custom vision imports

.gitignore

@@ -1,3 +1,4 @@
 .ipynb_checkpoints
 flagged
-telecom_object_detection.ipynb
+telecom_object_detection.ipynb
+.env

app.py

@@ -1,13 +1,117 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: telecom_object_detection.ipynb.
 
 # %% auto 0
-__all__ = ['title', 'css', 'urls', 'flip_text', 'flip_image']
+__all__ = ['prediction_endpoint', 'prediction_key', 'project_id', 'model_name', 'title', 'css', 'urls', 'imgs', 'img_samples',
+           'fig2img', 'custom_vision_detect_objects', 'flip_text', 'flip_image', 'set_example_url', 'set_example_image',
+           'detect_objects']
 
 # %% telecom_object_detection.ipynb 2
 import gradio as gr
+import numpy as np
+import os
+import io
+
+import requests
+
 from pathlib import Path
 
-# %% telecom_object_detection.ipynb 4
+# %% telecom_object_detection.ipynb 6
+from azure.cognitiveservices.vision.customvision.prediction import CustomVisionPredictionClient
+from msrest.authentication import ApiKeyCredentials
+from matplotlib import pyplot as plt
+from PIL import Image, ImageDraw, ImageFont
+from dotenv import load_dotenv
+
+# %% telecom_object_detection.ipynb 11
+def fig2img(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf)
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+    
+def custom_vision_detect_objects(image_file: Path):
+    dpi = 100
+
+    # Get Configuration Settings
+    load_dotenv()
+    prediction_endpoint = os.getenv('PredictionEndpoint')
+    prediction_key = os.getenv('PredictionKey')
+    project_id = os.getenv('ProjectID')
+    model_name = os.getenv('ModelName')
+
+    # Authenticate a client for the training API
+    credentials = ApiKeyCredentials(in_headers={"Prediction-key": prediction_key})
+    prediction_client = CustomVisionPredictionClient(endpoint=prediction_endpoint, credentials=credentials)
+
+    # Load image and get height, width and channels
+    #image_file = 'produce.jpg'
+    print('Detecting objects in', image_file)
+    image = Image.open(image_file)
+    h, w, ch = np.array(image).shape
+
+    # Detect objects in the test image
+    with open(image_file, mode="rb") as image_data:
+        results = prediction_client.detect_image(project_id, model_name, image_data)
+    
+    # Create a figure for the results
+    fig = plt.figure(figsize=(w/dpi, h/dpi))
+    plt.axis('off')
+
+    # Display the image with boxes around each detected object
+    draw = ImageDraw.Draw(image)
+    lineWidth = int(w/800)
+    color = 'cyan'
+
+    for prediction in results.predictions:
+        # Only show objects with a > 50% probability
+        if (prediction.probability*100) > 50:
+            # Box coordinates and dimensions are proportional - convert to absolutes
+            left = prediction.bounding_box.left * w 
+            top = prediction.bounding_box.top * h 
+            height = prediction.bounding_box.height * h
+            width =  prediction.bounding_box.width * w
+
+            # Draw the box
+            points = ((left,top), (left+width,top), (left+width,top+height), (left,top+height), (left,top))
+            draw.line(points, fill=color, width=lineWidth)
+
+            # Add the tag name and probability
+            #plt.annotate(prediction.tag_name + ": {0:.2f}%".format(prediction.probability * 100),(left,top), backgroundcolor=color)
+            plt.annotate(
+                prediction.tag_name + ": {0:.0f}%".format(prediction.probability * 100),
+                (left, top-1.372*h/dpi), 
+                backgroundcolor=color,
+                fontsize=max(w/dpi, h/dpi), 
+                fontfamily='monospace'
+            )
+
+    plt.imshow(image)
+    plt.tight_layout(pad=0)
+    
+    return fig2img(fig)
+
+    outputfile = 'output.jpg'
+    fig.savefig(outputfile)
+    print('Resulabsts saved in ', outputfile)
+
+# %% telecom_object_detection.ipynb 13
+load_dotenv()
+prediction_endpoint = os.getenv('PredictionEndpoint')
+prediction_key = os.getenv('PredictionKey')
+project_id = os.getenv('ProjectID')
+model_name = os.getenv('ModelName')
+print(prediction_endpoint)
+print(prediction_key)
+print(project_id)
+print(model_name)
+#print('/'*10)
+#print(credentials)
+#print(prediction_client)
+#print('/'*10)
+#print(h, w, ch)
+
+# %% telecom_object_detection.ipynb 15
 title = """<h1 id="title">Telecom Object Detection with Azure Custom Vision</h1>"""
 
 css = '''
@@ -16,15 +120,49 @@ h1#title {
 }
 '''
 
