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Update app.py
#1
by
pardhunadella
- opened
app.py
CHANGED
@@ -1,3 +1,53 @@
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import gradio as gr
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import cv2
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import requests
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@@ -5,6 +55,8 @@ import os
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from ultralytics import YOLO
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model0 = YOLO('yolov8.pt')
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model1 = YOLO('yolov8.pt')
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model2 = YOLO('yolov8.pt')
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@@ -12,13 +64,23 @@ model3 = YOLO('yolov8.pt')
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model4 = YOLO('yolov8.pt')
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model5 = YOLO('yolov8.pt')
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models = [
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image = cv2.imread(image_path)
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outputs = models[
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results = outputs[0].cpu().numpy()
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for i, det in enumerate(results.boxes.xyxy):
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cv2.rectangle(
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@@ -31,23 +93,26 @@ def show_preds_image(image_path, model_selection=0):
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)
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return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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gr.
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gr.inputs.Dropdown(
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choices=[(name, idx) for idx, name in enumerate(model_names)],
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label="Select Model",
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default=0
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)
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]
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outputs_image = [
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gr.
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]
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interface_image = gr.Interface(
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fn=show_preds_image,
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inputs=
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outputs=outputs_image,
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title="Panicle detector app",
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)
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# import gradio as gr
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# import cv2
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# from ultralytics import YOLO
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# model0 = YOLO('yolov8.pt')
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# model1 = YOLO('yolov8.pt')
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# model2 = YOLO('yolov8.pt')
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# model3 = YOLO('yolov8.pt')
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# model4 = YOLO('yolov8.pt')
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# model5 = YOLO('yolov8.pt')
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# models = [model0, model1, model2, model3, model4, model5]
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# model_names = ["Model 0", "Model 1", "Model 2", "Model 3", "Model 4", "Model 5"]
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# def show_preds_image(image, model_selection=0):
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# img = image.read()
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# outputs = models[model_selection].predict(source=img)
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# results = outputs[0].cpu().numpy()
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# for i, det in enumerate(results.boxes.xyxy):
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# cv2.rectangle(
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# img,
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# (int(det[0]), int(det[1])),
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# (int(det[2]), int(det[3])),
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# color=(0, 0, 255),
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# thickness=2,
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# lineType=cv2.LINE_AA
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# )
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# return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# interface_image = gr.Interface(
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# fn=show_preds_image,
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# inputs=[
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# gr.inputs.Image(type="file", label="Input Image"),
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# gr.inputs.Dropdown(
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# choices=[(name, idx) for idx, name in enumerate(model_names)],
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# label="Select Model",
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# default=0
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# )
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# ],
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# outputs=gr.outputs.Image(type="numpy", label="Output Image"),
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# title="Panicle detector app",
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# )
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# interface_image.launch()
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import gradio as gr
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import cv2
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import requests
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from ultralytics import YOLO
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model0 = YOLO('yolov8.pt')
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model1 = YOLO('yolov8.pt')
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model2 = YOLO('yolov8.pt')
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model4 = YOLO('yolov8.pt')
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model5 = YOLO('yolov8.pt')
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models = []
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models.append(model0)
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models.append(model1)
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models.append(model2)
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models.append(model3)
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models.append(model4)
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models.append(model5)
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path = [['flowering.png']]
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def show_preds_image(image_path, selection):
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image = cv2.imread(image_path)
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outputs = models[selection].predict(source=image_path)
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results = outputs[0].cpu().numpy()
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for i, det in enumerate(results.boxes.xyxy):
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cv2.rectangle(
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)
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return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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inputs = [
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gr.components.Image(type="filepath", label="Input Image"),
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gr.components.Dropdown(choices=[str(i) for i in range(len(models))], label="Select Model", type="index"),
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]
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outputs_image = [
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gr.components.Image(type="numpy", label="Output Image"),
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]
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model_select = []
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interface_image = gr.Interface(
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fn=show_preds_image,
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inputs=inputs,
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outputs=outputs_image,
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title="Panicle detector app",
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examples=path,
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cache_examples=False,
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)
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gr.TabbedInterface(
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[interface_image],
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tab_names=['Image inference']
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).queue().launch()
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