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import gradio as gr
import torch
from PIL import ImageDraw
import pathlib
# Model
model_path = 'model_torch.pt'
model = torch.hub.load('Ultralytics/yolov5', 'custom', model_path, verbose = False)
model.eval()
labels = model.names
colors = ["red", "blue", "green", "yellow"]
def detect_objects(image):
draw = ImageDraw.Draw(image)
detections = model(image)
probabilities = {}
for detection in detections.xyxy[0]:
x1, y1, x2, y2, p, category_id = detection
x1, y1, x2, y2, category_id = int(x1), int(y1), int(x2), int(y2), int(category_id)
draw.rectangle((x1, y1, x2, y2), outline=colors[category_id], width=4)
draw.text((x1, y1), labels[category_id], colors[category_id])
probabilities[labels[category_id]] = float(p)
return [image, probabilities]
demo = gr.Blocks()#(css=css)
title = '# 3D print failures detection App'
description = 'App for detect errors in the 3D printing'
with demo:
gr.Markdown(title)
gr.Markdown(description)
with gr.Tabs():
#Image static
with gr.TabItem('Image Upload'):
with gr.Row():
with gr.Column():
img_input = gr.Image(type='pil')
examples_images2 = gr.Examples(examples = [[path.as_posix()] for path in sorted(pathlib.Path('images').rglob('*.jpg'))],
inputs=img_input)
labels_bars = gr.Label(label = "Categories")
print(labels_bars)
with gr.Column():
img_output= gr.Image()
img_button = gr.Button('Detect')
img_button.click(detect_objects,inputs=img_input,outputs=[img_output, labels_bars])
#Image static
with gr.TabItem('Webcam'):
with gr.Row():
with gr.Column():
webcam_input = gr.Image(shape=(320,240), source='webcam', type="pil", )
webcam_output = gr.Image()
with gr.Column():
labels_bars2 = gr.Label(label = "Categories")
webcam_button = gr.Button('Detect')
webcam_button.click(detect_objects,inputs=webcam_input,outputs=[webcam_output, labels_bars2])
if __name__ == "__main__":
demo.launch() |