def numpy_saver(self, data, name):
if len(data.shape) == 4:
image.saves(os.path.join(self.folder, name), data)
elif len(data.shape) == 3:
image.save(os.path.join(self.folder, name + ".png"), data)
else:
raise "Wrong type, make sure your image shape=(n,h,w,c) or (h,w,c)"
def SPImageSeg(context):
args = context.args
images = args.inputImage
for idx, img in enumerate(images):
splitX, splitY = (
math.ceil(img.shape[1] / args.x),
math.ceil(img.shape[0] / args.y),
)
i = 0
for m in range(splitX):
for n in range(splitY):
filename = (
os.path.splitext(
storage.delimiter.join(
images.images[idx].split(storage.delimiter)[8:]
)
)[0]
+ "_"
+ str(i)
+ ".png"
)
image.save(
os.path.join(args.outputImage, filename,),
img[
n * args.y : (n + 1) * args.y, m * args.x : (m + 1) * args.x, :,
],
)
i += 1
return args.outputImage
def list_saver(self, data, name):
if isinstance(data[0], np.ndarray) and len(data[0].shape) == 3:
image.saves(os.path.join(self.folder, name), data)
elif isinstance(data[0], tuple) and len(data[0]) == 2:
for path, img in data:
image.save(os.path.join(self.folder, path), img)
else:
raise "Wrong type, make sure your image shape=(n,h,w,c)"
def visualizer(self, imgs, datas, output_dir):
for img, data in zip(imgs, datas):
im = image.read(img)
v = Visualizer(
im[:, :, ::-1],
MetadataCatalog.get(self.cfg.DATASETS.TRAIN[0]),
scale=1.2,
)
v = v.draw_instance_predictions(data["instances"].to("cpu"))
image.save(
os.path.join(output_dir, os.path.basename(img)),
v.get_image()[:, :, ::-1],
)
def SPObjectDetectionPrediction(context):
args = context.args
predict_image = get_predict_file(args.predictImages)
bboxes = []
i = 0
for inputImage in get_od_image(predict_image):
predictedBox, imageBox = args.model.predict(inputImage)
bboxes.append(predictedBox)
image.save(
os.path.join(
args.images,
storage.delimiter.join(predict_image[i].split(
storage.delimiter)[8:]),
),
imageBox[:, :, ::-1],
)
i += 1
return bboxes, args.images
def SPImageBox(context):
args = context.args
images = args.inputImage
files = []
try:
if args.inputData:
jsonFile = os.path.join(args.inputData, "project.json")
with open(jsonFile, "rb") as load_f:
fileInfo = json.load(load_f)
for i, j in fileInfo["metadata"].items():
files.append(os.path.join(images.folder, j["vid"]))
try:
filename = (os.path.splitext(j["vid"])[0] + "_" +
j["av"]["1"] + ".png")
except:
filename = (os.path.splitext(j["vid"])[0] + "_" +
i.split("_")[-1] + ".png")
xy = j["xy"][1:]
img = image.read(os.path.join(images.folder, j["vid"]))
image.save(
os.path.join(
args.outputImage,
filename,
),
img[int(xy[1]):int(xy[1] + xy[3]),
int(xy[0]):int(xy[0] + xy[2]), :, ],
)
elif args.xy:
for idx, img in enumerate(images):
files.append(images.images[idx])
x = int(args.xy[0] + args.x) if args.x else img.shape[1] + 1
y = int(args.xy[1] + args.y) if args.y else img.shape[0] + 1
image.save(
os.path.join(
args.outputImage,
storage.delimiter.join(images.images[idx].split(
storage.delimiter)[8:]),
),
img[int(args.xy[1]):y,
int(args.xy[0]):x, :, ],
)
except:
logger.info("can not find project.json or json format error")
for idx, img in enumerate(images):
if images.images[idx] not in files:
image.save(
os.path.join(
args.outputImage,
storage.delimiter.join(images.images[idx].split(
storage.delimiter)[8:]),
),
img,
)
return args.outputImage
def SPROI(context):
args = context.args
image_folder = args.inputData1
detection_dict = args.inputData2
images = os.listdir(image_folder)
for i, img in enumerate(images):
image = Image.open(os.path.join(image_folder, img))
im_height, im_width = image.size
boxes = detection_dict[i]["detection_boxes"]
y_min = boxes[0][0] * im_height
x_min = boxes[0][1] * im_width
y_max = boxes[0][2] * im_height
x_max = boxes[0][3] * im_width
x_2 = int((x_min + x_max) / 2) + 72
y_1 = int(y_max + 10)
x_1 = int(x_2 - 72)
y_2 = int(y_1 + 72)
box = (x_1, y_1, x_2, y_2)
roi = image.crop(box)
im.save(os.path.join(args.outputData1, img), np.array(roi)[:, :, -1])
return args.outputData1