Update process_automated_droplet.py

classes/droplet/process_automated_droplet.py

@@ -43,6 +43,8 @@ name_result_file = "test"
 weights_path = r"test"
 # MRCNN model name to find in "Mask_R_CNN\models\
 weights_name = r"test"
+# file format of images
+file_format = "jpg"
 # save n-th result image 
 save_nth_image = 1  
 # pixel size in [µm/px]. To read from Sopat log file enter pixelsize = 0
@@ -152,6 +154,7 @@ if cluster is False:
     IMAGE_CROP = image_crop
     CONTRAST = contrast
     CONFIDENCE = confidence
+    FILE_FORMAT = file_format
 else:
     import argparse
     # Parse command line arguments
@@ -167,6 +170,8 @@ else:
                         help='Weights path to find in Mask_R_CNN\models')
     parser.add_argument('--weights_name', required=True,
                         help='Choose Neuronal Network / Epoch to find in Mask_R_CNN\models')
+    parser.add_argument('--file_format', required=True,
+                        help='')
     parser.add_argument('--masks', required=False, type=str,
                         default="False",
                         help='Generate detection masks?')
@@ -237,6 +242,7 @@ else:
     DATASET_DIR = os.path.join(ROOT_DIR, "datasets/input", args.dataset_path)
     SAVE_DIR = os.path.join(ROOT_DIR, "datasets/output", args.save_path)
     EXCEL_DIR = os.path.join(SAVE_DIR, args.name_result_file + '.xlsx')
+    FILE_FORMAT = args.file_format
     if args.detect_oval_droplets == "True":
         DETECT_OVAL_DROPLETS = True
     elif args.detect_oval_droplets == "False":
@@ -286,7 +292,7 @@ Path(SAVE_DIR).mkdir(parents=True, exist_ok=True)
 
 ### mean pixel detectieren
 images_mean_pixel = []
-images_path = glob.glob(DATASET_DIR + "/*.jpg")
+images_path = glob.glob(DATASET_DIR + "/*." + FILE_FORMAT)
 for img_path in images_path:
     img = cv2.imread(img_path)
     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)