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Roof_Elements_Y5.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8f26cc2ea112dd530ecdef90b982750e3257e3b5bff498e2881929e2f0a039bf
+size 42274665

app.py

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+import gradio as gr
+from gradio.outputs import Label
+import cv2
+import requests
+import os
+import numpy as np
+
+from ultralytics import YOLO
+import yolov5
+
+# Function for inference
+def yolov5_inference(
+    image: gr.inputs.Image = None,
+    model_path: gr.inputs.Dropdown = None,
+    image_size: gr.inputs.Slider = 640,
+    conf_threshold: gr.inputs.Slider = 0.25,
+    iou_threshold: gr.inputs.Slider = 0.45 ):
+
+    # Loading Yolo V5 model
+    model = yolov5.load(model_path, device="cpu")
+
+    # Setting model configuration 
+    model.conf = conf_threshold
+    model.iou = iou_threshold
+
+    # Inference
+    results = model([image], size=image_size)
+
+    # Cropping the predictions    
+    crops = results.crop(save=False)
+    img_crops = []
+    for i in range(len(crops)):
+        img_crops.append(crops[i]["im"][..., ::-1])
+    return results.render()[0], img_crops
+        
+# gradio Input
+inputs = [
+    gr.inputs.Image(type="pil", label="Input Image"),
+    gr.inputs.Dropdown(["Roof_Elements_Y5.pt"], label="Model", default = 'Roof_Elements_Y5.pt'),
+    gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
+    gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
+]
+
+# gradio Output
+outputs = gr.outputs.Image(type="filepath", label="Output Image")
+outputs_crops = gr.Gallery(label="Object crop")
+
+title = "Roof element identification"
+
+# gradio examples: "Image", "Model", "Image Size", "Confidence Threshold", "IOU Threshold"
+examples = [['image_0.jpg', 'Roof_Elements_Y5.pt', 640, 0.35, 0.45]
+           ,['image_1.jpg', 'Roof_Elements_Y5.pt', 640, 0.35, 0.45]
+           ,['image_2.jpg', 'Roof_Elements_Y5.pt', 640, 0.35, 0.45],
+           ]
+
+# gradio app launch
+demo_app = gr.Interface(
+    fn=yolov5_inference,
+    inputs=inputs,
+    outputs=[outputs,outputs_crops],
+    title=title,
+    examples=examples,
+    cache_examples=True,
+    live=True,
+    theme='huggingface',
+)
+demo_app.launch(debug=True, enable_queue=True, width=50, height=50)

requirements.txt

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+gradio==3.4.0
+opencv-python
+numpy<1.24
+ultralytics
+yolov5