Init Repo

Add images and everything except model

app.py

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+from gradio.outputs import Label
+from icevision.all import *
+from icevision.models.checkpoint import *
+import PIL
+import gradio as gr
+import os
+
+detection_threshold = 0.7
+checkpoint_path = "2022-10-03_resnet_slates_147.pth"
+description = (
+    "A faster-rcnn model that detects film slates / clappers. "
+    "Upload an image or click an example below."
+)
+
+# Load model
+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 = [["1.jpg"], ["2.jpg"], ["3.jpg"]]
+
+
+def show_preds(input_image):
+    img = PIL.Image.fromarray(input_image, "RGB")
+    pred_dict = model_type.end2end_detect(
+        img,
+        valid_tfms,
+        model,
+        class_map=class_map,
+        detection_threshold=detection_threshold,
+        display_label=False,
+        display_bbox=True,
+        return_img=True,
+        font_size=16,
+        label_color="#FF59D6",
+    )
+    return pred_dict["img"]
+
+
+gr_interface = gr.Interface(
+    fn=show_preds,
+    inputs=["image"],
+    outputs=[gr.outputs.Image(type="pil", label="Inference")],
+    title="Film Slate Detector",
+    description=description,
+    examples=examples,
+)
+gr_interface.launch(inline=False, share=False, debug=True)

packages.txt

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+python3-opencv

requirements.txt

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+--find-links https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html
+mmcv-full==1.3.17
+mmdet==2.17.0
+gradio==2.7.5
+icevision[all]==0.12.0