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Update app.py
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app.py
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@@ -1,11 +1,4 @@
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import
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import sys
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print("Reinstalling mmcv")
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subprocess.check_call([sys.executable, "-m", "pip", "uninstall", "-y", "mmcv-full==1.3.17"])
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subprocess.check_call([sys.executable, "-m", "pip", "install", "mmcv-full==1.3.17", "-f", "https://download.openmmlab.com/mmcv/dist/cpu/torch1.10.0/index.html"])
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print("mmcv install complete")
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from icevision.all import *
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from icevision.models.checkpoint import *
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import PIL
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import os
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# Load model
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checkpoint_path =
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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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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
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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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def show_preds(input_image, display_label, display_bbox, detection_threshold):
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if detection_threshold==0:
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pred_dict
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display_label=display_label, display_bbox=display_bbox, return_img=True,
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font_size=16, label_color="#FF59D6")
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return pred_dict['img']
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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=
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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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outputs =
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# Option 1: Get an image from local drive
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gr_interface = gr.Interface(
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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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from gradio.outputs import Label
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from icevision.all import *
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from icevision.models.checkpoint import *
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import PIL
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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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model = checkpoint_and_model["model"]
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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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[example_images[1], True, True, 0.5],
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[example_images[2], False, True, 0.7],
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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, 0.5],
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[example_images[7], False, True, 0.5],
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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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model,
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class_map=class_map,
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detection_threshold=detection_threshold,
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display_label=display_label,
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display_bbox=display_bbox,
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return_img=True,
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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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# Option 1: Get an image from local drive
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gr_interface = gr.Interface(
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fn=show_preds,
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inputs=[
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"image",
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display_chkbox_label,
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display_chkbox_box,
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detection_threshold_slider,
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],
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outputs=outputs,
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title="Microalgae Detector with RetinaNet",
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description="This RetinaNet model counts microalgaes on a given image. Upload an image or click an example image below to use.",
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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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