Update app.py

app.py

@@ -1,156 +1,84 @@
-import io
 import gradio as gr
-import matplotlib.pyplot as plt
-import requests, validators
 import torch
-import pathlib
-from PIL import Image
-from transformers import AutoFeatureExtractor, DetrForObjectDetection, YolosForObjectDetection
-
-import os
-
-# colors for visualization
-COLORS = [
-    [0.000, 0.447, 0.741],
-    [0.850, 0.325, 0.098],
-    [0.929, 0.694, 0.125],
-    [0.494, 0.184, 0.556],
-    [0.466, 0.674, 0.188],
-    [0.301, 0.745, 0.933]
-]
-
-def make_prediction(img, feature_extractor, model):
-    inputs = feature_extractor(img, return_tensors="pt")
-    outputs = model(**inputs)
-    img_size = torch.tensor([tuple(reversed(img.size))])
-    processed_outputs = feature_extractor.post_process(outputs, img_size)
-    return processed_outputs[0]
-
-def fig2img(fig):
-    buf = io.BytesIO()
-    fig.savefig(buf)
-    buf.seek(0)
-    img = Image.open(buf)
-    return img
-
-
-def visualize_prediction(pil_img, output_dict, threshold=0.7, id2label=None):
-    keep = output_dict["scores"] > threshold
-    boxes = output_dict["boxes"][keep].tolist()
-    scores = output_dict["scores"][keep].tolist()
-    labels = output_dict["labels"][keep].tolist()
-    if id2label is not None:
-        labels = [id2label[x] for x in labels]
-
-    plt.figure(figsize=(16, 10))
-    plt.imshow(pil_img)
-    ax = plt.gca()
-    colors = COLORS * 100
-    for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors):
-        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3))
-        ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
-    plt.axis("off")
-    return fig2img(plt.gcf())
-
-def detect_objects(model_name,url_input,image_input,threshold):
-    
-    #Extract model and feature extractor
-    feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)
-    
-    if 'detr' in model_name:
-        
-        model = DetrForObjectDetection.from_pretrained(model_name)
-        
-    elif 'yolos' in model_name:
-    
-        model = YolosForObjectDetection.from_pretrained(model_name)
-    
-    if validators.url(url_input):
-        image = Image.open(requests.get(url_input, stream=True).raw)
-        
-    elif image_input:
-        image = image_input
-    
-    #Make prediction
-    processed_outputs = make_prediction(image, feature_extractor, model)
-    
-    #Visualize prediction
-    viz_img = visualize_prediction(image, processed_outputs, threshold, model.config.id2label)
-    
-    return viz_img   
+from sahi.prediction import ObjectPrediction
+from sahi.utils.cv import visualize_object_predictions, read_image
+from ultralyticsplus import YOLO
+
+# Images
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/kadirnar/dethub/main/data/images/highway.jpg', 'highway.jpg')
+torch.hub.download_url_to_file('https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg', 'highway1.jpg')
+torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
+
+def yolov8_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,
+):
+    """
+    YOLOv8 inference function
+    Args:
+        image: Input image
+        model_path: Path to the model
+        image_size: Image size
+        conf_threshold: Confidence threshold
+        iou_threshold: IOU threshold
+    Returns:
+        Rendered image
+    """
+    model = YOLO(model_path)
+    model.conf = conf_threshold
+    model.iou = iou_threshold
+    results = model.predict(image, imgsz=image_size, return_outputs=True)
+    object_prediction_list = []
+    for _, image_results in enumerate(results):
+        if len(image_results)!=0:
+            image_predictions_in_xyxy_format = image_results['det']
+            for pred in image_predictions_in_xyxy_format:
+                x1, y1, x2, y2 = (
+                    int(pred[0]),
+                    int(pred[1]),
+                    int(pred[2]),
+                    int(pred[3]),
+                )
+                bbox = [x1, y1, x2, y2]
+                score = pred[4]
+                category_name = model.model.names[int(pred[5])]
+                category_id = pred[5]
+                object_prediction = ObjectPrediction(
+                    bbox=bbox,
+                    category_id=int(category_id),
+                    score=score,
+                    category_name=category_name,
+                )
+                object_prediction_list.append(object_prediction)
+
+    image = read_image(image)
+    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
+    return output_image['image']
         
