visualization changes

howto_tf

@@ -14,6 +14,13 @@ python object_detection/train.py \
     --pipeline_config_path=/home/gustav/workspace/deeptraining_hands/model/ssd_mobilehandsnet_train.config \
     --train_dir=/home/gustav/workspace/deeptraining_hands/model/train
 
+python object_detection/train.py \
+    --logtostderr \
+    --pipeline_config_path=/home/gustav/workspace/deeptraining_hands/model/frcnni_handsnet.config \
+    --train_dir=/home/gustav/workspace/deeptraining_hands/model/train
+
+
+
 ## EVALUATION TENSORFLOW
 # From the tensorflow/models/research directory
 python object_detection/eval.py \
@@ -22,6 +29,14 @@ python object_detection/eval.py \
     --checkpoint_dir=/home/gustav/workspace/deeptraining_hands/model/train \
     --eval_dir=/home/gustav/workspace/deeptraining_hands/model/eval
 
+python object_detection/eval.py \
+    --logtostderr \
+    --pipeline_config_path=/home/gustav/workspace/deeptraining_hands/model/frcnni_handsnet.config \
+    --checkpoint_dir=/home/gustav/workspace/deeptraining_hands/model/train \
+    --eval_dir=/home/gustav/workspace/deeptraining_hands/model/eval
+
+
+
 ## TENSORBOARD
 tensorboard --logdir=/home/gustav/workspace/deeptraining_hands/model
 

model/ssd_mobilehandsnet_train.config

@@ -181,7 +181,7 @@ train_input_reader: {
 }
 
 eval_config: {
-  num_examples: 1
+  num_examples: 821
   eval_interval_secs: 600
   # Note: The below line limits the evaluation process to 10 evaluations.
   # Remove the below line to evaluate indefinitely.

xml_to_csv.py

@@ -7,13 +7,15 @@
 import glob
 import pandas as pd
 import xml.etree.ElementTree as ET
+import cv2
 
 
-def xml_to_csv(path):
+def xml_to_csv(xml_path,img_path):
     xml_list = []
-    for xml_file in glob.glob(path + '/*.xml'):
+    for xml_file in glob.glob(xml_path + '/*.xml'):
         tree = ET.parse(xml_file)
         root = tree.getroot()
+        boxes = []
         for member in root.findall('object'):
             value = (root.find('filename').text,
                      int(root.find('size')[0].text),
@@ -25,17 +27,36 @@ def xml_to_csv(path):
                      int(member[4][3].text)
                      )
             xml_list.append(value)
+            boxes.append(list(value[4:]))
+            name = str(value[0])
+
+        image = img_path + '/' + name
+        visualize(image,boxes,name,1)
+
     column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax']
     xml_df = pd.DataFrame(xml_list, columns=column_name)
     return xml_df
 
+def visualize(image,boxes,text,time):
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    img = cv2.imread(image)
+
+    for box in boxes:
+        xmin, ymin, xmax, ymax = box
+        cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
+
+    cv2.putText(img, text, (0,20), font, 0.75, (77, 255, 9), 2)
+    cv2.imshow('label_check', img)
+    cv2.waitKey(time)
 
 def main():
     for directory in ['train','eval']:
         xml_path = os.path.join(os.getcwd(), 'data/{}/annotations/xml'.format(directory))
-        xml_df = xml_to_csv(xml_path)
+        img_path = os.path.join(os.getcwd(), 'data/{}/images'.format(directory))
+        xml_df = xml_to_csv(xml_path,img_path)
         xml_df.to_csv('data/{}_labels.csv'.format(directory,directory), index=None)
         print('Successfully converted {} xml to csv.').format(directory)
+        cv2.destroyAllWindows()
 
 
 if __name__ == '__main__':