move flow code to cli.py

.travis.yml

@@ -12,12 +12,11 @@ cache:
 # command to install dependencies
 install: 
     - pip install -r test/requirements-testing.txt
-    - coverage run -m pip install -e .
+    - pip install -e .
 
 # command to run tests
 script: pytest -x --cov=./
 
 #Upload code coverage statistics
 after_success:
-  - coverage combine
   - codecov

darkflow/cli.py

@@ -0,0 +1,40 @@
+from .defaults import argHandler #Import the default arguments
+import os
+
+def cliHandler(args):
+    FLAGS = argHandler()
+    FLAGS.setDefaults()
+    FLAGS.parseArgs(args)
+
+    from darkflow.net.build import TFNet
+
+    # make sure all necessary dirs exist
+    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.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)
+    except: pass
+
+    tfnet = TFNet(FLAGS)
+
+    if FLAGS.profile:
+        tfnet.framework.profile(tfnet)
+        exit()
+
+    if FLAGS.demo:
+        tfnet.camera(FLAGS.demo, FLAGS.saveVideo)
+        exit()
+
+    if FLAGS.train:
+        print('Enter training ...'); tfnet.train()
+        if not FLAGS.savepb: exit('Training finished')
+
+    if FLAGS.savepb:
+        print('Rebuild a constant version ...')
+        tfnet.savepb(); exit('Done')
+
+    tfnet.predict()

darkflow/defaults.py

@@ -5,6 +5,36 @@ class argHandler(dict):
     __delattr__ = dict.__delitem__
     _descriptions = {"help, -h": "show this super helpful message and exit"}
 
+    def setDefaults(self):
+        self.define("imgdir", "./sample_img/", "path to testing directory with images")
+        self.define("binary", "./bin/", "path to .weights directory")
+        self.define("config", "./cfg/", "path to .cfg directory")
+        self.define("dataset", "../pascal/VOCdevkit/IMG/", "path to dataset directory")
+        self.define("backup", "./ckpt/", "path to backup folder")
+        self.define("summary", "./summary/", "path to TensorBoard summaries directory")
+        self.define("annotation", "../pascal/VOCdevkit/ANN/", "path to annotation directory")
+        self.define("threshold", -0.1, "detection threshold")
+        self.define("model", "", "configuration of choice")
+        self.define("trainer", "rmsprop", "training algorithm")
+        self.define("momentum", 0.0, "applicable for rmsprop and momentum optimizers")
+        self.define("verbalise", True, "say out loud while building graph")
+        self.define("train", False, "train the whole net")
+        self.define("load", "", "how to initialize the net? Either from .weights or a checkpoint, or even from scratch")
+        self.define("savepb", False, "save net and weight to a .pb file")
+        self.define("gpu", 0.0, "how much gpu (from 0.0 to 1.0)")
+        self.define("gpuName", "/gpu:0", "GPU device name")
+        self.define("lr", 1e-5, "learning rate")
+        self.define("keep",20,"Number of most recent training results to save")
+        self.define("batch", 16, "batch size")
+        self.define("epoch", 1000, "number of epoch")
+        self.define("save", 2000, "save checkpoint every ? training examples")
+        self.define("demo", '', "demo on webcam")
+        self.define("profile", False, "profile")
+        self.define("json", False, "Outputs bounding box information in json format.")
+        self.define("saveVideo", False, "Records video from input video or camera")
+        self.define("pbLoad", "", "path to .pb protobuf file (metaLoad must also be specified)")
+        self.define("metaLoad", "", "path to .meta file generated during --savepb that corresponds to .pb file")
+
     def define(self, argName, default, description):
         self[argName] = default
         self._descriptions[argName] = description
@@ -63,35 +93,3 @@ def parseArgs(self, args):
                 print("Try running ./flow --help")
                 exit()
             i += 1
-
-def getDefaults():
-    defaultFLAGS = argHandler()
-    defaultFLAGS.define("imgdir", "./sample_img/", "path to testing directory with images")
-    defaultFLAGS.define("binary", "./bin/", "path to .weights directory")
-    defaultFLAGS.define("config", "./cfg/", "path to .cfg directory")
-    defaultFLAGS.define("dataset", "../pascal/VOCdevkit/IMG/", "path to dataset directory")
-    defaultFLAGS.define("backup", "./ckpt/", "path to backup folder")
-    defaultFLAGS.define("summary", "./summary/", "path to TensorBoard summaries directory")
-    defaultFLAGS.define("annotation", "../pascal/VOCdevkit/ANN/", "path to annotation directory")
-    defaultFLAGS.define("threshold", -0.1, "detection threshold")
-    defaultFLAGS.define("model", "", "configuration of choice")
-    defaultFLAGS.define("trainer", "rmsprop", "training algorithm")
-    defaultFLAGS.define("momentum", 0.0, "applicable for rmsprop and momentum optimizers")
-    defaultFLAGS.define("verbalise", True, "say out loud while building graph")
-    defaultFLAGS.define("train", False, "train the whole net")
-    defaultFLAGS.define("load", "", "how to initialize the net? Either from .weights or a checkpoint, or even from scratch")
-    defaultFLAGS.define("savepb", False, "save net and weight to a .pb file")
-    defaultFLAGS.define("gpu", 0.0, "how much gpu (from 0.0 to 1.0)")
-    defaultFLAGS.define("gpuName", "/gpu:0", "GPU device name")
-    defaultFLAGS.define("lr", 1e-5, "learning rate")
-    defaultFLAGS.define("keep",20,"Number of most recent training results to save")
-    defaultFLAGS.define("batch", 16, "batch size")
-    defaultFLAGS.define("epoch", 1000, "number of epoch")
-    defaultFLAGS.define("save", 2000, "save checkpoint every ? training examples")
-    defaultFLAGS.define("demo", '', "demo on webcam")
-    defaultFLAGS.define("profile", False, "profile")
-    defaultFLAGS.define("json", False, "Outputs bounding box information in json format.")
-    defaultFLAGS.define("saveVideo", False, "Records video from input video or camera")
-    defaultFLAGS.define("pbLoad", "", "path to .pb protobuf file (metaLoad must also be specified)")
-    defaultFLAGS.define("metaLoad", "", "path to .meta file generated during --savepb that corresponds to .pb file")
-    return defaultFLAGS

