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 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 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 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)
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 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)