Update app.py

app.py

@@ -1,137 +1,12 @@
-import supervision as sv
-from ultralytics import YOLO
-from PIL import Image
 import gradio as gr
-import numpy as np
-import cv2
-import urllib
+from ImageAnnotate import image_annotate
+from PoseAnnotate import pose_annotate
 
-# load pre-trained vision model
-model = YOLO("yolo12s.pt")
-
-def image_annotate(image:str, annotator:str) -> Image.Image:
-  """
-  Args:
-    image: the path to the image file
-    annotator: the type of annotator to use
-  Returns:
-    annotated image
-  """
-  # load the input image
-  image = cv2.imread(image)
-
-  # run object detection on the image
-  result = model(image)[0]
-
-  # convert YOLO output to a Supervision-compatible detections format
-  detections = sv.Detections.from_ultralytics(result)
-
-  annotated_image_show = None
-    
-  # select annotator
-  if annotator == "Box":
-    box_annotator = sv.BoxAnnotator()
-    annotated_image_show = box_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  elif annotator == "Roundbox":
-    round_box_annotator = sv.RoundBoxAnnotator()
-    annotated_image_show = round_box_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  elif annotator == "Boxcorner":
-    corner_annotator = sv.BoxCornerAnnotator()
-    annotated_image_show = corner_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  elif annotator == "Color":
-    color_annotator = sv.ColorAnnotator()
-    annotated_image_show = color_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  elif annotator == "Circle":
-    circle_annotator = sv.CircleAnnotator()
-    annotated_image_show = circle_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  elif annotator == "Dot":
-    dot_annotator = sv.DotAnnotator()
-    annotated_image_show = dot_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  elif annotator == "Triangle":
-    triangle_annotator = sv.TriangleAnnotator()
-    annotated_image_show = triangle_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  elif annotator == "Ellipse":
-    ellipse_annotator = sv.EllipseAnnotator()
-    annotated_image_show = ellipse_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  elif annotator == "Percentage":
-    percentage_bar_annotator = sv.PercentageBarAnnotator()
-    annotated_image_show = percentage_bar_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  elif annotator == "Heatmap":
-    heatmap_annotator = sv.HeatMapAnnotator()
-    annotated_image_show = heatmap_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  elif annotator == "Label":
-    labels = [
-    f"{class_id} {confidence:.2f}"
-    for class_id, confidence
-    in zip(detections.class_id, detections.confidence)
-    ]
-
-    rich_label_annotator = sv.RichLabelAnnotator(
-        text_color=sv.Color.BLACK,
-        text_padding=10,
-        text_position=sv.Position.CENTER)
-
-    annotated_image_show = rich_label_annotator.annotate(
-        scene=image.copy(),
-        detections=detections,
-        labels=labels )
-
-  elif annotator == "Blur":
-    blur_annotator = sv.BlurAnnotator()
-    annotated_image_show = blur_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  elif annotator == "Pixelate":
-    pixelate_annotator = sv.PixelateAnnotator()
-    annotated_image_show = pixelate_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  elif annotator == "Backgroundcolor":
-    background_overlay_annotator = sv.BackgroundOverlayAnnotator()
-    annotated_image_show = background_overlay_annotator.annotate(
-    scene=image.copy(),
-    detections=detections)
-
-  # return annotated image
-  return annotated_image_show
-
-app = gr.Interface(
+objectDetector = gr.Interface(
     fn = image_annotate,
     theme = "gstaff/sketch",
     title="Object Detection",
-    inputs = [gr.Image(type="filepath",label="Image"),gr.Radio(label="Select Annotator",
+    inputs = [gr.Image(type="filepath",label="Image"), gr.Radio(label="Select Annotator",
                                                  choices=["Box","Roundbox","Boxcorner","Color","Circle","Dot","Triangle",
                                                           "Ellipse","Percentage","Heatmap","Label","Blur",
                                                           "Pixelate","Backgroundcolor"],
@@ -145,5 +20,16 @@ app = gr.Interface(
                 ["bowling_ball.jpg","Percentage"]]
 )
 
+poseDetector = gr.Interface(
+    fn = pose_annotate,
+    title="Pose Detection",
+    inputs = [gr.Image(type="filepath",label="Image"), gr.Radio(label="Select Annotator",
+                                                 choices=["Vertex","Edge","Vertexlabel"],
+                                                 value = "Vertex")],
+    outputs = gr.Image(label = "Annotated Image"),
+)
+
+app = gr.TabbedInterface([objectDetector,poseDetector],["Object Detection","Pose Detection"])
+
 if __name__ == "__main__":
   app.launch(mcp_server = True)