flow

@@ -1,47 +1,7 @@
 #! /usr/bin/env python
 
 import sys
-import os
-from darkflow.defaults import getDefaults #Import the default arguments (if you want to change/add default arguments do it in /darkflow/defaults.py)
+from darkflow.cli import cliHandler
 
-def main(args):
-	defaultFLAGS = getDefaults()
-	defaultFLAGS.parseArgs(args)
-	FLAGS = defaultFLAGS #Handle the arguments passed
-
-	from darkflow.net.build import TFNet
-
-	# make sure all necessary dirs exist
-	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.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)
-	except: pass
-
-	tfnet = TFNet(FLAGS)
-
-	if FLAGS.profile:
-		tfnet.framework.profile(tfnet)
-		exit()
-
-	if FLAGS.demo:
-		tfnet.camera(FLAGS.demo, FLAGS.saveVideo)
-		exit()
-
-	if FLAGS.train:
-		print('Enter training ...'); tfnet.train()
-		if not FLAGS.savepb: exit('Training finished')
-
-	if FLAGS.savepb:
-		print('Rebuild a constant version ...')
-		tfnet.savepb(); exit('Done')
-
-	tfnet.predict()
-
-if __name__ == "__main__":
-	main(sys.argv)
+cliHandler(sys.argv)
 

test/test_darkflow.py

@@ -1,4 +1,5 @@
 from darkflow.net.build import TFNet
+from darkflow.cli import cliHandler
 import json
 import requests
 import cv2
@@ -46,11 +47,6 @@ def download_file(url, savePath):
     else:
         print("Found existing " + fileName + " file.")
 
-if os.path.isfile(os.path.join(buildPath, "flow")):
-    os.rename(os.path.join(buildPath, "flow"), os.path.join(buildPath, "flow.py")) #Change flow to flow.py so we can import it
-sys.path.insert(0, buildPath) #Add the buildPath to the PATH
-from flow import main
-
 yoloWeightPathV1 = os.path.join(buildPath, "bin", yoloDownloadV1.split("/")[-1])
 yoloCfgPathV1 = os.path.join(buildPath, "cfg", "v1", "{0}.cfg".format(os.path.splitext(os.path.basename(yoloWeightPathV1))[0]))
 
@@ -70,7 +66,7 @@ def executeCLI(commandString):
     print("Executing: {0}".format(commandString))
     print()
     splitArgs = [item.strip() for item in commandString.split(" ")]
-    main(splitArgs) #Run the command
+    cliHandler(splitArgs) #Run the command
     print()
 
 def compareSingleObjects(firstObject, secondObject, width, height):