Added Detectron2

feet_detection.py

@@ -0,0 +1,33 @@
+import torch
+import cv2
+
+# import some common detectron2 utilities
+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, DatasetCatalog
+
+if __name__ == '__main__':
+ print(torch.__version__, torch.cuda.is_available())
+
+ video = cv2.VideoCapture("resources/Short4Mosaicing.mp4")
+
+ while video.isOpened():
+ ok, frame = video.read()
+ if ok:
+ frame = frame[320:, :]
+
+ # Inference with a keypoint detection model
+ cfg = get_cfg() # get a fresh new config
+ cfg.merge_from_file(model_zoo.get_config_file("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml"))
+ cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7 # set threshold for this model
+ cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml")
+ predictor = DefaultPredictor(cfg)
+ outputs = predictor(frame)
+ v = Visualizer(frame[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
+ out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
+ cv2.imshow("image", out.get_image()[:, :, ::-1])
+
+ k = cv2.waitKey(5) & 0xff
+ if k == 27: break