From 16cc08528cd725d75e873b46258c53396c54a48e Mon Sep 17 00:00:00 2001
From: Tetiana Yemelianenko <tyemel.mzeom@gmail.com>
Date: Tue, 17 Sep 2024 14:10:22 +0000
Subject: [PATCH] Upload New File
---
annotate_with_yolo.py | 57 +++++++++++++++++++++++++++++++++++++++++++
1 file changed, 57 insertions(+)
create mode 100644 annotate_with_yolo.py
diff --git a/annotate_with_yolo.py b/annotate_with_yolo.py
new file mode 100644
index 0000000..27c7afe
--- /dev/null
+++ b/annotate_with_yolo.py
@@ -0,0 +1,57 @@
+#predict
+from ultralytics import YOLO
+from PIL import Image
+import pandas as pd
+from glob import glob
+import os
+
+model = YOLO('path_to_the_finetuned_yolo8.pt')
+
+parentpath = 'path_to_the_images_annotated_with_owl'
+i = 0
+ext = '.jpg'
+imgPaths = glob(parentpath + "/*" + ext)
+
+for fname in imgPaths:
+ try:
+ raw_image = Image.open(fname)
+ except:
+ continue
+
+ txtfile = fname.replace("jpg", "txt")
+ txtfile = txtfile.replace("images", "labels")
+ with open(txtfile, "r") as f:
+ lines = f.readlines()
+ current_classes = []
+ for line in lines:
+ current_classes.append(int(line[0]))
+
+ results=model.predict(source=raw_image, save=True)
+
+ a=results[0].boxes.data
+ a = a.detach().cpu().numpy()
+ px=pd.DataFrame(a).astype("float")
+
+ height, width = results[0].orig_shape
+
+ lines=[]
+
+ for index,row in px.iterrows():
+ x1 = row[0]
+ y1 = row[1]
+ x2 = row[2]
+ y2 = row[3]
+ d = int(row[5])
+ xcen = float((x1 + x2)) / 2 / width
+ ycen = float((y1 + y2)) / 2 / height
+ w = float((x2 - x1)) / width
+ h = float((y2 - y1)) / height
+ if d not in current_classes:
+ lines.append([d, xcen, ycen, w, h])
+ if os.path.exists(txtfile):
+ with open(txtfile, "a") as f:
+ for line in lines:
+ f.write("%d %.06f %.06f %.06f %.06f\n" % (line[0], line[1], line[2], line[3], line[4]))
+
+ i += 1
+ print(i)
--
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