Clean up wording

Test should be reserved for actually testing (i.e. pytest) - images
should be stored in a more intuitively named folder and references to
"test" should be changed to "predict" where appropirate.

.gitignore

@@ -7,6 +7,7 @@ bin/
 
 # Test data
 test/*.jpg
+!test/sample_*.jpg
 test/out/*.jpg
 
 # Annotated test results

README.md

@@ -97,16 +97,16 @@ First, let's take a closer look at one of a very useful option `--load`
 # this will print out which layers are reused, which are initialized
 ```
 
-All input images from default folder `test/` are flowed through the net and predictions are put in `test/out/`. We can always specify more parameters for such forward passes, such as detection threshold, batch size, test folder, etc.
+All input images from default folder `sample_img/` are flowed through the net and predictions are put in `sample_img/out/`. We can always specify more parameters for such forward passes, such as detection threshold, batch size, images folder, etc.
 
 ```bash
-# Forward all images in test/ using tiny yolo and 100% GPU usage
-./flow --test test/ --model cfg/yolo-tiny.cfg --load bin/yolo-tiny.weights --gpu 1.0
+# Forward all images in sample_img/ using tiny yolo and 100% GPU usage
+./flow --imgdir sample_img/ --model cfg/yolo-tiny.cfg --load bin/yolo-tiny.weights --gpu 1.0
 ```
-json output can be generated with descriptions of the pixel location of each bounding box and the pixel location. Each prediction is stored in the `test/out` folder by default. An example json array is shown below.
+json output can be generated with descriptions of the pixel location of each bounding box and the pixel location. Each prediction is stored in the `sample_img/out` folder by default. An example json array is shown below.
 ```bash
-# Forward all images in test/ using tiny yolo and JSON output.
-./flow --test test/ --model cfg/yolo-tiny.cfg --load bin/yolo-tiny.weights --json
+# Forward all images in sample_img/ using tiny yolo and JSON output.
+./flow --imgdir sample_img/ --model cfg/yolo-tiny.cfg --load bin/yolo-tiny.weights --json
 ```
 JSON output:
 ```json
@@ -189,7 +189,7 @@ options = {"model": "cfg/yolo.cfg", "load": "bin/yolo.weights", "threshold": 0.1
 
 tfnet = TFNet(options)
 
