[FEATURE] add color_picker.py for ease of picking HSV lower and upper…

… ranges to use with TrackDLO

docs/RUN.md

@@ -16,13 +16,11 @@ Installation and execution of TrackDLO was verified with the below dependencies
 * [Pillow](https://pillow.readthedocs.io/en/stable/installation.html) (Our version: 9.2.0)
 * [ROS Numpy](https://pypi.org/project/rosnumpy/) (Our version: 0.0.5)
 
-We also provide Docker files for compatibility with other system configurations, refer to the ![DOCKER.md](https://github.com/RMDLO/trackdlo/blob/master/docs/DOCKER.md) for more information.
+We also provide Docker files for compatibility with other system configurations, refer to [DOCKER.md](https://github.com/RMDLO/trackdlo/blob/master/docs/DOCKER.md) for more information.
 
 ## Other Requirements
 
-We used an Intel RealSense d435 camera in all of the experiments performed in our paper. 
-
-* [librealsense](https://github.com/IntelRealSense/librealsense) and [realsense-ros](https://github.com/IntelRealSense/realsense-ros/tree/ros1-legacy) (for testing with the RealSense D435 camera and the corresponding camera configuration file we provided)
+We used an Intel RealSense d435 camera in all of the experiments performed in our paper. We used the [librealsense](https://github.com/IntelRealSense/librealsense) and [realsense-ros](https://github.com/IntelRealSense/realsense-ros/tree/ros1-legacy) packages for testing with the RealSense D435 camera and for obtaining the [camera configuration file](https://github.com/RMDLO/trackdlo/blob/master/config/preset_decimation_4.0_depth_step_100.json).
 
 ## Installation
 
@@ -55,9 +53,7 @@ To run TrackDLO, the three ROS topic names below should be modified in `launch/t
 
 **Note: TrackDLO assumes the RGB image and its corresponding depth image are ALIGNED and SYNCHRONIZED. This means depth_image(i, j) should contain the depth value of pixel rgb_image(i, j) and the two images should be published with the same ROS timestamp.**
 
-TrackDLO uses color thresholding to obtain the DLO segmentation mask. Below are two different ways to set the color thresholding parameters:
-* If the DLO of interest only has one color: you can use the parameters `hsv_threshold_upper/lower_limit` and set their values with format `h_value s_value v_value` (`h_value<space>s_value<space>v_value`).
-* If the DLO of interest has multiple colors: set `multi_color_dlo` to `true` in `launch/trackdlo.launch`, then you can modify the function `color_thresholding` in `trackdlo/src/initialize.py` and `trackdlo/src/trackdlo_node.cpp` to customize the DLO segmentation process.
+TrackDLO uses color thresholding in Hue, Saturation, and Value (HSV) color space to obtain the DLO segmentation mask. A tutorial on how to obtain the HSV limits is provided in [`COLOR_THRESHOLD.md`](https://github.com/RMDLO/trackdlo/blob/master/docs/COLOR_THRESHOLD.md)
 
 Other useful parameters:
 * `num_of_nodes`: the number of nodes initialized for the DLO

utils/color_picker.py

@@ -0,0 +1,76 @@
+import cv2
+import argparse
+import numpy as np
+
+def nothing(x):
+    pass
+
+
+parser = argparse.ArgumentParser(description="Pick HSV color threshold")
+parser.add_argument("path", help="Indicate /full-or-relative/path/to/image_file.png")
+args = parser.parse_args()
+img_path = f"{args.path}"
+
+# Create a window
+cv2.namedWindow('image')
+
+# create trackbars for color change
+cv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv
+cv2.createTrackbar('SMin','image',0,255,nothing)
+cv2.createTrackbar('VMin','image',0,255,nothing)
+cv2.createTrackbar('HMax','image',0,179,nothing)
+cv2.createTrackbar('SMax','image',0,255,nothing)
+cv2.createTrackbar('VMax','image',0,255,nothing)
+
+# Set default value for MAX HSV trackbars.
+cv2.setTrackbarPos('HMax', 'image', 179)
+cv2.setTrackbarPos('SMax', 'image', 255)
+cv2.setTrackbarPos('VMax', 'image', 255)
+
+# Initialize to check if HSV min/max value changes
+hMin = sMin = vMin = hMax = sMax = vMax = 0
+phMin = psMin = pvMin = phMax = psMax = pvMax = 0
+
+img = cv2.imread(img_path)
+img = cv2.resize(img, (640, 480))
+output = img
+waitTime = 33
+
+while(1):
+
+    # get current positions of all trackbars
+    hMin = cv2.getTrackbarPos('HMin','image')
+    sMin = cv2.getTrackbarPos('SMin','image')
+    vMin = cv2.getTrackbarPos('VMin','image')
+
+    hMax = cv2.getTrackbarPos('HMax','image')
+    sMax = cv2.getTrackbarPos('SMax','image')
+    vMax = cv2.getTrackbarPos('VMax','image')
+
+    # Set minimum and max HSV values to display
+    lower = np.array([hMin, sMin, vMin])
+    upper = np.array([hMax, sMax, vMax])
+
+    # Create HSV Image and threshold into a range.
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    mask = cv2.inRange(hsv, lower, upper)
+    output = cv2.bitwise_and(img,img, mask= mask)
+
+    # Print if there is a change in HSV value
+    if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
+        print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
+        phMin = hMin
+        psMin = sMin
+        pvMin = vMin
+        phMax = hMax
+        psMax = sMax
+        pvMax = vMax
+
+    # Display output image
+    cv2.imshow('image',output)
+
+    # Wait longer to prevent freeze for videos.
+    if cv2.waitKey(waitTime) & 0xFF == ord('q'):
+        break
+
+cv2.destroyAllWindows()