From 9d96b4c7de4e58e1c6f1cc5fba490b7b52f808fb Mon Sep 17 00:00:00 2001
From: MathieuKoz <127348750+MathieuKoz@users.noreply.github.com>
Date: Thu, 20 Jun 2024 21:41:13 +0200
Subject: [PATCH] correction vue
---
supervised_learning/vue.py | 31 ++++++++++++++++++++++++++-----
1 file changed, 26 insertions(+), 5 deletions(-)
diff --git a/supervised_learning/vue.py b/supervised_learning/vue.py
index a02e4dd..083c03c 100644
--- a/supervised_learning/vue.py
+++ b/supervised_learning/vue.py
@@ -1,4 +1,3 @@
-from supervised_learning.detection import *
from supervised_learning.config import *
from supervised_learning.utils import *
from PIL import Image, ImageDraw, ImageFont, ImageColor
@@ -6,8 +5,30 @@ import os
import csv
import random
import matplotlib.pyplot as plt
+import argparse
+
+# ------------- IMPORT PARSER & ARGS ---------------
+
+parser = argparse.ArgumentParser()
+parser.add_argument('img_folder', metavar='DIR', help='Folder of input images to analyse')
+parser.add_argument('pred_folder', metavar='DIR', help='Folder of predicted labels from the analysis')
+
+# Analyser les arguments de la ligne de commande
+args = parser.parse_args()
+
+
+# Load input folder
+img_folder = args.img_folder
+# Check if input folder exists
+if not os.path.exists(img_folder):
+ raise FileNotFoundError(f"[Error] Folder '{img_folder}' does not exist.")
+
+# Load input folder
+pred_folder = args.pred_folder
+# Check if input folder exists
+if not os.path.exists(pred_folder):
+ raise FileNotFoundError(f"[Error] Folder '{pred_folder}' does not exist.")
-#detecteur(VAL_IMAGE_FOLDER_PATH, PREDICTION_LABEL_FOLDER_PATH)
def calculate_text_size(text, font):
# calculate text size based on font properties
@@ -80,9 +101,9 @@ fig, axs = plt.subplots(2, 5, figsize=(12, 6))
for ax in axs.ravel():
#images_dir = os.path.join("train", "images")
#labels_dir = os.path.join("train", "labels")
- image_name = random.choice(os.listdir(VAL_IMAGE_FOLDER_PATH))
- image_path = os.path.join(VAL_IMAGE_FOLDER_PATH, image_name)
- csv_path = os.path.join(PREDICTION_LABEL_FOLDER_PATH, image_name[:-4] + ".csv")
+ image_name = random.choice(os.listdir(img_folder))
+ image_path = os.path.join(img_folder, image_name)
+ csv_path = os.path.join(pred_folder, image_name[:-4] + ".csv")
# Call visualize_image function to modify the image
image_to_display = visualize_image(image_path, csv_path)
--
GitLab