-def set_example_image(example: list) -> dict:
-    return gr.Image.update(value=example[0])
 
-def set_example_url(example: list) -> dict:
-    return gr.Textbox.update(value=example[0])
-
-
-title = """<h1 id="title">Object Detection App with DETR and YOLOS</h1>"""
-
-description = """
-Links to HuggingFace Models:
-
-- [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)  
-- [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101)  
-- [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small)
-- [hustvl/yolos-tiny](https://huggingface.co/hustvl/yolos-tiny)
-- [Snearec/detectorMalezasYolo8](https://huggingface.co/Snearec/detectorMalezasYolo8)
-
-"""
-
-models = ["facebook/detr-resnet-50","facebook/detr-resnet-101",'hustvl/yolos-small','hustvl/yolos-tiny','[Snearec/detectorMalezasYolo8]']
-urls = ["https://c8.alamy.com/comp/J2AB4K/the-new-york-stock-exchange-on-the-wall-street-in-new-york-J2AB4K.jpg"]
-
-twitter_link = """
-[![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi)
-"""
-
-css = '''
-h1#title {
-  text-align: center;
-}
-'''
-demo = gr.Blocks(css=css)
-
-with demo:
-    gr.Markdown(title)
-    gr.Markdown(description)
-    gr.Markdown(twitter_link)
-    options = gr.Dropdown(choices=models,label='Select Object Detection Model',show_label=True)
-    slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.7,label='Prediction Threshold')
-    
-    with gr.Tabs():
-        with gr.TabItem('Image URL'):
-            with gr.Row():
-                url_input = gr.Textbox(lines=2,label='Enter valid image URL here..')
-                img_output_from_url = gr.Image(shape=(650,650))
-                
-            with gr.Row():
-                example_url = gr.Dataset(components=[url_input],samples=[[str(url)] for url in urls])
-            
-            url_but = gr.Button('Detect')
-     
-        with gr.TabItem('Image Upload'):
-            with gr.Row():
-                img_input = gr.Image(type='pil')
-                img_output_from_upload= gr.Image(shape=(650,650))
-                
-            with gr.Row(): 
-                example_images = gr.Dataset(components=[img_input],
-                                            samples=[[path.as_posix()]
-                                                     for path in sorted(pathlib.Path('images').rglob('*.JPG'))])
-                
-            img_but = gr.Button('Detect')
-        
-    
-    url_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_url,queue=True)
-    img_but.click(detect_objects,inputs=[options,url_input,img_input,slider_input],outputs=img_output_from_upload,queue=True)
-    example_images.click(fn=set_example_image,inputs=[example_images],outputs=[img_input])
-    example_url.click(fn=set_example_url,inputs=[example_url],outputs=[url_input])
-    
-
-    gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-object-detection-with-detr-and-yolos)")
+inputs = [
+    gr.inputs.Image(type="filepath", label="Input Image"),
+    gr.inputs.Dropdown(["kadirnar/yolov8n-v8.0", "kadirnar/yolov8m-v8.0", "kadirnar/yolov8l-v8.0", "kadirnar/yolov8x-v8.0", "kadirnar/yolov8x6-v8.0"], 
+                       default="kadirnar/yolov8m-v8.0", label="Model"),
+    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"),
+]
 
-    
-demo.launch(enable_queue=True)
+outputs = gr.outputs.Image(type="filepath", label="Output Image")
+title = "Ultralytics YOLOv8: State-of-the-Art YOLO Models"
+
+examples = [['highway.jpg', 'kadirnar/yolov8m-v8.0', 640, 0.25, 0.45], ['highway1.jpg', 'kadirnar/yolov8l-v8.0', 640, 0.25, 0.45], ['small-vehicles1.jpeg', 'kadirnar/yolov8x-v8.0', 1280, 0.25, 0.45]]
+demo_app = gr.Interface(
+    fn=yolov8_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
+    cache_examples=True,
+    theme='huggingface',
+)
+demo_app.launch(debug=True, enable_queue=True)