Merge pull request #40 from Lucs1590/master

Readme and CI/CD

.github/dependabot.yml

@@ -0,0 +1,11 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "daily"
+
+  - package-ecosystem: "pip"
+    directory: "/"
+    schedule:
+      interval: "weekly"

.github/workflows/greetings.yml

@@ -0,0 +1,16 @@
+name: Greetings
+
+on: [pull_request, issues]
+
+jobs:
+  greeting:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    steps:
+    - uses: actions/first-interaction@v1
+      with:
+        repo-token: ${{ secrets.GITHUB_TOKEN }}
+        issue-message: 'Thanks for contributing this issue! We will be replying soon.'
+        pr-message: 'Thanks for contributing this PR! We will validade soon.'

readme.md

@@ -3,25 +3,33 @@
 
 Are you tired to label your images by hand to work with object detection? Have hundreds or thousands of images to label? Then this project will make your life easier, just create some annotations and let the machine do the rest for you!
 
-## Requirements
+## Contents
+- [Requirements](#requirements)
+- [How to run](#usage)
+    - [TensorFlow < 2.x](#minor)
+    - [TensorFlow >= 2.x](#greater)
+- [Any trouble?](#trouble)
+
+
+## Requirements <a id="requirements"></a>
 - You will need to [clone the TensorFlow repository](https://github.com/tensorflow/models)
 - Install the [dependencies](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/tensorflow-1.14/install.html) for object detection
 
-## How to run
+## How to run <a id="usage"></a>
 - Copy and paste the files **generate_xml.py** and **visualization_utils.py** into the **research/object_detection/utils** in the tensorflow repo.
 - Change the xml path in generate_xml.py to put your own local path.
 - Add the images you want to label into the images folder
 
-### TensorFlow < 2.x
+### TensorFlow < 2.x <a id="minor"></a>
 - Add your pre-treined model (as a fronzen inference graph) and label map into the 'graphs' folder.
 - Inside the auto_annotate folder run: **python3 scripts/detection_images.py**
-### TensorFlow >= 2.x
+### TensorFlow >= 2.x <a id="greater"></a>
 - If you have TF 2.x, just run the following command:
 ```
 python3 scripts/detection_img_tf2.py --saved_model /path-saved-model --labelmap /path-label-map.pbtxt --imgs /path-of-the-imgs
 ```
 
 - If it runs correctly, you will see the inference results and the xml in your respective folders!
 
-## Any trouble?
+## Any trouble? <a id="trouble"></a>
 If you have trouble or doubt check my [tutorial on medium](https://medium.com/@alvaroleandrocavalcante/auto-annotate-images-for-tensorflow-object-detection-19b59f31c4d9?sk=0a189a8af4874462c1977c6f6738d759). You can also open an issue and I'll hep you!

scripts/visualization_utils.py

@@ -44,8 +44,6 @@
 matplotlib.use('Agg')  # pylint: disable=multiple-statements
 
 
-_TITLE_LEFT_MARGIN = 10
-_TITLE_TOP_MARGIN = 10
 STANDARD_COLORS = [
     'AliceBlue', 'Chartreuse', 'Aqua', 'Aquamarine', 'Azure', 'Beige', 'Bisque',
     'BlanchedAlmond', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite',
@@ -821,7 +819,8 @@ def visualize_boxes_and_labels_on_image_array(
                 if not skip_labels:
                     if not agnostic_mode:
                         if classes[i] in six.viewkeys(category_index):
-                            class_name.append(category_index[classes[i]]['name'])
+                            class_name.append(
+                                category_index[classes[i]]['name'])
                             new_xml = True
                         else:
                             class_name.append('N/A')
@@ -894,10 +893,11 @@ def visualize_boxes_and_labels_on_image_array(
                 color=color,
                 radius=line_thickness / 2,
                 use_normalized_coordinates=use_normalized_coordinates)
-    
+
     if new_xml != False:
-      xml = generate_xml.GenerateXml(array_position, im_width, im_height, class_name, file_name)
-      xml.gerenate_basic_structure()
+        xml = generate_xml.GenerateXml(
+            array_position, im_width, im_height, class_name, file_name)
+        xml.gerenate_basic_structure()
 
     return image