root directory of this project! It supports extensions like .pt, .onnx, and .engine!"
+                ),
+                gr.Slider(
+                    minimum=0,
+                    maximum=100,
+                    value=75,
+                    label="Confidence",
+                    step=5,
+                    info="Choose between 0% and 100%"
                 ),
-                gr.Slider(0, 100, value=75, label="Confidence", step=5, info="Choose between 0% and 100%"),
             ],
             [
                 gr.File(label="Annotated Video"),
-                # gr.Textbox(label="Bounding Boxes CSV"),
             ]
         )
 
 if __name__ == "__main__":
-    main_ui.launch(server_name="0.0.0.0")
+    main_ui.queue().launch(server_name="0.0.0.0")

model_download.sh

@@ -1,3 +1,8 @@
 #!/bin/bash
 mkdir -p ml_model
+
+# embryo model
 gdown --fuzzy -O ./ml_model/demo_model.pt 'https://docs.google.com/uc?export=download&id=1mnEUifj4NQXptB3rL48a8EarYtGrUqJx'
+
+# coco train model
+gdown --fuzzy -O ./ml_model/coco.pt 'https://docs.google.com/uc?export=download&id=1meIWpfRB0KYe7OcGC8j63dVCZGLKGOUb'

run_dev_env.sh

@@ -31,6 +31,7 @@ PLATFORM="$(uname -m)"
 
 if [ $PLATFORM = "x86_64" ]; then
     echo "x86"
+    docker pull ghcr.io/moriyalab/horus_inference_server:latest
     docker run -it --rm --gpus all --runtime nvidia --shm-size=32G -v $ROOT/datasets:/usr/src/datasets -v $ROOT:/home/root -w /home/root --network host ghcr.io/moriyalab/horus_inference_server:latest
 else
     echo "Not Support Platform. Only support x86."