# HG changeset patch
# User Laman
# Date 2019-05-15 16:26:02
# Node ID 4eb46a5b9c2bcd53c3352bec68e3ecc41f4ee01d
# Parent  247811dfb9beef1515ef63e2fc31eba7821f33c1

Sansa board detection module

diff --git a/.hgignore b/.hgignore
--- a/.hgignore
+++ b/.hgignore
@@ -1,5 +1,5 @@
 __pycache__/
 ^\.idea/
 ^images/
-^config.json$
+config.json$
 ftp.json$
diff --git a/exp/config.py b/exp/exp_config.py
rename from exp/config.py
rename to exp/exp_config.py
--- a/exp/config.py
+++ b/exp/exp_config.py
@@ -1,8 +1,17 @@
 import os
+import json
+import logging as log
 
 
+log.basicConfig(level=log.INFO,format="%(asctime)s %(levelname)s: %(message)s")
+thisDir=os.path.dirname(__file__)
+
+with open(os.path.join(thisDir,"config.json")) as f:
+	cfg=json.load(f)
+
 INTERACTIVE=False
 imgDir="/tmp/oneEye"
+sansaModel=cfg.get("sansaModel")
 i=1
 
 if not os.path.exists(imgDir):
diff --git a/exp/sansa.py b/exp/sansa.py
new file mode 100644
--- /dev/null
+++ b/exp/sansa.py
@@ -0,0 +1,88 @@
+import os
+import time
+import argparse
+import logging as log
+
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+from distutils.version import StrictVersion
+
+import exp_config as cfg
+
+if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
+	raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')
+
+detection_graph = tf.Graph()
+with detection_graph.as_default():
+	od_graph_def = tf.GraphDef()
+	with tf.gfile.GFile(cfg.sansaModel, 'rb') as fid:
+		serialized_graph = fid.read()
+		od_graph_def.ParseFromString(serialized_graph)
+		tf.import_graph_def(od_graph_def, name='')
+
+
+def load_image_into_numpy_array(image):
+	(im_width, im_height) = image.size
+	return np.array(image.getdata()).reshape(
+			(im_height, im_width, 3)).astype(np.uint8)
+
+
+def run_inference_for_single_image(image, graph):
+	with graph.as_default():
+		with tf.Session() as sess:
+			# Get handles to input and output tensors
+			ops = tf.get_default_graph().get_operations()
+			all_tensor_names = {output.name for op in ops for output in op.outputs}
+			tensor_dict = {}
+			for key in [
+					'num_detections', 'detection_boxes', 'detection_scores',
+					'detection_classes', 'detection_masks'
+			]:
+				tensor_name = key + ':0'
+				if tensor_name in all_tensor_names:
+					tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
+							tensor_name)
+			image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
+
+			# Run inference
+			output_dict = sess.run(tensor_dict,
+														 feed_dict={image_tensor: np.expand_dims(image, 0)})
+
+			# all outputs are float32 numpy arrays, so convert types as appropriate
+			output_dict['num_detections'] = int(output_dict['num_detections'][0])
+			output_dict['detection_classes'] = output_dict[
+					'detection_classes'][0].astype(np.uint8)
+			output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
+			output_dict['detection_scores'] = output_dict['detection_scores'][0]
+	return output_dict
+
+
+def extractBoard(image):
+	t1=time.time()
+	image_np=load_image_into_numpy_array(image)
+	output_dict=run_inference_for_single_image(image_np,detection_graph)
+
+	(width,height)=image.size
+	(y1,x1,y2,x2)=output_dict["detection_boxes"][0]
+	(w,h)=((x2-x1),(y2-y1))
+	x1=max(0,x1-0.1*w)
+	x2=min(1,x2+0.1*w)
+	y1=max(0,y1-0.1*h)
+	y2=min(1,y2+0.1*h)
+	goban=image.crop((x1*width,y1*height,x2*width,y2*height))
+	t=time.time()-t1
+	log.info("board detected in {0:.3}s".format(t))
+	return goban
+
+
+if __name__=="__main__":
+	parser=argparse.ArgumentParser()
+	parser.add_argument("-i","--input",nargs="+")
+	parser.add_argument("-o","--output_dir",required=True)
+	args=parser.parse_args()
+
+	for image_path in args.input:
+		image=Image.open(image_path)
+		goban=extractBoard(image)
+		goban.save(os.path.join(args.output_dir,os.path.basename(image_path)))