Create app.py

app.py

@@ -0,0 +1,48 @@
+from PIL import Image, ImageDraw,ImageFont
+import gradio as gr
+import torch
+from transformers import pipeline
+
+modelname= "SenseTime/deformable-detr-with-box-refine"
+modelpath = 'models/models--SenseTime--deformable-detr-with-box-refine/snapshots/2e9e461623a8fdc296e19666c46c8a4389a3a6fe'
+
+def draw_bounding_boxes(image, detections, color=(0, 255, 0), thickness=2, font_path=None, font_size=12):
+  draw = ImageDraw.Draw(image)
+  for detection in detections:
+    xmin, ymin, xmax, ymax = detection['box']['xmin'], detection['box']['ymin'], detection['box']['xmax'], detection['box']['ymax']
+    draw.rectangle(((xmin, ymin), (xmax, ymax)), outline=color, width=thickness)
+    if font_path:
+      try:
+        font = ImageFont.truetype(font_path, font_size)
+        label_text = f"{detection['label']}: {detection['score']:.2f}"
+        text_width, text_height = draw.textsize(label_text, font=font)
+        draw.rectangle(((xmin, ymin), (xmin + text_width + 5, ymin + text_height + 5)), fill=(0, 0, 0, 0.5))  # Semi-transparent black background
+        draw.text((xmin, ymin), label_text, fill=color, font=font)
+      except (IOError, OSError):
+        print(f"Warning: Could not load font '{font_path}'. Labels will not be drawn.")
+  return image
+
+
+def hf_pipeline(model_name=None,model_path=None):
+    model = model_path if model_name == None else model_name
+    print(f"=============model: {model} =============")
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    image_detector = pipeline("object-detection", model=model,device=device)
+    return image_detector
+
+
+def detect_image_withbox(image):
+    obj_detector = hf_pipeline(modelname)
+    detections = obj_detector(image)
+    image_with_boxes = draw_bounding_boxes(image.copy(), detections)
+    print(detections)
+    return image_with_boxes
+
+
+
+demo = gr.Interface(fn=detect_image_withbox,
+                    inputs=[gr.Image(label="Select Image",type="pil")],
+                    outputs=[gr.Image(label="Processed Image With Boxes", type="pil")],
+                    title="@SmartChoiceLearningHub HF Project 2 : Object Detector With Box",
+                    description="This app detects objects in an image and draws bounding boxes around them.")
+demo.launch()