first commit with sahi vfnet

app.py

@@ -0,0 +1,64 @@
+from gradio import Label
+from icevision.all import *
+import gradio as gr
+from icevision.models.checkpoint import *
+import PIL
+import gradio as gr
+import os
+
+from icevision.models.inference_sahi import IceSahiModel
+
+
+
+
+# Load model
+checkpoint_path = "labeled_data_vfnet_resnet50ms2x_640_bs8_maxbbox500.pth"
+checkpoint_and_model = model_from_checkpoint(checkpoint_path)
+model = checkpoint_and_model["model"]
+model_type = checkpoint_and_model["model_type"]
+class_map = checkpoint_and_model["class_map"]
+
+# Transforms
+img_size = checkpoint_and_model["img_size"]
+valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
+
+# Populate examples in Gradio interface
+examples = [
+    ['./sqlot.jpg'],
+]
+sahimodel = IceSahiModel(model_type=model_type, model=model, class_map=class_map, tfms=valid_tfms, confidence_threshold=0.4)
+
+def show_preds(input_image):
+    img = PIL.Image.fromarray(input_image, "RGB")
+
+    pred_dict = sahimodel.get_sliced_prediction(
+                img,
+                keep_sahi_format=False,
+                return_img=True,
+                slice_height = 512,
+                slice_width = 512,
+                overlap_height_ratio = 0.2,
+                overlap_width_ratio = 0.2,
+                display_label=True, 
+                display_bbox=True)
+
+
+    # pred_dict = model_type.end2end_detect(img, valid_tfms, model, 
+    #                                       class_map=class_map, 
+    #                                       detection_threshold=0.5,
+    #                                       display_label=True, 
+    #                                       display_bbox=True, 
+    #                                       return_img=True, 
+    #                                       font_size=15,
+    #                                       label_color="#FF59D6")
+    return pred_dict["img"]
+
+gr_interface = gr.Interface(
+    fn=show_preds,
+    inputs=["image"],
+    outputs=[gr.outputs.Image(type="pil", label="VFNET Inference with Sahi")],
+    title="Spaces Empty or Not?",
+    description="A VFNET model that detects whether parking spaces are empty or not. Upload an image or click an example image below to use.",
+    examples=examples,
+)
+gr_interface.launch(inline=False, share=False, debug=True)

labeled_data_vfnet_resnet50ms2x_640_bs8_maxbbox500.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5a320b3b5e3b6d91a91d3aecbeaec877dfd8647f9b34396992c31436a1cde40
+size 131192663

packages.txt

@@ -0,0 +1 @@
+python3-opencv

requirements.txt

@@ -0,0 +1,8 @@
+sahi
+gradio
+-f https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html
+mmcv-full==1.3.17
+git+https://github.com/airctic/icevision.git#egg=icevision[all]
+git+https://github.com/airctic/icedata.git
+mmdet==2.17.0
+