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Update app.py
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app.py
CHANGED
@@ -17,7 +17,6 @@ import os
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# Load model
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checkpoint_path = "models/model_checkpoint.pth"
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checkpoint_and_model = model_from_checkpoint(checkpoint_path)
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-
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model = checkpoint_and_model["model"]
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model_type = checkpoint_and_model["model_type"]
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class_map = checkpoint_and_model["class_map"]
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@@ -26,18 +25,13 @@ class_map = checkpoint_and_model["class_map"]
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img_size = checkpoint_and_model["img_size"]
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valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
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for root, dirs, files in os.walk(r"sample_images/"):
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for filename in files:
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print(filename)
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examples = ["sample_images/" + file for file in files]
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article = "<p style='text-align: center'><a href='https://dicksonneoh.com/' target='_blank'>Blog post</a></p>"
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enable_queue = True
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# Populate examples in Gradio interface
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example_images = [["sample_images/" + file] for file in files]
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# Columns: Input Image | Label | Box | Detection Threshold
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examples = [
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[example_images[0], False, True, 0.5],
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@@ -46,17 +40,15 @@ examples = [
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[example_images[3], True, True, 0.7],
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[example_images[4], False, True, 0.5],
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[example_images[5], False, True, 0.5],
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[example_images[6], False, True, 0.
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[example_images[7], False, True, 0.
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]
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def show_preds(input_image, display_label, display_bbox, detection_threshold):
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if detection_threshold == 0:
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detection_threshold = 0.5
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img = PIL.Image.fromarray(input_image, "RGB")
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pred_dict = model_type.end2end_detect(
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img,
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valid_tfms,
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@@ -69,22 +61,21 @@ def show_preds(input_image, display_label, display_bbox, detection_threshold):
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font_size=16,
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label_color="#FF59D6",
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)
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return pred_dict["img"], len(pred_dict["detection"]["bboxes"])
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# display_chkbox = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display", default=True)
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display_chkbox_label = gr.inputs.Checkbox(label="Label", default=False)
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display_chkbox_box = gr.inputs.Checkbox(label="Box", default=True)
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detection_threshold_slider = gr.inputs.Slider(
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minimum=0, maximum=1, step=0.1, default=0.5, label="Detection Threshold"
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)
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outputs = [
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gr.outputs.Image(type="pil", label="RetinaNet Inference"),
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gr.outputs.Textbox(type=
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# Option 1: Get an image from local drive
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gr_interface = gr.Interface(
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@@ -101,13 +92,8 @@ gr_interface = gr.Interface(
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article=article,
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examples=examples,
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)
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# # Option 2: Grab an image from a webcam
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# gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=False)
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# # Option 3: Continuous image stream from the webcam
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# gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=True)
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gr_interface.launch(inline=False, share=False, debug=True)
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# Load model
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checkpoint_path = "models/model_checkpoint.pth"
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checkpoint_and_model = model_from_checkpoint(checkpoint_path)
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model = checkpoint_and_model["model"]
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model_type = checkpoint_and_model["model_type"]
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class_map = checkpoint_and_model["class_map"]
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img_size = checkpoint_and_model["img_size"]
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valid_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(img_size), tfms.A.Normalize()])
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for root, dirs, files in os.walk(r"sample_images/"):
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for filename in files:
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print("Loading sample image:", filename)
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# Populate examples in Gradio interface
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example_images = [["sample_images/" + file] for file in files]
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# Columns: Input Image | Label | Box | Detection Threshold
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examples = [
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[example_images[0], False, True, 0.5],
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[example_images[3], True, True, 0.7],
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[example_images[4], False, True, 0.5],
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[example_images[5], False, True, 0.5],
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[example_images[6], False, True, 0.6],
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[example_images[7], False, True, 0.6],
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]
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def show_preds(input_image, display_label, display_bbox, detection_threshold):
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if detection_threshold == 0:
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detection_threshold = 0.5
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img = PIL.Image.fromarray(input_image, "RGB")
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pred_dict = model_type.end2end_detect(
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img,
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valid_tfms,
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font_size=16,
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label_color="#FF59D6",
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)
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return pred_dict["img"], len(pred_dict["detection"]["bboxes"])
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# display_chkbox = gr.inputs.CheckboxGroup(["Label", "BBox"], label="Display", default=True)
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display_chkbox_label = gr.inputs.Checkbox(label="Label", default=False)
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display_chkbox_box = gr.inputs.Checkbox(label="Box", default=True)
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detection_threshold_slider = gr.inputs.Slider(
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minimum=0, maximum=1, step=0.1, default=0.5, label="Detection Threshold"
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)
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outputs = [
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gr.outputs.Image(type="pil", label="RetinaNet Inference"),
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gr.outputs.Textbox(type="number", label="Microalgae Count"),
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]
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article = "<p style='text-align: center'><a href='https://dicksonneoh.com/' target='_blank'>Blog post</a></p>"
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# Option 1: Get an image from local drive
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gr_interface = gr.Interface(
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article=article,
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examples=examples,
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)
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# # Option 2: Grab an image from a webcam
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# gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=False)
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# # Option 3: Continuous image stream from the webcam
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# gr_interface = gr.Interface(fn=show_preds, inputs=["webcam", display_chkbox_label, display_chkbox_box, detection_threshold_slider], outputs=outputs, title='IceApp - COCO', live=True)
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gr_interface.launch(inline=False, share=False, debug=True)
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