Bump lxml from 4.3.0 to 4.9.1

.gitignore

@@ -1,6 +1,6 @@
+*.pyc
 .idea/
 venv/
 main/input/people_walking_mp4
 main/output
-object_detection/models
-*.pyc
+object_detection/models/*

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "main/DaSiamRPN"]
+	path = main/DaSiamRPN
+	url = https://github.com/foolwood/DaSiamRPN/

README.md

@@ -1,24 +1,5 @@
 # OpenLabeling: open-source image and video labeler
 
-
-# New features:
-- Auto labeling by using combination between Tracker and Deep Learning Object Detection Model
-
-# How to use
-- Download one or some deep learning models from https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
-  and put it into `object_detection` directory. The outline of `object_detection` looks like that:
-  + `tf_object_detection.py`
-  + `utils.py`
-  + `ssdlite_mobilenet_v2_coco_2018_05_09` . Download by clicking this link http://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz
-  + Or `mask_rcnn_inception_v2_coco_2018_01_28`. Download by clicking this link http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_v2_coco_2018_01_28.tar.gz
-
-  **Note**: Default model used in `main_auto.py` is `mask_rcnn_inception_v2_coco_2018_01_28`. We can
-  set `graph_model_path` in file `main_auto.py` to change the pretrain model
-- Using `main_auto.py` to automatically label data first
-
-# Plan 
-- Combine with state of the art deep learning tracker
-
 [![GitHub stars](https://img.shields.io/github/stars/Cartucho/OpenLabeling.svg?style=social&label=Stars)](https://github.com/Cartucho/OpenLabeling)
 
 Image labeling in multiple annotation formats:
@@ -31,6 +12,8 @@ Image labeling in multiple annotation formats:
 
 ## Latest Features
 
+- Jun 2019: Deep Learning Object Detection Model
+- May 2019: [ECCV2018] Distractor-aware Siamese Networks for Visual Object Tracking
 - Jan 2019: easy and quick bounding-boxe's resizing!
 - Jan 2019: video object tracking with OpenCV trackers!
 - TODO: Label photos via Google drive to allow "team online labeling".
@@ -92,6 +75,22 @@ Step by step:
 
   5. You can find the annotations in the folder **output/**
 
+#### How to use the deep learning feature
+
+- Download one or some deep learning models from https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
+  and put it into `object_detection/models` directory (you need to create the `models` folder by yourself). The outline of `object_detection` looks like that:
+  + `tf_object_detection.py`
+  + `utils.py`
+  + `models/ssdlite_mobilenet_v2_coco_2018_05_09`
+
+Download the pre-trained model by clicking this link http://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz and put it into `object_detection/models`. Create the `models` folder if necessary. Make sure to extract the model.
+
+  **Note**: Default model used in `main_auto.py` is `ssdlite_mobilenet_v2_coco_2018_05_09`. We can
+  set `graph_model_path` in file `main_auto.py` to change the pretrain model
+- Using `main_auto.py` to automatically label data first
+
+  TODO: explain how the user can 
+
 ### GUI usage
 
 Keyboard, press: 

