make it pythonic

Author: gustavz

README.md

@@ -21,15 +21,14 @@ Results in up to x10 performance increase depending on the running system <br />
 
 
 ## Inference:  
-- create a copy of `config.sample.yml` called `config.yml`
-- optional: Change Parameters in `config.yml` to load other models or to modify configurations.<br />
-For example: If you are not interested in visualization: set `visualize` to `False`, <br />
-or if you want to switch off the speed hack set `split_model` to `False`, <br />
-- to be able to use KCF_Tracking run `build_kcf.sh` to build it and set `use_tracker` to `true` to use it <br />
-(currently only works for pure object detection models without `split_model`)
+- Change Inference Configurations inside `rod/config.py` according to your needs
+For example: If you are not interested in visualization: set `VISUALIZE` to `False`, <br />
+or if you want to switch off the speed hack set `SPLIT_MODEL` to `False`, <br />
+- to be able to use KCF_Tracking inside `scripts/` run `bash build_kcf.sh` to build it and set `USE_TRACKER` to `True` to use it <br />
+(currently only works for pure object detection models without `SPLIT_MODEL`)
 - for realtime inference using video stream run: `run_objectdetection.py` or `run_deeplab.py`
 - for benchmark tests on sample images run: `test_objectdetection.py`or `test_deeplab.py` <br />
-(put them as `.jpg`  into `test_images/`)
+(put them as `.jpg`  into `test_images/`. timeline results will appear in `test_results/`)
 - Enjoy!
 
 

build_kcf.sh

@@ -0,0 +1,69 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+@author: www.github.com/GustavZ
+"""
+import numpy as np
+
+class Config(object):
+    """
+    Inference Configuration class
+    Replaces 'config.sample.yml' of v1.0
+    """
+    ### Inference Config
+    VIDEO_INPUT = 0                 # Input Must be OpenCV readable
+    VISUALIZE = True                # Disable for performance increase
+
+
+    ### Testing
+    IMAGE_PATH = 'test_images'      # path for test.py test_images
+    LIMIT_IMAGES = None             # if set to None, all images are used
+    CPU_ONLY = False                # CPU Placement for speed test
+    WRITE_TIMELINE = True           # write json timeline file (slows infrence)
+
+
+    ### Object_Detection
+    WIDTH = 600                     # OpenCV only supports 4:3 formats others will be converted
+    HEIGHT = 600                    # 600x600 leads to 640x480
+    MAX_FRAMES = 5000               # only used if visualize==False
+    FPS_INTERVAL = 5                # Interval [s] to print fps of the last interval in console
+    DET_INTERVAL = 500              # intervall [frames] to print detections to console
+    DET_TH = 0.5                    # detection threshold for det_intervall
+    ## speed hack
+    SPLIT_MODEL = True              # Splits Model into a GPU and CPU session (currently only works for ssd_mobilenets)
+    SSD_SHAPE = 300                 # used for the split model algorithm (currently only supports ssd networks trained on 300x300 and 600x600 input)
+    ## Tracking
+    USE_TRACKER = False             # Use a Tracker (currently only works properly WITHOUT split_model)
+    TRACKER_FRAMES = 20             # Number of tracked frames between detections
+    NUM_TRACKERS = 5                # Max number of objects to track
+    ## Model
+    OD_MODEL_NAME = 'ssd_mobilenet_v11_coco'
+    OD_MODEL_PATH = 'models/ssd_mobilenet_v11_coco/frozen_inference_graph.pb'
+    LABEL_PATH = 'rod/data/mscoco_label_map.pbtxt'
+    NUM_CLASSES = 90
+
+
+    ### DeepLab
+    ALPHA = 0.3                     # mask overlay factor
+    BBOX = True                     # compute boundingbox in postprocessing
+    MINAREA = 500                   # min Pixel Area to apply bounding boxes (avoid noise)
+    ## Model
+    DL_MODEL_NAME = 'deeplabv3_mnv2_pascal_train_aug_2018_01_29'
+    DL_MODEL_PATH = 'models/deeplabv3_mnv2_pascal_train_aug/frozen_inference_graph.pb'
+    LABEL_NAMES = np.asarray([
+        'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
+        'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
+        'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tv'])
+
+
+    def __init__(self):
+        ## TimeLine File naming
+        if self.CPU_ONLY:
+        	os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+        	self.DEVICE = '_CPU'
+        else:
+        	self.DEVICE = '_GPU'
+        if self.SPLIT_MODEL:
+            self.SM = '_SM'
+        else:
+            self.SM = ''

config.sample.yml

@@ -25,8 +25,8 @@
 """
 import tensorflow as tf
 
-from stuff.object_detection.core import box_list
-from stuff.object_detection.utils import shape_utils
+from rod.core import box_list
+from rod.utils import shape_utils
 
 
 class SortOrder(object):