-# %% telecom_object_detection.ipynb 5
-import numpy as np
-import gradio as gr
-
+# %% telecom_object_detection.ipynb 16
 urls = ["https://c8.alamy.com/comp/J2AB4K/the-new-york-stock-exchange-on-the-wall-street-in-new-york-J2AB4K.jpg"]
+imgs = [path.as_posix() for path in sorted(Path('images').rglob('*.jpg'))]
+img_samples = [[path.as_posix()] for path in sorted(Path('images').rglob('*.jpg'))]
 
+# %% telecom_object_detection.ipynb 17
 def flip_text(): pass
 def flip_image(): pass
+def set_example_url(example: list) -> dict:
+    return gr.Textbox.update(value=example[0])
+
+def set_example_image(example: list) -> dict:
+    #print(example)
+    #print(gr.Image.update(value=example[0]))
+    return gr.Image.update(value=example[0])
+
+#def detect_objects(url_input, image_input):
+
+def detect_objects(image_input:Image):
+    #if validators.url(url_input):
+    #    image = Image.open(requests.get(url_input, stream=True).raw)
+    #elif image_input:
+    #    image = image_input
+    print(image_input)
+    print(image_input.size)
+    w, h = image_input.size
+    
+    if max(w, h) > 1_200:
+        #factor = int(max(w, h) / 1_200)
+        #image_input = image_input.reduce(factor)
+        factor = 1_200 / max(w, h)
+        size = (int(w*factor), int(h*factor))
+        image_input = image_input.resize(size, resample=Image.Resampling.BILINEAR)
+    
+    resized_image_path = "input_object_detection.png"
+    print(image_input.save(resized_image_path))
+    
+    #return fig2img(fig)
+    return image_input
+    #return custom_vision_detect_objects(Path(filename[0]))
+    #return custom_vision_detect_objects(resized_image_path))
 
+# %% telecom_object_detection.ipynb 18
 with gr.Blocks(css=css) as demo:
     
     gr.Markdown(title)
@@ -32,18 +170,16 @@ with gr.Blocks(css=css) as demo:
     with gr.Tabs():
         with gr.TabItem("Image Upload"):
             with gr.Row():
-                image_input = gr.Image()
-                image_output = gr.Image()
+                image_input = gr.Image(type='pil')
+                image_output = gr.Image(shape=(650,650))
+                
             with gr.Row(): 
                 """example_images = gr.Dataset(components=[img_input],
                     samples=[[path.as_posix()] for path in sorted(Path('images').rglob('*.jpg'))]
                 )"""
-                example_images = gr.Examples(
-                    examples=[path.as_posix() for path in sorted(Path('images').rglob('*.jpg'))],
-                    inputs=image_input
-                )
-                
-                                             
+                #example_images = gr.Examples(examples=imgs, inputs=image_input)
+                example_images = gr.Dataset(components=[image_input], samples=img_samples)
+            
             image_button = gr.Button("Detect")
         
         with gr.TabItem("Image URL"):
@@ -55,8 +191,11 @@ with gr.Blocks(css=css) as demo:
                 example_url = gr.Dataset(components=[url_input], samples=[[str(url)] for url in urls])
             url_button = gr.Button("Detect")
             
-
-    url_button.click(flip_text, inputs=url_input, outputs=img_output_from_url)
-    image_button.click(flip_image, inputs=image_input, outputs=image_output)
+    url_button.click(detect_objects, inputs=[url_input], outputs=img_output_from_url)
+    image_button.click(detect_objects, inputs=[image_input], outputs=image_output)
+    #image_button.click(detect_objects, inputs=[example_images], outputs=image_output)
+    
+    example_url.click(fn=set_example_url, inputs=[example_url], outputs=[url_input])
+    example_images.click(fn=set_example_image, inputs=[example_images], outputs=[image_input])
 
 demo.launch()