main/dasiamrpn.py

@@ -0,0 +1,102 @@
+"""
+Author : Will Stone
+Date   : 190407
+Desc   : Wrapper class for the DaSiamRPN tracking method. This class has the
+         methods required to interface with the tracking class implemented
+         in main.py within the OpenLabeling package.
+"""
+import torch
+import numpy as np
+import sys
+from os.path import realpath, dirname, join, exists
+try:
+    from DaSiamRPN.code.run_SiamRPN import SiamRPN_init, SiamRPN_track
+except ImportError:
+    # check if the user has downloaded the submodules
+    if not exists(join('DaSiamRPN', 'code', 'net.py')):
+        print('Error: DaSiamRPN files not found. Please run the following command:')
+        print('\tgit submodule update --init')
+        exit()
+    else:
+        # if python 3
+        if sys.version_info >= (3, 0):
+            sys.path.append(realpath(join('DaSiamRPN', 'code')))
+        else:
+            # check if __init__py files exist (otherwise create them)
+            path_temp = join('DaSiamRPN', 'code', '__init__.py')
+            if not exists(path_temp):
+                open(path_temp, 'w').close()
+            path_temp = join('DaSiamRPN', '__init__.py')
+            if not exists(path_temp):
+                open(path_temp, 'w').close()
+        # try to import again
+        from DaSiamRPN.code.run_SiamRPN import SiamRPN_init, SiamRPN_track
+from DaSiamRPN.code.net import SiamRPNvot
+from DaSiamRPN.code.utils import get_axis_aligned_bbox, cxy_wh_2_rect
+
+class dasiamrpn(object):
+    """
+    Wrapper class for incorporating DaSiamRPN into OpenLabeling
+    (https://github.com/foolwood/DaSiamRPN,
+    https://github.com/Cartucho/OpenLabeling)
+    """
+    def __init__(self):
+        self.net = SiamRPNvot()
+        # check if SiamRPNVOT.model was already downloaded (otherwise download it now)
+        model_path = join(realpath(dirname(__file__)), 'DaSiamRPN', 'code', 'SiamRPNVOT.model')
+        print(model_path)
+        if not exists(model_path):
+            print('\nError: module not found. Please download the pre-trained model and copy it to the directory \'DaSiamRPN/code/\'\n')
+            print('\tdownload link: https://drive.google.com/file/d/1-vNVZxfbIplXHrqMHiJJYWXYWsOIvGsf/view')
+            exit()
+        self.net.load_state_dict(torch.load(model_path))
+        self.net.eval().cuda()
+
+    def init(self, init_frame, initial_bbox):
+        """
+        Initialize DaSiamRPN tracker with inital frame and bounding box.
+        """
+        target_pos, target_sz = self.bbox_to_pos(initial_bbox)
+        self.state = SiamRPN_init(
+            init_frame, target_pos, target_sz, self.net)
+
+    def update(self, next_image):
+        """
+        Update bounding box position and size on next_image. Returns True
+        beacuse tracking is terminated based on number of frames predicted
+        in OpenLabeling, not based on feedback from tracking algorithm (unlike
+        the opencv tracking algorithms).
+        """
+        self.state = SiamRPN_track(self.state, next_image)
+        target_pos = self.state["target_pos"]
+        target_sz  = self.state["target_sz"]
+        bbox = self.pos_to_bbox(target_pos, target_sz)
+
+        return True, bbox
+
+    def bbox_to_pos(self, initial_bbox):
+        """
+        Convert bounding box format from a tuple format containing
+        xmin, ymin, width, and height to a tuple of two arrays which contain
+        the x and y coordinates of the center of the box and its width and
+        height respectively.
+        """
+        xmin, ymin, w, h = initial_bbox
+        cx = int(xmin + w/2)
+        cy = int(ymin + h/2)
+        target_pos = np.array([cx, cy])
+        target_sz  = np.array([w, h])
+
+        return target_pos, target_sz
+
+    def pos_to_bbox(self, target_pos, target_sz):
+        """
+        Invert the bounding box format produced in the above conversion
+        function.
+        """
+        w = target_sz[0]
+        h = target_sz[1]
+        xmin = int(target_pos[0] - w/2)
+        ymin = int(target_pos[1] - h/2)
+
+        return xmin, ymin, w, h

main/main.py

@@ -12,6 +12,7 @@
 from lxml import etree
 import xml.etree.cElementTree as ET
 
+from dasiamrpn import dasiamrpn
 
 DELAY = 20 # keyboard delay (in milliseconds)
 WITH_QT = False
@@ -28,18 +29,22 @@
 parser.add_argument('-i', '--input_dir', default='input', type=str, help='Path to input directory')
 parser.add_argument('-o', '--output_dir', default='output', type=str, help='Path to output directory')
 parser.add_argument('-t', '--thickness', default='1', type=int, help='Bounding box and cross line thickness')
+parser.add_argument('--tracker', default='KCF', type=str, help='Type of tracker being used')
+parser.add_argument('-n', '--n_frames', default='50', type=int, help='number of frames to track object for')
 args = parser.parse_args()
 
 class_index = 0
 img_index = 0
 img = None
 img_objects = []
 
-INPUT_DIR = args.input_dir
+INPUT_DIR  = args.input_dir
 OUTPUT_DIR = args.output_dir
+N_FRAMES   = args.n_frames
+TRACKER_TYPE = args.tracker
 
-WINDOW_NAME = 'OpenLabeling'
-TRACKBAR_IMG = 'Image'
+WINDOW_NAME    = 'OpenLabeling'
+TRACKBAR_IMG   = 'Image'
 TRACKBAR_CLASS = 'Class'
 
 annotation_formats = {'PASCAL_VOC' : '.xml', 'YOLO_darknet' : '.txt'}
@@ -569,7 +574,7 @@ def mouse_listener(event, x, y, flags, param):
                     point_1 = (x, y)
             else:
                 # minimal size for bounding box to avoid errors
-                threshold = 10
+                threshold = 5
                 if abs(x - point_1[0]) > threshold or abs(y - point_1[1]) > threshold:
                     # second click
                     point_2 = (x, y)
@@ -790,7 +795,7 @@ class LabelTracker():
     # TODO: press ESC to stop the tracking process
 