-imgcv = cv2.imread("./test/dog.jpg")
+imgcv = cv2.imread("./sample_img/dog.jpg")
 result = tfnet.return_predict(imgcv)
 print(result)
 ```
@@ -210,8 +210,8 @@ The created `.pb` file can be used to migrate the graph to mobile devices (JAVA
 
 Also, darkflow supports loading from a `.pb` and `.meta` file for generating predictions (instead of loading from a `.cfg` and checkpoint or `.weights`).
 ```bash
-## Forward images in test for predictions based on protobuf file
-./flow --pbLoad graph-cfg/yolo.pb --metaLoad graph-cfg/yolo.meta --test test/
+## Forward images in sample_img for predictions based on protobuf file
+./flow --pbLoad graph-cfg/yolo.pb --metaLoad graph-cfg/yolo.meta --imgdir sample_img/
 ```
 If you'd like to load a `.pb` and `.meta` file when using `return_predict()` you can set the `"pbLoad"` and `"metaLoad"` options in place of the `"model"` and `"load"` options you would normally set.
 

darkflow/net/flow.py

@@ -97,11 +97,11 @@ def return_predict(self, im):
 import math
 
 def predict(self):
-    inp_path = self.FLAGS.test
+    inp_path = self.FLAGS.imgdir
     all_inps = os.listdir(inp_path)
     all_inps = [i for i in all_inps if self.framework.is_inp(i)]
     if not all_inps:
-        msg = 'Failed to find any test files in {} .'
+        msg = 'Failed to find any images in {} .'
         exit('Error: {}'.format(msg.format(inp_path)))
 
     batch = min(self.FLAGS.batch, len(all_inps))

darkflow/net/framework.py

@@ -21,28 +21,28 @@ class YOLO(framework):
     constructor = yolo.constructor
     parse = yolo.data.parse
     shuffle = yolo.data.shuffle
-    preprocess = yolo.test.preprocess
-    postprocess = yolo.test.postprocess
+    preprocess = yolo.predict.preprocess
+    postprocess = yolo.predict.postprocess
     loss = yolo.train.loss
     is_inp = yolo.misc.is_inp
     profile = yolo.misc.profile
     _batch = yolo.data._batch
-    resize_input = yolo.test.resize_input
-    findboxes = yolo.test.findboxes
-    process_box = yolo.test.process_box
+    resize_input = yolo.predict.resize_input
+    findboxes = yolo.predict.findboxes
+    process_box = yolo.predict.process_box
 
 class YOLOv2(framework):
     constructor = yolo.constructor
     parse = yolo.data.parse
     shuffle = yolov2.data.shuffle
-    preprocess = yolo.test.preprocess
+    preprocess = yolo.predict.preprocess
     loss = yolov2.train.loss
     is_inp = yolo.misc.is_inp
-    postprocess = yolov2.test.postprocess
+    postprocess = yolov2.predict.postprocess
     _batch = yolov2.data._batch
-    resize_input = yolo.test.resize_input
-    findboxes = yolov2.test.findboxes
-    process_box = yolo.test.process_box
+    resize_input = yolo.predict.resize_input
+    findboxes = yolov2.predict.findboxes
+    process_box = yolo.predict.process_box
 
 """
 framework factory

darkflow/net/yolo/__init__.py

@@ -1,5 +1,5 @@
 from . import train
-from . import test
+from . import predict
 from . import data
 from . import misc
 import numpy as np

darkflow/net/yolo/data.py

@@ -1,6 +1,6 @@
 from darkflow.utils.pascal_voc_clean_xml import pascal_voc_clean_xml
 from numpy.random import permutation as perm
-from .test import preprocess
+from .predict import preprocess
 # from .misc import show
 from copy import deepcopy
 import pickle

darkflow/net/yolo/test.py → darkflow/net/yolo/predict.py

@@ -117,7 +117,7 @@ def postprocess(self, net_out, im, save = True):
 
 	# Removing trailing comma+newline adding json list terminator.
 	textBuff = textBuff[:-2] + "]"
-	outfolder = os.path.join(self.FLAGS.test, 'out')
+	outfolder = os.path.join(self.FLAGS.imgdir, 'out')
 	img_name = os.path.join(outfolder, im.split('/')[-1])
 	if self.FLAGS.json:
 		textFile = os.path.splitext(img_name)[0] + ".json"

darkflow/net/yolov2/__init__.py

@@ -1,5 +1,5 @@
 from . import train
-from . import test
+from . import predict
 from . import data
 from ..yolo import misc
 import numpy as np

darkflow/net/yolov2/data.py

@@ -1,6 +1,6 @@
 from darkflow.utils.pascal_voc_clean_xml import pascal_voc_clean_xml
 from numpy.random import permutation as perm
-from ..yolo.test import preprocess
+from ..yolo.predict import preprocess
 from ..yolo.data import shuffle
 from copy import deepcopy
 import pickle

darkflow/net/yolov2/test.py → darkflow/net/yolov2/predict.py

@@ -64,7 +64,7 @@ def postprocess(self, net_out, im, save = True):
 	if not save: return imgcv
 	# Removing trailing comma+newline adding json list terminator.
 	textBuff = textBuff[:-2] + "]"
-	outfolder = os.path.join(self.FLAGS.test, 'out')
+	outfolder = os.path.join(self.FLAGS.imgdir, 'out')
 	img_name = os.path.join(outfolder, im.split('/')[-1])
 	if self.FLAGS.json:
 		textFile = os.path.splitext(img_name)[0] + ".json"

flow

@@ -4,7 +4,7 @@ from darkflow.net.build import TFNet
 from tensorflow import flags
 import os
 
-flags.DEFINE_string("test", "./test/", "path to testing directory")
+flags.DEFINE_string("imgdir", "./sample_img/", "path to testing directory with images")
 flags.DEFINE_string("binary", "./bin/", "path to .weights directory")
 flags.DEFINE_string("config", "./cfg/", "path to .cfg directory")
 flags.DEFINE_string("dataset", "../pascal/VOCdevkit/IMG/", "path to dataset directory")
@@ -39,7 +39,7 @@ def get_dir(dirs):
 	for d in dirs:
 		this = os.path.abspath(os.path.join(os.path.curdir, d))
 		if not os.path.exists(this): os.makedirs(this)
-get_dir([FLAGS.test, FLAGS.binary, FLAGS.backup, os.path.join(FLAGS.test,'out'), FLAGS.summary])
+get_dir([FLAGS.imgdir, FLAGS.binary, FLAGS.backup, os.path.join(FLAGS.imgdir,'out'), FLAGS.summary])
 
 # fix FLAGS.load to appropriate type
 try: FLAGS.load = int(FLAGS.load)