make watcher accept arguments,

README.org

@@ -20,13 +20,13 @@ The process is pretty simple:
   # build the training dataset
   docker-compose up --build --remove-orphans -d
   docker-compose exec python ./run
-
+  
   # run the training on your machine or collab
   # https://colab.research.google.com/drive/10R7uwVJJ1R1k6oTjbkkhxPDka7COK-WE
   git clone https://github.com/ultralytics/yolov5  # clone repo
   pip install -U -r yolov5/requirements.txt  # install dependencies
   python3 yolov5/train.py --img 416 --batch 80 --epochs 100 --data ./ia/data.yaml  --cfg ./ia/yolov5s.yaml --weights ''
-
+  
 #+end_src
 
 * research
@@ -38,10 +38,10 @@ https://github.com/Hyuto/yolov5-tfjs
 there were a lot of augmentation solutions out there, because it had better
 piplines and multicore support we went with:
  - https://github.com/aleju/imgaug
-
+   
 but leaving the other here for refs
  - https://github.com/srp-31/Data-Augmentation-for-Object-Detection-YOLO-
- - https://github.com/mdbloice/Augmentor
+ - https://github.com/mdbloice/Augmentor 
 
 ** proveedores
 http://www.bcra.gov.ar/SistemasFinancierosYdePagos/Proveedores-servicios-de-pago-ofrecen-cuentas-de-pago.asp

detect.js

@@ -4,7 +4,7 @@ let run = () => {
     // https://developer.chrome.com/extensions/match_patterns
     var ALL_SITES = { urls: ['<all_urls>'] }
 
-    // Mozilla doesn't use tlsInfo in extraInfoSpec
+    // Mozilla doesn't use tlsInfo in extraInfoSpec 
     var extraInfoSpec = ['blocking'];
 
     // https://developer.mozilla.org/en-US/Add-ons/WebExtensions/API/webRequest/onHeadersReceived

python/imtool.py

@@ -131,7 +131,7 @@ def cut_logo(im, l):
 
 def add_alpha(img):
     b, g, r = cv2.split(img)
-    a = np.ones(b.shape, dtype=b.dtype) * 50
+    a = np.ones(b.shape, dtype=b.dtype) * 50 
     return cv2.merge((b,g,r,a))
 
 def remove_white(img):

python/watcher.py

@@ -1,22 +1,45 @@
 import os
 import inotify.adapters
+
+import augment
 from imtool import read_bounding_boxes, crop
+from common import defaults, mkdir
+
+def handle_png(event):
+    (_, type_names, path, filename) = event
+
+    bbs = read_bounding_boxes(os.path.join(path, filename.replace('.png', '.txt')))
+    crop(os.path.join(path, filename), bbs)
+
+def handle_csv(event):
+    (_, type_names, path, filename) = event
+
+    print('csv changed, will run vendor')
+    import vendor
+    vendor.from_csv(os.path.join(path, filename))
+    augment.process()
+
+handlers = {
+    '.png': handle_png,
+    '.csv': handle_csv
+}
 
-def watch(dir):
-    seen = 0
+def watch(dirs):
     i = inotify.adapters.Inotify()
-    i.add_watch(dir)
+    [i.add_watch(d) for d in dirs]
+    print(f'watching {dirs}')
     for event in i.event_gen(yield_nones=False):
         (_, type_names, path, filename) = event
 
-        if filename.endswith(".png") and type_names[0] in ['IN_CLOSE_WRITE']:
-            seen += 1
-            print(f"{seen} PATH=[{path}] FILENAME=[{filename}] EVENT_TYPES={type_names}")
-            try:
-                bbs = read_bounding_boxes(os.path.join(path, filename.replace('.png', '.txt')))
-                crop(os.path.join(path, filename), bbs)
-            except Exception as e:
-                print(f"error: {e}")
+        for k in handlers.keys():
+            if filename.endswith(k) and type_names[0] in ['IN_CLOSE_WRITE']:
+                print(f"PATH=[{path}] FILENAME=[{filename}] EVENT_TYPES={type_names}")
+                try:
+                    handlers[k](event)
+                except Exception as e:
+                    print(f'Error in {k} handler: {e}')
 
 if __name__ == '__main__':
-    watch('./data')
+    dirs = ['./data', defaults.IMAGES_PATH, defaults.AUGMENTED_IMAGES_PATH]
+    mkdir.make_dirs(dirs)
+    watch(dirs)

python/write_data.py

@@ -20,7 +20,7 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='creates a YOLOv5 data.yaml')
     parser.add_argument('csv', metavar='csv', type=str, 
                     help='csv file')
-    parser.add_argument('--data', metavar='data', type=str,
+    parser.add_argument('--data', metavar='data', type=str, 
                         help='data path', default='../data')
     args = parser.parse_args()
     bcos =  entity.read_entities(args.csv)