     def __init__(self, tracker_type, init_frame, next_frame_path_list):
-        tracker_types = ['CSRT', 'KCF','MOSSE', 'MIL', 'BOOSTING', 'MEDIANFLOW', 'TLD', 'GOTURN']
+        tracker_types = ['CSRT', 'KCF','MOSSE', 'MIL', 'BOOSTING', 'MEDIANFLOW', 'TLD', 'GOTURN', 'DASIAMRPN']
         ''' Recomended tracker_type:
               KCF -> KCF is usually very good (minimum OpenCV 3.1.0)
               CSRT -> More accurate than KCF but slightly slower (minimum OpenCV 3.4.2)
@@ -809,27 +814,36 @@ def __init__(self, tracker_type, init_frame, next_frame_path_list):
 
 
     def call_tracker_constructor(self, tracker_type):
-        # -- TODO: remove this if I assume OpenCV version > 3.4.0
-        if int(self.major_ver == 3) and int(self.minor_ver) < 3:
-            tracker = cv2.Tracker_create(tracker_type)
-        # --
+        if tracker_type == 'DASIAMRPN':
+            tracker = dasiamrpn()
         else:
-            if tracker_type == 'CSRT':
-                tracker = cv2.TrackerCSRT_create()
-            elif tracker_type == 'KCF':
-                tracker = cv2.TrackerKCF_create()
-            elif tracker_type == 'MOSSE':
-                tracker = cv2.TrackerMOSSE_create()
-            elif tracker_type == 'MIL':
-                tracker = cv2.TrackerMIL_create()
-            elif tracker_type == 'BOOSTING':
-                tracker = cv2.TrackerBoosting_create()
-            elif tracker_type == 'MEDIANFLOW':
-                tracker = cv2.TrackerMedianFlow_create()
-            elif tracker_type == 'TLD':
-                tracker = cv2.TrackerTLD_create()
-            elif tracker_type == 'GOTURN':
-                tracker = cv2.TrackerGOTURN_create()
+            # -- TODO: remove this if I assume OpenCV version > 3.4.0
+            if int(self.major_ver == 3) and int(self.minor_ver) < 3:
+                #tracker = cv2.Tracker_create(tracker_type)
+                pass
+            # --
+            else:
+                try:
+                    tracker = cv2.TrackerKCF_create()
+                except AttributeError as error:
+                    print(error)
+                    print('\nMake sure that OpenCV contribute is installed: opencv-contrib-python\n')
+                if tracker_type == 'CSRT':
+                    tracker = cv2.TrackerCSRT_create()
+                elif tracker_type == 'KCF':
+                    tracker = cv2.TrackerKCF_create()
+                elif tracker_type == 'MOSSE':
+                    tracker = cv2.TrackerMOSSE_create()
+                elif tracker_type == 'MIL':
+                    tracker = cv2.TrackerMIL_create()
+                elif tracker_type == 'BOOSTING':
+                    tracker = cv2.TrackerBoosting_create()
+                elif tracker_type == 'MEDIANFLOW':
+                    tracker = cv2.TrackerMedianFlow_create()
+                elif tracker_type == 'TLD':
+                    tracker = cv2.TrackerTLD_create()
+                elif tracker_type == 'GOTURN':
+                    tracker = cv2.TrackerGOTURN_create()
         return tracker
 
 
@@ -848,6 +862,8 @@ def start_tracker(self, json_file_data, json_file_path, img_path, obj, color, an
             next_image = cv2.imread(frame_path)
             # get the new bbox prediction of the object
             success, bbox = tracker.update(next_image.copy())
+            if pred_counter >= N_FRAMES:
+                success = False
             if success:
                 pred_counter += 1
                 xmin, ymin, w, h = map(int, bbox)
@@ -1080,7 +1096,7 @@ def complement_bgr(color):
                         next_frame_path_list = get_next_frame_path_list(video_name, img_path)
                         # initial frame
                         init_frame = img.copy()
-                        label_tracker = LabelTracker('KCF', init_frame, next_frame_path_list) # TODO: replace 'KCF' by 'CSRT'
+                        label_tracker = LabelTracker(TRACKER_TYPE, init_frame, next_frame_path_list)
                         for obj in object_list:
                             class_index = obj[0]
                             color = class_rgb[class_index].tolist()

requirements.txt

@@ -1,4 +1,4 @@
-lxml==4.3.0
+lxml==4.9.1
 numpy==1.16.0
 opencv-contrib-python==3.4.5.20
 tqdm==4.29.1