Merge branch 'gustavovaliati-master'

extra/README.md

@@ -13,6 +13,16 @@
     3) Insert images into **images/**
     4) Run the python script: `python convert_gt_yolo.py`
 
+- ### convert keras-yolo3 to our format:
+
+    1) Add or update the class list to the file `class_list.txt`
+    2) Use the parameter `--gt` to set the **ground-truth** source.
+    3) Run the python script: `python3 convert_keras-yolo3.py --gt <gt_file_path>`
+        1) Supports only python 3.
+        2) This code can handle recursive annotation structure. Just use the `-r` parameter.
+        3) The converted annotation is placed by default in a new from_kerasyolo3 folder. You can change that with the parameter `-o`.
+        4) The format is defined according with github.com/qqwweee/keras-yolo3
+
 ## Predicted:
 - ### convert darkflow `json` to our format:
 
@@ -26,6 +36,16 @@
     3) Insert images into **images/**
     4) Run the python script: `python convert_pred_yolo.py`
 
+- ### convert keras-yolo3 to our format:
+
+    1) Add or update the class list to the file `class_list.txt`
+    2) Use the parameter `--predicted` to set the **prediction** source.
+    3) Run the python script: `python3 convert_keras-yolo3.py --pred <prediction_file_path>`
+        1) Supports only python 3.
+        2) This code can handle recursive annotation structure. Just use the `-r` parameter.
+        3) The converted annotation is placed by default in a new from_kerasyolo3 folder. You can change that with the parameter `-o`.
+        4) The format is defined according with github.com/gustavovaliati/keras-yolo3
+
 ## Remove specific char delimiter from files
 
 E.g. remove `;` from:

extra/convert_keras-yolo3.py

@@ -0,0 +1,88 @@
+'''
+ABOUT THIS SCRIPT:
+Converts ground-truth from the annotation files
+according to the https://github.com/qqwweee/keras-yolo3
+or https://github.com/gustavovaliati/keras-yolo3 format.
+
+And converts the predicitons from the annotation files
+according to the https://github.com/gustavovaliati/keras-yolo3 format.
+'''
+
+import argparse
+import datetime
+import os
+
+'''
+Each time this script runs, it saves the output in a different path
+controlled by the following folder suffix: annotation_version.
+'''
+annotation_version = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
+
+ap = argparse.ArgumentParser()
+
+ap.add_argument("-o", "--output_path",
+                required=False,
+                default='from_kerasyolo3/version_{}'.format(annotation_version),
+                type=str,
+                help="The dataset root path location.")
+ap.add_argument("-r", "--gen_recursive",
+                required=False,
+                default=False,
+                action="store_true",
+                help="Define if the output txt files will be placed in a \
+                recursive folder tree or to direct txt files.")
+group = ap.add_mutually_exclusive_group(required=True)
+group.add_argument('--gt',
+    type=str,
+    default=None,
+    help="The annotation file that refers to ground-truth in (keras-yolo3 format)")
+group.add_argument('--pred',
+    type=str,
+    default=None,
+    help="The annotation file that refers to predictions in (keras-yolo3 format)")
+
+ARGS = ap.parse_args()
+
+with open('class_list.txt', 'r') as class_file:
+    class_map = class_file.readlines()
+print(class_map)
+annotation_file = ARGS.gt if ARGS.gt else ARGS.pred
+
+os.makedirs(ARGS.output_path, exist_ok=True)
+
+with open(annotation_file, 'r') as annot_f:
+    for annot in annot_f:
+        annot = annot.split(' ')
+        img_path = annot[0].strip()
+        if ARGS.gen_recursive:
+            annotation_dir_name = os.path.dirname(img_path)
+            # remove the root path to enable to path.join.
+            if annotation_dir_name.startswith('/'):
+                annotation_dir_name = annotation_dir_name.replace('/', '', 1)
+            destination_dir = os.path.join(ARGS.output_path, annotation_dir_name)
+            os.makedirs(destination_dir, exist_ok=True)
+            # replace .jpg with your image format.
+            file_name = os.path.basename(img_path).replace('.jpg', '.txt')
+            output_file_path = os.path.join(destination_dir, file_name)
+        else:
+            file_name = img_path.replace('.jpg', '.txt').replace('/', '__')
+            output_file_path = os.path.join(ARGS.output_path, file_name)
+            os.path.dirname(output_file_path)
+
+        with open(output_file_path, 'w') as out_f:
+            for bbox in annot[1:]:
+                if ARGS.gt:
+                    # Here we are dealing with ground-truth annotations
+                    # <class_name> <left> <top> <right> <bottom> [<difficult>]
+                    # todo: handle difficulty
+                    x_min, y_min, x_max, y_max, class_id = list(map(float, bbox.split(',')))
+                    out_box = '{} {} {} {} {}'.format(
+                        class_map[int(class_id)].strip(), x_min, y_min, x_max, y_max)
+                else:
+                    # Here we are dealing with predictions annotations
+                    # <class_name> <confidence> <left> <top> <right> <bottom>
+                    x_min, y_min, x_max, y_max, class_id, score = list(map(float, bbox.split(',')))
+                    out_box = '{} {} {} {} {} {}'.format(
+                        class_map[int(class_id)].strip(), score,  x_min, y_min, x_max, y_max)
+
+                out_f.write(out_box + "\n")