Overview & Examples

This file illustrates few image processing pipeline using vision module from niryo_edu package. This module is based in OpenCV and its functions are detailed in Image Processing API

The package niryo_edu comes up with the vision module which contains image processing functions including thresholding, blob detection, …

To use it, add to your imports from pyniryo.vision import *

Note

It is also possible to merge both import lines by using from pyniryo import *

Play with Robot Video Stream

We are firstly going to take a look at robot’s functions which can be find at API - Vision

Get & Display Image from Stream

Ned can share its video stream through TCP. As sending raw images will lead to heavy packets which can saturate the network, it send compressed images. You access it through the Robot’s function : get_img_compressed(). Once your image is received, you firstly need to uncompress via uncompress_image() and you can then display it with show_img_and_wait_close().

from pyniryo import *

# Connecting to robot
robot = NiryoRobot("10.10.10.10")

# Getting image
img_compressed = robot.get_img_compressed()
# Uncompressing image
img = uncompress_image(img_compressed)

# Displaying
show_img_and_wait_close("img_stream", img)

Note

show_img_and_wait_close() will wait for the user to press either Q or Esc key, before closing the window

Undistort and display video stream

In this section, we are going to display the raw video stream & the undistorted video stream

As Ned’s camera is passing raw images to the robot, these images are distorted due to the camera lens. In order to undistort them, we need to use Robot’s camera intrinsics.

To undistort the raw image, we use undistort_image() which need to be called with the parameters given by the robot through get_camera_intrinsics().

Once, we have both raw & undistorted images, we can concatenate them in order to display them in once with concat_imgs(). Finally, we display the image show_img()

from pyniryo import *

observation_pose = PoseObject(
    x=0.18, y=0.0, z=0.35,
    roll=0.0, pitch=1.57, yaw=-0.2,
)

# Connecting to robot
robot = NiryoRobot("10.10.10.10")
robot.calibrate_auto()

# Getting calibration param
mtx, dist = robot.get_camera_intrinsics()
# Moving to observation pose
robot.move_pose(observation_pose)

while "User do not press Escape neither Q":
    # Getting image
    img_compressed = robot.get_img_compressed()
    # Uncompressing image
    img_raw = uncompress_image(img_compressed)
    # Undistorting
    img_undistort = undistort_image(img_raw, mtx, dist)

    # - Display
    # Concatenating raw image and undistorted image
    concat_ims = concat_imgs((img_raw, img_undistort))

    # Showing images
    key = show_img("Images raw & undistorted", concat_ims, wait_ms=30)
    if key in [27, ord("q")]:  # Will break loop if the user press Escape or Q
        break

Note

To see more about camera distortion/undistortion, go on OpenCV Documentation about Camera Calibration

Pure image processing functions

Hint

See TP on image processing for more theoretical information

In order to illustrate functions, we are going to use the following image

Attention

In this section it is supposed that :

You have import pyniryo.vision
The variable img is containing the image on which image processing is applied

Color Thresholding

Color Thresholding is very useful in order to detect object with an uniform color. The implemented function to realize this operation is threshold_hsv()

The following code is using parameters from ColorHSV enum in order to threshold Red features & hand made parameters to extract Blue

img_threshold_red = threshold_hsv(img_test, *ColorHSV.RED.value)

blue_min_hsv = [90, 85, 70]
blue_max_hsv = [125, 255, 255]

img_threshold_blue = threshold_hsv(img_test, list_min_hsv=blue_min_hsv,
                                   list_max_hsv=blue_max_hsv, reverse_hue=False)

show_img("img_threshold_red", img_threshold_red)

show_img_and_wait_close("img_threshold_blue", img_threshold_blue)

Images result
Thresh Color	Image Result
Blue
Red

Morphological Transformations

Morphological transformations are some simple operations based on the image shape. It is normally performed on binary images. It needs two inputs, one is our original image, second one is called structuring element or kernel which decides the nature of operation. Two basic morphological operators are Erosion and Dilation

