From 046482d474287520ae0f8b0e022ae3c922684311 Mon Sep 17 00:00:00 2001
From: Guillaume-Duret <guillaume.duret@ec-lyon.fr>
Date: Fri, 19 May 2023 18:48:04 +0200
Subject: [PATCH] script for eval
---
compute_seg_result.py | 43 +++++++++++++++++++++++++++++++++++++++++++
1 file changed, 43 insertions(+)
create mode 100644 compute_seg_result.py
diff --git a/compute_seg_result.py b/compute_seg_result.py
new file mode 100644
index 0000000..29d600f
--- /dev/null
+++ b/compute_seg_result.py
@@ -0,0 +1,43 @@
+from ultralytics import YOLO
+import cv2
+import matplotlib.pyplot as plt
+import argparse
+import os
+
+# Create the parser
+parser = argparse.ArgumentParser()
+# Add an argument
+parser.add_argument('--path_evaluation', type=str, required=True)
+parser.add_argument('--path_result', type=str, required=True)
+parser.add_argument('--class_object', type=str, required=True)
+parser.add_argument('--path_model_yolo', type=str, required=True)
+# Parse the argument
+args = parser.parse_args()
+
+
+# load the model.
+model = YOLO(f"{args.path_model_yolo}")
+#
+
+path_evaluation_data = args.path_evaluation + "/" + args.class_object + "/RGB_resized"
+
+for files in os.listdir(path_evaluation_data):
+ #print("files : ", files)
+ #print(f"{path_evaluation_data}/{files}")
+
+ try:
+ results = model.predict(source=f"{path_evaluation_data}/{files}", conf=0.5,
+save=True)
+ #print(results)
+ results1 = results[0].to('cpu')
+ results11 = results1.numpy()
+ # boxes = results11.boxes # Boxes object for bbox outputs
+ # probs = results11.probs # Class probabilities for classification outputs
+#
+ masks = results11.masks # Masks object for segmentation masks outputs
+ #print(masks)
+ mask_res = masks.data[0]
+ plt.imsave(f"{args.path_result}/{files}", mask_res, cmap='gray')
+ print("images saved : ", files )
+ except :
+ print("no prediction")
--
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