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main.py
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main.py
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import rawpy
import imageio
from utils import ensure_dir, file_message, files
import cv2
import numpy as np
from PIL import Image
import shutil
import json
import os
import pickle
DIR = "./photos/plant1_20180615/entire_plant"
APP_DIR = "C:/Users/Breght/Documents/Doctoraat/Annotator"
RAW_DIR = DIR + "/raw"
IMG_DIR = DIR + "/images"
ANN_DIR = DIR + "/annotations"
CAL_DIR = DIR + "/calibration"
UND_DIR = DIR + "/undistorted"
UNDCR_DIR = DIR + "/undistorted_cropped"
ARUCO_DIR = DIR + "/ArUco"
RESIZE_DIR = DIR + "/resized"
IMG_TRANSFER_DIR = DIR + "/img_transfer"
ANN_TRANSFER_DIR = DIR + "/ann_transfer"
def raw2jpg(in_f, out_f):
with rawpy.imread(in_f) as raw:
rgb = raw.postprocess()
imageio.imsave(out_f, rgb)
file_message(out_f)
def batch_raw2jpg(in_dir=RAW_DIR, out_dir=IMG_DIR):
ensure_dir(out_dir)
for in_f, out_f in files(in_dir=in_dir, out_dir=out_dir, in_ext=".nef", out_ext=".jpg"):
raw2jpg(in_f, out_f)
def threshold_segmentation(in_f, out_f, limits, colour_space="HSV"):
img = cv2.imread(in_f, 1)
if colour_space == "BGR":
cvt = img.copy()
elif colour_space == "LAB":
cvt = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
elif colour_space == "HSV":
cvt = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
else:
cvt = img.copy()
limits = np.array(list(zip(*limits)))
mask = cv2.inRange(cvt, limits[0], limits[1])
mask = (mask / 255).astype(int)
encode_segmentation(mask, out_f)
file_message(out_f)
def encode_segmentation(segm, filename):
Image.fromarray(np.stack([
np.bitwise_and(segm, 255),
np.bitwise_and(segm >> 8, 255),
np.bitwise_and(segm >> 16, 255),
], axis=2).astype(np.uint8)).save(filename)
def decode_segmentation(filename):
encoded = np.array(Image.open(filename))
annotation = np.bitwise_or(np.bitwise_or(
encoded[:, :, 0].astype(np.uint32),
encoded[:, :, 1].astype(np.uint32) << 8),
encoded[:, :, 2].astype(np.uint32) << 16)
return annotation
def batch_threshold_segmentation(limits=([0, 255], [54, 255], [0, 255]), colour_space="HSV", in_dir=IMG_DIR, out_dir=ANN_DIR):
ensure_dir(out_dir)
for in_f, out_f in files(in_dir=in_dir, out_dir=out_dir, in_ext=".jpg", out_ext=".png"):
threshold_segmentation(in_f, out_f, limits, colour_space)
def undistortion_parameters(cal_f):
dims = (7, 9)
coords_3d = np.zeros((dims[0]*dims[1], 3), np.float32)
coords_3d[:, :2] = np.mgrid[0:dims[0], 0:dims[1]].T.reshape(-1, 2)
squares_3d = []
squares_2d = []
img = cv2.imread(cal_f, 0)
ret, img = cv2.threshold(img, 80, 255, cv2.THRESH_BINARY)
ret, corners = cv2.findChessboardCorners(img, dims, None)
if ret:
squares_3d.append(coords_3d)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
corners = cv2.cornerSubPix(img, corners, (11, 11), (-1, -1), criteria)
squares_2d.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(squares_3d, squares_2d, img.shape[::-1], None, None)
h, w = img.shape[:2]
newmtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))
mapx, mapy = cv2.initUndistortRectifyMap(mtx, dist, None, newmtx, (w, h), 5)
return mapx, mapy, roi
else:
raise Exception("Chessboard detection failed")
def undistortion(in_f, out_f, mapx, mapy, roi, crop=True):
img = cv2.imread(in_f, 1)
undist = cv2.remap(img, mapx, mapy, cv2.INTER_LINEAR)
if crop:
x, y, w, h = roi
undist = undist[y:y+h, x:x+w]
cv2.imwrite(out_f, undist)
file_message(out_f)
def batch_undistortion(cal_dir=CAL_DIR, in_dir=IMG_DIR, out_dir=UND_DIR, crop=True):
ensure_dir(out_dir)
