Calibration

0. Import packages

%matplotlib inline

import cv2
import matplotlib.pyplot

import openalea.phenomenal.data as phm_data
import openalea.phenomenal.display as phm_display
import openalea.phenomenal.calibration as phm_calib
from openalea.phenotyping_data.fetch import fetch_all_data

1. Detect chessboard corners in the images

1.0 Load Images

data_dir = fetch_all_data("plant_1/chessboard").parent
chessboard_images = phm_data.chessboard_images(data_dir)[0]
phm_display.show_image(chessboard_images["side"][42])

Downloading file 'plant_1/chessboard/points/chessboard_1.json' from 'https://raw.githubusercontent.com/openalea/phenotyping_data/main/data/plant_1/chessboard/points/chessboard_1.json' to '/home/docs/.cache/phenotyping_data'.
Downloading file 'plant_1/chessboard/points/chessboard_2.json' from 'https://raw.githubusercontent.com/openalea/phenotyping_data/main/data/plant_1/chessboard/points/chessboard_2.json' to '/home/docs/.cache/phenotyping_data'.
Downloading file 'plant_1/chessboard/points/image_points_target_1.json' from 'https://raw.githubusercontent.com/openalea/phenotyping_data/main/data/plant_1/chessboard/points/image_points_target_1.json' to '/home/docs/.cache/phenotyping_data'.
Downloading file 'plant_1/chessboard/points/image_points_target_2.json' from 'https://raw.githubusercontent.com/openalea/phenotyping_data/main/data/plant_1/chessboard/points/image_points_target_2.json' to '/home/docs/.cache/phenotyping_data'.
Downloading file 'plant_1/chessboard/side/42.jpg' from 'https://raw.githubusercontent.com/openalea/phenotyping_data/main/data/plant_1/chessboard/side/42.jpg' to '/home/docs/.cache/phenotyping_data'.
Downloading file 'plant_1/chessboard/side/42.png' from 'https://raw.githubusercontent.com/openalea/phenotyping_data/main/data/plant_1/chessboard/side/42.png' to '/home/docs/.cache/phenotyping_data'.

1.1 Create chessboard object

Create chessboard object with square size and shape of chessboard

square_size_of_chessboard = 47  # In mm
square_shape_of_chessboard = (8, 6)  # (8 square x 6 square on chessboard)

# BUILD CHESSBOARD OBJECT
chessboard = phm_calib.Chessboard(square_size_of_chessboard, square_shape_of_chessboard)

# DISPLAY IT
print(chessboard)

Chessboard Attributes :
Square size (mm): 47
Shape : (8, 6)

1.2 Detect corners from images

for id_camera in chessboard_images:
    for angle in chessboard_images[id_camera]:
        im = chessboard_images[id_camera][angle]
        found = chessboard.detect_corners(id_camera, angle, im, check_order=False)
        print(
            "Angle {} - Chessboard corners {}".format(
                angle, "found" if found else "not found"
            )
        )

8x6 checkerboard found (x1)
Angle 42 - Chessboard corners found

1.3 Display chessboard corners on images

angle = 42
img = chessboard_images["side"][angle].copy()

# DRAW RED POINT ON IMAGE CORNERS POSITION
points_2d = chessboard.image_points[id_camera][angle].astype(int)
for x, y in zip(points_2d[:, 0, 1], points_2d[:, 0, 0]):
    cv2.circle(img, (y, x), 5, (255, 0, 0), -1)

phm_display.show_image(img)

2.4 Dump & load

chessboard.dump("chessboard.json")

chessboard = phm_calib.Chessboard.load("chessboard.json")

2. Multi-camera calibration

2.0 Load chessboard object with all corners points

# Load image points
image_points = phm_data.image_points(data_dir)

2.1 Do calibration

# setup calibration layout
calibrator = phm_calib.Calibrator(
    south_camera=("side", 90, 5500),
    targets={"target_1": (45, 48), "target_2": (45, 48 + 180)},
    chessboards={"target_1": (47, 8, 6), "target_2": (47, 8, 6)},
)
calibrator.load_image_points(image_points)

# calibrate
calibration = calibrator.calibrate(verbose=False)

# Error of reprojection (in pixel distance)
print(calibration.calibration_statistics["mean_error"])

0.11771153048846127

2.2 Dump

# Dump
calibration.dump("calibration_camera_side.json")

2.6 Viewing calibration result

2.6.2 Show chessboard image with corners projection

angle = 42
circle_radius = 5
img = chessboard_images["side"][angle].copy()

# RED POINTS ARE POINTS POSITIONS DETECTED BY OPENCV CHESSBOARD DETECTION
pt_2d = image_points["target_1"].get_corners_2d("side")[42].astype(int)
for x, y in pt_2d:
    cv2.circle(img, (x, y), circle_radius, (255, 0, 0), -1)

# BLUE POINTS ARE POINTS POSITIONS PROJECTED BY CALIBRATION CHESSBOARD COMPUTATION
points_2d = calibration.get_target_projected("side", "target_1", 42).astype(int)
for x, y in pt_2d:
    cv2.circle(img, (x, y), circle_radius, (0, 0, 255), -1)
phm_display.show_image(img)