Then its variant forms like Opening, Closing also comes into play. Lean more on Wikipedia page

The implemented function to realize these operations is morphological_transformations(). It uses MorphoType and KernelType to determine which operation should be applied on the image

The code shows how to do a Closing & an Erosion

img_threshold = threshold_hsv(img_test, *ColorHSV.ANY.value)

img_close = morphological_transformations(img_threshold, morpho_type=MorphoType.CLOSE,
                                          kernel_shape=(11, 11), kernel_type=KernelType.ELLIPSE)

img_erode = morphological_transformations(img_threshold, morpho_type=MorphoType.ERODE,
                                          kernel_shape=(9, 9), kernel_type=KernelType.RECT)

show_img("img_threshold", img_threshold)
show_img("img_erode", img_erode)
show_img_and_wait_close("img_close", img_close)

Images result
Morpho Type	Image Result
None
Erode
Close

Contours finder

Contours can be explained simply as a curve joining all the continuous points (along the boundary), having same color or intensity. The contours are a useful tool for shape analysis and object detection and recognition. See more on OpenCV Documentation.

The implemented function to realize these operations is biggest_contours_finder() which takes a Black & White image, and extract biggest (in term of area) contours from it

The code to extract and draw the 3 biggest contours from an image is the following :

img_threshold = threshold_hsv(img_test, *ColorHSV.ANY.value)
img_threshold = morphological_transformations(img_threshold, morpho_type=MorphoType.OPEN,
                                              kernel_shape=(11, 11), kernel_type=KernelType.ELLIPSE)

cnts = biggest_contours_finder(img_threshold, 3)

img_contours = draw_contours(img_threshold, cnts)

show_img("init", img_threshold)
show_img_and_wait_close("img with contours", img_contours)

Images result
Thresh + Opening
3 Contours

Find object center position

In order to catch an object, we need to find a pose from where the end effector can grasp the object. The method which follow uses contours which have been found in the previous section and finds their barycenter and orientation via the functions get_contour_barycenter() & get_contour_angle()

img_threshold = threshold_hsv(img_test, *ColorHSV.ANY.value)
img_threshold = morphological_transformations(img_threshold, morpho_type=MorphoType.OPEN,
                                              kernel_shape=(11, 11), kernel_type=KernelType.ELLIPSE)

cnt = biggest_contour_finder(img_threshold)

cnt_barycenter = get_contour_barycenter(cnt)
cnt_angle = get_contour_angle(cnt)

Images result
Thresh + Opening
Barycenter + Angle

Note

The drawn vector is normal to the contour’s length because we want the robot to catch the object by the width rather than the length. Indeed, it leads to least cases where the gripper cannot open enough

Markers Extraction

As image processing happens in a workspace, it is important to extract the workspace beforehand ! To do so, you can use the function extract_img_workspace()

status, im_work = extract_img_workspace(img, workspace_ratio=1.0)
show_img("init", img_test)
show_img_and_wait_close("img_workspace", img_workspace)

Images result
Original
Extracted

Debug mode

If robot’s functions are failing, you can use Debug functions which are debug_threshold_color() & debug_markers() in order to display what the robot sees.

You can use the functions as follow :

debug_color = debug_threshold_color(img_test, ColorHSV.RED)
_status, debug_markers_im = debug_markers(img_test, workspace_ratio=1.0)

show_img("init", img_test)
show_img("debug_color", debug_color)
show_img_and_wait_close("debug_markers", debug_markers_im)

Images result
Original
Debug Red
Debug Markers

Do your own image processing !