cal_f = files(dir=cal_dir, ext=".jpg")[0]
mapx, mapy, roi = undistortion_parameters(cal_f)
for in_f, out_f in files(in_dir=in_dir, out_dir=out_dir, in_ext=".jpg", out_ext=".jpg"):
undistortion(in_f, out_f, mapx, mapy, roi, crop)
def detect_markers(in_f, out_f, aruco_dict, aruco_params, show=False):
img = cv2.imread(in_f, cv2.IMREAD_COLOR)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
corners, ids, rejected = cv2.aruco.detectMarkers(gray, aruco_dict, parameters=aruco_params)
with open(out_f, "wb") as out:
pickle.dump([corners, ids, rejected], out)
file_message(out_f)
if show:
img_with_aruco = cv2.aruco.drawDetectedMarkers(img, corners, ids, (0, 255, 0))
cv2.imshow("aruco", img_with_aruco)
cv2.waitKey(0)
def batch_detect_markers(in_dir=UNDCR_DIR, out_dir=ARUCO_DIR, show=True):
ensure_dir(out_dir)
aruco_dict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_7X7_1000)
aruco_params = cv2.aruco.DetectorParameters_create()
for in_f, out_f in files(in_dir=in_dir, out_dir=out_dir, in_ext=".jpg", out_ext=".pkl"):
detect_markers(in_f, out_f, aruco_dict=aruco_dict, aruco_params=aruco_params, show=show)
def resize(in_f, out_f, ratio=.5):
img = cv2.imread(in_f, 1)
dim = (int(ratio * img.shape[1]), int(ratio * img.shape[0]))
resized = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
cv2.imwrite(out_f, resized)
file_message(out_f)
def batch_resize(in_dir, out_dir, ratio=.5):
ensure_dir(out_dir)
for in_f, out_f in files(in_dir=in_dir, out_dir=out_dir, in_ext=".jpg", out_ext=".jpg"):
resize(in_f, out_f, ratio)
for in_f, out_f in files(in_dir=in_dir, out_dir=out_dir, in_ext=".png", out_ext=".png"):
resize(in_f, out_f, ratio)
def prepare_annotator(img_dir=IMG_DIR, ann_dir=ANN_DIR, app_dir=APP_DIR, classes=("background", "plant", "panicle")):
img_out_dir = app_dir + "/data/images"
for f in files(img_out_dir, ".jpg"):
os.remove(f)
for f in files(img_dir, ".jpg"):
shutil.copy(f, img_out_dir)
file_message(img_out_dir + "/" + os.path.basename(f))
ann_out_dir = app_dir + "/data/annotations"
for f in files(ann_out_dir, ".png"):
os.remove(f)
for f in files(ann_dir, ".png"):
shutil.copy(f, ann_out_dir)
file_message(ann_out_dir + "/" + os.path.basename(f))
jsonfile = files(app_dir + "/data", ".json")[0]
with open(jsonfile, "r") as read_file:
data = json.load(read_file)
data["labels"] = classes
data["imageURLs"] = ["data/images/" + os.path.basename(f) for f in files(img_out_dir, ".jpg")]
data["annotationURLs"] = ["data/annotations/" + os.path.basename(f) for f in files(ann_out_dir, ".png")]
with open(jsonfile, "w") as write_file:
json.dump(data, write_file)
file_message(jsonfile)
if __name__ == "__main__":
# step 1: convert images from raw to jpeg
batch_raw2jpg(RAW_DIR, IMG_DIR)
batch_raw2jpg(CAL_DIR, CAL_DIR)
# step 2: make images smaller for more convenient use in annotator tool
batch_resize(IMG_DIR, RESIZE_DIR, .5)
# step 3: do the segmentation of the resized images
batch_threshold_segmentation(limits=[[0, 255], [54, 255], [0, 255]], colour_space="HSV", in_dir=RESIZE_DIR, out_dir=ANN_DIR)
# step 4: prepare the annotator tool (give the tool the images and annotations)
# first create the IMG_TRANSFER_DIR and ANN_TRANSFER_DIR directories,
# and move some images and corresponding annotations in the folder,
# then run the prepare_annotator function
prepare_annotator(IMG_TRANSFER_DIR, ANN_TRANSFER_DIR, APP_DIR, ("background", "plant", "panicle"))
# step 5: do undistortion
batch_undistortion(CAL_DIR, IMG_DIR, UND_DIR, crop=False)
batch_undistortion(CAL_DIR, IMG_DIR, UNDCR_DIR, crop=True)
# step 6: detect markers
batch_detect_markers(UNDCR_DIR, ARUCO_DIR)