Update app.py

app.py

@@ -1,48 +1,90 @@
 import gradio as gr
-from PIL import Image, ImageDraw
-import json
+import csv
+from io import StringIO
+from PIL import Image
+import numpy as np
+import base64
 
-# Define the function that will annotate the image
-def annotate_image(input_image, annotations):
-    # Load the image and create a drawing context
-    img = Image.fromarray(input_image.astype('uint8'), 'RGB')
-    draw = ImageDraw.Draw(img)
+# Define the annotation types
+ANNOTATION_TYPES = ['rect', 'circle']
 
-    # Draw any existing annotations on the image
-    for annotation in annotations:
-        x1, y1, x2, y2 = annotation['box']
-        draw.rectangle([x1, y1, x2, y2], outline='red', width=2)
+# Define the Annotation class
+class Annotation:
+    def __init__(self, x, y, width, height, annotation_type):
+        self.x = x
+        self.y = y
+        self.width = width
+        self.height = height
+        self.type = annotation_type
 
-    # Return the annotated image as a numpy array
-    return img
+# Define the Gradio interface
+def annotate_images(images):
+    # Define the canvas size
+    canvas_size = (600, 600)
 
-# Define the function that will save the annotations to a file
-def save_annotations(annotations):
-    with open('annotations.json', 'w') as f:
-        json.dump(annotations, f)
+    # Define the initial state
+    state = {
+        'image': None,
+        'annotations': [],
+        'annotation_type': ANNOTATION_TYPES[0],
+        'start_point': None
+    }
 
-# Define the Gradio interface
-inputs = [
-    gr.inputs.Image(type='numpy', label='Input Image'),
-    gr.inputs.Textbox(type='json', label='Annotations', default='[]')
-]
+    # Define the canvas drawing function
+    def draw_canvas(canvas, image_data, annotations):
+        # Convert the image data to a PIL Image object
+        image = Image.fromarray(image_data)
+
+        # Resize the image to fit the canvas
+        image = image.resize(canvas_size)
+
+        # Draw the image on the canvas
+        canvas.draw_image(image, (canvas_size[0]/2, canvas_size[1]/2))
+
+        # Draw the annotations on the canvas
+        for annotation in annotations:
+            x, y, width, height = annotation.x, annotation.y, annotation.width, annotation.height
+            if annotation.type == 'rect':
+                canvas.draw_rect(x, y, width, height, stroke_color='red')
+            elif annotation.type == 'circle':
+                radius = np.sqrt(np.power(width, 2) + np.power(height, 2)) / 2
+                center_x, center_y = x + width / 2, y + height / 2
+                canvas.draw_circle(center_x, center_y, radius, stroke_color='red')
+
+    # Define the canvas mousedown event handler
+    def canvas_mousedown(canvas, x, y):
+        state['start_point'] = (x, y)
+
+    # Define the canvas mousemove event handler
+    def canvas_mousemove(canvas, x, y):
+        if state['start_point'] is not None:
+            start_x, start_y = state['start_point']
+            end_x, end_y = x, y
+            annotation_type = state['annotation_type']
+            draw_annotation(canvas, start_x, start_y, end_x, end_y, annotation_type)
 
-def predict(input_image, annotations):
-    # Parse the annotations from the input string
-    annotations = json.loads(annotations)
+    # Define the canvas mouseup event handler
+    def canvas_mouseup(canvas, x, y):
+        if state['start_point'] is not None:
+            start_x, start_y = state['start_point']
+            end_x, end_y = x, y
+            annotation_type = state['annotation_type']
+            add_annotation(start_x, start_y, end_x, end_y, annotation_type)
+            state['start_point'] = None
 
-    # Annotate the image
-    output_image = annotate_image(input_image, annotations)
+    # Define the add annotation function
+    def add_annotation(start_x, start_y, end_x, end_y, annotation_type):
+        # Calculate the width and height of the annotation
+        width = np.abs(start_x - end_x)
+        height = np.abs(start_y - end_y)
 
-    # Save the annotations to a file
-    save_annotations(annotations)
+        # Create the annotation object
+        annotation = Annotation(start_x, start_y, width, height, annotation_type)
 
-    # Return the annotated image as a numpy array
-    return output_image
+        # Add the annotation to the array
+        state['annotations'].append(annotation)
 
-outputs = [
-    gr.outputs.Image(type='numpy', label='Output Image')
-]
+        # Redraw the canvas
+        draw_canvas(canvas, state['image'], state['annotations'])
 
-gr.Interface(predict, inputs, outputs, title='Image Annotator', 
-             description='Annotate images using bounding boxes').launch()
+    #