new

app.py

@@ -1,7 +1,44 @@
+import numpy as np
 import gradio as gr
+import cv2
+from detectron2 import model_zoo
+from detectron2.engine import DefaultPredictor
+from detectron2.config import get_cfg
+from detectron2.utils.visualizer import Visualizer
+from detectron2.data import MetadataCatalog
 
-def greet(name):
- return "Hello " + name + "!!"
+# Setup detectron2 logger
+import detectron2
+from detectron2.utils.logger import setup_logger
+setup_logger()
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+def detect_objects(input_img):
+ # Load image
+ im = input_img.copy()
+
+ # Configuration
+ cfg = get_cfg()
+ cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
+ cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
+ cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
+
+ # Prediction
+ predictor = DefaultPredictor(cfg)
+ outputs = predictor(im)
+
+ # Visualization
+ v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.9)
+ out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+ result_image = out.get_image()[:, :, ::-1]
+
+ return result_image
+
+# Interface
+image = gr.Image()
+output_image = gr.Image()
+
+title = "Object Detection using Mask R-CNN"
+description = "This app detects objects in the input image using Mask R-CNN."
+examples = [["./input.png"]]
+
+gr.Interface(detect_objects, [image], output_image, title=title, description=description, examples=examples).launch()