Now that you are a master in image processing, let’s look at full examples

Display video stream with extracted workspace

In the current state, the following code will display the video stream and the extracted workspace img. You can add your own image processing functions maybe to apply additional process

from pyniryo import *

# Connecting to robot
robot = NiryoRobot("10.10.10.10")
robot.calibrate_auto()

# Getting calibration param
mtx, dist = robot.get_camera_intrinsics()
# Moving to observation pose
robot.move_pose(*observation_pose.to_list())

while "User do not press Escape neither Q":
    # Getting image
    img_compressed = robot.get_img_compressed()
    # Uncompressing image
    img_raw = uncompress_image(img_compressed)
    # Undistorting
    img_undistort = undistort_image(img_raw, mtx, dist)
    # Trying to find markers
    workspace_found, res_img_markers = debug_markers(img_undistort)
    # Trying to extract workspace if possible
    if workspace_found:
        img_workspace = extract_img_workspace(img_undistort, workspace_ratio=1.0)
    else:
        img_workspace = None

    # --- --------- --- #
    # --- YOUR CODE --- #
    # --- --------- --- #

    # - Display
    # Concatenating raw image and undistorted image
    concat_ims = concat_imgs((img_raw, img_undistort))
    # Concatenating extracted workspace with markers annotation
    if img_workspace is not None:
        resized_img_workspace = resize_img(img_workspace, height=res_img_markers.shape[0])
        res_img_markers = concat_imgs((res_img_markers, resized_img_workspace))
    # Showing images
    show_img("Images raw & undistorted", concat_ims)
    key = show_img("Markers", res_img_markers, wait_ms=30)
    if key in [27, ord("q")]:  # Will break loop if the user press Escape or Q
        break

Vision pick via your image processing pipeline

You may want to do send coordinate to Ned in order to pick the object of your choice ! To do that, use the function get_target_pose_from_rel() which convert a relative pose in the workspace into a pose in the robot’s world !

# Initializing variables
obj_pose = None
try_without_success = 0
count = 0
color_hsv_setting = ColorHSV.ANY.value


mtx, dist = robot.get_camera_intrinsics()
# Loop
while try_without_success < 5:
    # Moving to observation pose
    robot.move_pose(observation_pose)

    img_compressed = robot.get_img_compressed()
    img = uncompress_image(img_compressed)
    img = undistort_image(img, mtx, dist)
    # extracting working area
    im_work = extract_img_workspace(img, workspace_ratio=1.0)
    if im_work is None:
        print("Unable to find markers")
        try_without_success += 1
        if display_stream:
            cv2.imshow("Last image saw", img)
            cv2.waitKey(25)
        continue

    # Applying Threshold on ObjectColor
    img_thresh = threshold_hsv(im_work, *color_hsv_setting)

    if display_stream:
        show_img("Last image saw", img, wait_ms=0)
        show_img("Image thresh", img_thresh, wait_ms=30)
    # Getting biggest contour/blob from threshold image
    contour = biggest_contour_finder(img_thresh)
    if contour is None or len(contour) == 0:
        print("No blob found")
        obj_found = False
    else:
        img_thresh_rgb = cv2.cvtColor(img_thresh, cv2.COLOR_GRAY2BGR)
        draw_contours(img_thresh_rgb, [contour])
        show_img("Image thresh", img_thresh, wait_ms=30)

        # Getting contour/blob center and angle
        cx, cy = get_contour_barycenter(contour)
        cx_rel, cy_rel = relative_pos_from_pixels(im_work, cx, cy)
        angle = get_contour_angle(contour)

        # Getting object world pose from relative pose
        obj_pose = robot.get_target_pose_from_rel(workspace_name,
                                                             height_offset=0.0,
                                                             x_rel=cx_rel, y_rel=cy_rel,
                                                             yaw_rel=angle)
        obj_found = True

    if not obj_found:
        try_without_success += 1
        continue

    # Everything is good, so we going to object
    robot.pick_from_pose(obj_pose)

    # Computing new place pose
    offset_x = count % grid_dimension[0] - grid_dimension[0] // 2
    offset_y = (count // grid_dimension[1]) % 3 - grid_dimension[1] // 2
    offset_z = count // (grid_dimension[0] * grid_dimension[1])
    place_pose = center_conditioning_pose.copy_with_offsets(0.05 * offset_x, 0.05 * offset_y, 0.025 * offset_z)

    # Placing
    robot.place_from_pose(place_pose)

    try_without_success = 0
    count += 1