Create app.py

app.py

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+
+import os
+os.system("pip install torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 --index-url https://download.pytorch.org/whl/cpu")
+os.system('pip install git+https://github.com/facebookresearch/detectron2.git')
+os.system('pip install opencv-python-headless==4.8.1.78')
+
+import gradio as gr
+import cv2
+from detectron2 import model_zoo
+from detectron2.config import get_cfg
+from detectron2.engine import DefaultPredictor
+from detectron2.utils.visualizer import Visualizer
+from detectron2.utils.visualizer import ColorMode
+from detectron2.data import MetadataCatalog
+import numpy as np
+
+# Path to the trained model weights
+model_path = './model/keypoint_rcnn_X_101_32x8d_FPN_3x.pth'
+
+number_of_keypoints = 15
+
+# Setup the configuration for the model
+cfg = get_cfg()
+cfg.merge_from_file(model_zoo.get_config_file("COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml"))
+cfg.MODEL.DEVICE = 'cpu'
+cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
+cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
+cfg.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = number_of_keypoints
+cfg.TEST.KEYPOINT_OKS_SIGMAS = np.ones((number_of_keypoints, 1), dtype=float).tolist()
+
+# Load the trained model weights
+cfg.MODEL.WEIGHTS = model_path
+cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.6  # set a custom testing threshold
+predictor = DefaultPredictor(cfg)
+
+# Set metadata for visualization
+MetadataCatalog.get("spot").set(thing_classes=["wing"])
+metadata = MetadataCatalog.get("spot")
+
+
+def markin(image_path):
+    im = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
+    outputs = predictor(im)
+    v = Visualizer(im[:, :, ::-1],
+                   metadata=metadata,
+                   # scale=0.9,
+                   instance_mode=ColorMode.SEGMENTATION
+                   )
+    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+    return out.get_image()
+
+
+# Setup the Gradio interface
+demo = gr.Interface(markin,
+                    gr.Image(type="filepath", sources=['upload']),
+                    "image",
+                    examples=[
+                        os.path.join(os.path.dirname(__file__), "images/drosophila-wing-1.jpg"),
+                        os.path.join(os.path.dirname(__file__), "images/drosophila-wing-2.jpg"),
+                        os.path.join(os.path.dirname(__file__), "images/drosophila-wing-3.jpg"),
+                        os.path.join(os.path.dirname(__file__), "images/drosophila-wing-4.jpg"),
+                        os.path.join(os.path.dirname(__file__), "images/drosophila-wing-5.jpg")
+                    ],
+                    title='Drosophila wing landmarkin',
+                    description='Drosophila is a genus of small flies, commonly called fruit flies. These flies are widely used in scientific research, particularly in genetics and evolutionary biology, because they are easy to care for, reproduce rapidly, and have a short generation time. Measuring the wings of Drosophila is important in scientific research. Wing size and shape can vary among different Drosophila species and strains, and these differences can be used to study the genetic basis of wing development, evolution and other studies. <br> <a href="https://datamarkin.com/models/automated-measurement-of-drosophila-wings" class="navbar-item "> More about project </a>')
+
+if __name__ == "__main__":
+    demo.launch()