Upload app.py

app.py

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+
+
+import io
+import gradio as gr
+import matplotlib.pyplot as plt
+import requests, validators
+from sqlalchemy import true
+import torch
+import pathlib
+from PIL import Image
+import os
+
+from detecto import core, utils, visualize
+from detecto.core import Model as DetectoModel
+
+
+
+title = """<h1 id="title">AEYE INSPECTOR</h1>"""
+css = '''
+h1#title {
+  text-align: center;
+}
+'''
+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]
+]
+
+models = ["Detecto (Faster-RCNN)","YOLOv100"]
+urls = [#'http://fbbbb.ddns.net:4080/static/images/ai_img(1).jpg',
+        #'http://fbbbb.ddns.net:4080/static/images/ai_img(2).jpg',
+        #'http://fbbbb.ddns.net:4080/static/images/ai_img(3).jpg',
+        #'http://fbbbb.ddns.net:4080/static/images/ai_img(4).jpg',
+        #'http://fbbbb.ddns.net:4080/static/images/ai_img(5).jpg',
+        #'http://fbbbb.ddns.net:4080/static/images/ai_img(6).jpg',
+        #'http://fbbbb.ddns.net:4080/static/images/ai_img(7).jpg',
+        #'http://fbbbb.ddns.net:4080/static/images/ai_img(8).jpg',
+        #'http://fbbbb.ddns.net:4080/static/images/ai_img(9).jpg',
+        #'http://fbbbb.ddns.net:4080/static/images/ai_img(10).jpg'
+        ]
+
+
+def detect_objects(model_name,url_input,image_input,threshold):
+    if validators.url(url_input):
+        image = Image.open(requests.get(url_input, stream=True).raw)
+    elif image_input:
+        image = image_input
+        
+    if  'Detecto' in model_name:
+        model = DetectoModel(['heltmet_safe','face_mask','safety_vest','safety_belts','safety_shoes'])
+        model.load('ai_30ep.pth',['heltmet_safe','face_mask','safety_vest','safety_belts','safety_shoes'])
+
+        print("OK")
+        labels, boxes, scores = model.predict(image)
+        viz_img = visualize_prediction(image, labels, boxes, scores, threshold)
+        
+        print(labels)
+        #print(boxes)
+        print(scores)
+        
+        return viz_img
+
+def visualize_prediction(pil_img, labels, boxes, scores, threshold=0.7):
+    keeps = scores > threshold
+    print(keeps)
+    
+    boxess = boxes[keeps].tolist()
+    print(boxess)
+    
+    #labelss = labels[keep]
+    #print(labelss)
+
+    plt.figure(figsize=(16, 10))
+    plt.imshow(pil_img)
+    ax = plt.gca()
+    colors = COLORS * 100
+    for idx, keep in enumerate(keeps):
+      if keep:
+        (xmin, ymin, xmax, ymax) = zip(boxess[idx])
+        print(xmin[0])
+        print(ymin[0])
+        ax.add_patch(plt.Rectangle((xmin[0], ymin[0]), xmax[0] - xmin[0], ymax[0] - ymin[0], fill=False, color=colors[idx], linewidth=3))
+        ax.text(xmin[0], ymin[0], f'{labels[idx]}: {scores[idx]:0.2f}', fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
+        
+    plt.axis("off")
+    return fig2img(plt.gcf())
+        
+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])   
+    
+def fig2img(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf)
+    buf.seek(0)
+    img = Image.open(buf)
+    return img     
+
+
+
+app = gr.Blocks(css=css)
+
+with app:
+    gr.Markdown(title)
+    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])
+    
+   
+app.launch(enable_queue=True, server_name='0.0.0.0', show_error=True)