def shrink_roi(zipfile):
# Declare the ROI File
roi = read_roi_zip(zipfile)
# Convert the roi list
roi_list = list(roi.values())
print("Shrinking the size quarter...")
for a in roi_list:
for b in range(len(a['x'])):
a['x'][b] = int(a['x'][b] / 4)
for c in range(len(a['y'])):
a['y'][c] = int(a['y'][c] / 4)
os.remove(zipfile)
print("Export the data...")
print(roi_list)
for a in roi_list:
roi_obj = ROIPolygon(a['x'], a['y'])
with ROIEncoder(a['name'] + ".roi", roi_obj) as roi:
roi.write()
with ZipFile(zipfile, 'a') as myzip:
myzip.write(a['name'] + ".roi")
os.remove(a['name'] + ".roi")
from PymageJ.roi import ROIEncoder, ROIRect
roi_obj = ROIRect(20, 30, 40,
50) # Make ROIRect object specifing top, left, bottom, right
with ROIEncoder('roi_filepath.roi', roi_obj) as roi:
roi.write()
def run(self):
#WORK_DIR="/media/min20120907/Resources/Linux/MaskRCNN"
ROOT_DIR = os.path.abspath(self.WORK_DIR)
#self.append.emit(ROOT_DIR)
# Import Mask RCNN
sys.path.append(ROOT_DIR) # To find local version of the library
# import training functions
import mrcnn.utils
import mrcnn.visualize
import mrcnn.visualize
import mrcnn.model as modellib
from mrcnn.model import log
import cell
# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
# Path to Ballon trained weights
# You can download this file from the Releases page
# https://github.com/matterport/Mask_RCNN/releases
CELL_WEIGHTS_PATH = self.weight_path # TODO: update this path
DEVICE = self.DEVICE
config = cell.CustomConfig()
# Override the training configurations with a few
# changes for inferencing.
def parseInt(a):
filenum = ""
if int(a) >= 100 and int(a) < 1000:
filenum = "0" + str(a)
elif int(a) >= 10 and int(a) < 100:
filenum = "00" + str(a)
elif int(a) >= 1 and int(a) < 10:
filenum = "000" + str(a)
elif int(a) >= 1000 and int(a) < 10000:
filenum = str(a)
else:
filenum = "0000"
return filenum
class InferenceConfig(config.__class__):
# Run detection on one image at a time
GPU_COUNT = 1
IMAGES_PER_GPU = 1
config = InferenceConfig()
config.display()
# Device to load the neural network on.
# Useful if you're training a model on the same
# machine, in which case use CPU and leave the
# GPU for training.
# Inspect the model in training or inference modes
# values: 'inference' or 'training'
# TODO: code for 'training' test mode not ready yet
TEST_MODE = "inference"
# Create model in inference mode
with tf.device(DEVICE):
model = modellib.MaskRCNN(mode="inference",
model_dir=MODEL_DIR,
config=config)
# Or, load the last model you trained
weights_path = self.weight_path
# Load weights
self.append.emit("Loading weights " + str(weights_path))
model.load_weights(weights_path, by_name=True)
self.append.emit("loaded weights!")
for d in os.walk(self.DETECT_PATH):
for folder in d[1]:
filenames = []
self.append.emit("folder" + str(folder))
for f in glob.glob(self.DETECT_PATH + "/" + str(folder) +
"/*" + self.txt):
if os.path.splitext(f)[-1] == str(self.txt):
filenames.append(f)
#bar = progressbar.ProgressBar(max_value=len(filenames))
self.progressBar_setMaximum.emit(len(filenames))
#filenames = sorted(filenames, key=lambda a : int(a.replace(self.format_txt.toPlainText(), "").replace("-", " ").split(" ")[6]))
filenames.sort()
file_sum = 0
#self.append.emit(str(np.array(filenames)))
for j in range(len(filenames)):
self.append.emit("files: " + str(filenames))
self.progressBar.emit(j)
image = skimage.io.imread(os.path.join(filenames[j]))
h, w, c = image.shape
dim_o = (h, w)
h = int(h * 4)
w = int(w * 4)
dim = (h, w) # new size with 4x increased
resized = cv2.resize(image,
dim,
interpolation=cv2.INTER_AREA)
cv2.imwrite(os.path.join(filenames[j]), resized)
# Run object detection
results = model.detect([image], verbose=0)
r = results[0]
data = numpy.array(r['masks'], dtype=numpy.bool)
# self.append.emit(data.shape)
edges = []
for a in range(len(r['masks'][0][0])):
# self.append.emit(data.shape)
# data[0:256, 0:256] = [255, 0, 0] # red patch in upper left
mask = (numpy.array(r['masks'][:, :, a] * 255)).astype(
numpy.uint8)
print("image shape: ", mask.shape)
img = Image.fromarray(mask, 'L')
g = cv2.Canny(np.array(img), 10, 100)
contours, hierarchy = cv2.findContours(
mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
resized = cv2.resize(image,
dim_o,
interpolation=cv2.INTER_AREA)
cv2.imwrite(os.path.join(filenames[j], resized))
self.progressBar.emit(j)
for contour in contours:
file_sum += 1
x = [i[0][0] / 4 for i in contour]
y = [i[0][1] / 4 for i in contour]
if (len(x) >= 100):
roi_obj = ROIPolygon(x, y)
with ROIEncoder(
parseInt(j + 1) + "-" +
parseInt(file_sum) + "-0000" + ".roi",
roi_obj) as roi:
roi.write()
with ZipFile(
self.ROI_PATH + "/" + str(folder) +
"-" + str(self.conf_rate) + "-" +
str(self.epoches) + "-" +
str(self.step) + ".zip", 'a') as myzip:
myzip.write(
parseInt(j + 1) + "-" +
parseInt(file_sum) + "-0000" + ".roi")
self.append.emit("Compressed " +
parseInt(j + 1) + "-" +
parseInt(file_sum) +
"-0000" + ".roi")
os.remove(
parseInt(j + 1) + "-" +
parseInt(file_sum) + "-0000" + ".roi")
edges = []
for a in range(len(r['masks'][0][0])):
# print(data.shape)
# data[0:256, 0:256] = [255, 0, 0] # red patch in upper left
mask = (numpy.array(r['masks'][:, :, a] * 255)).astype(numpy.uint8)
img = Image.fromarray(mask, 'L')
g = cv2.Canny(np.array(img), 10, 100)
contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
bar.update(j)
for contour in contours:
file_sum += 1
x = [i[0][0] for i in contour]
y = [i[0][1] for i in contour]
if (len(x) >= 100):
roi_obj = ROIPolygon(x, y)
with ROIEncoder(
parseInt(j + 1) + "-" + parseInt(file_sum) + "-0000" +
".roi", roi_obj) as roi:
roi.write()
with ZipFile("low-res-AI" + ".zip", 'a') as myzip:
myzip.write(
parseInt(j + 1) + "-" + parseInt(file_sum) + "-0000" +
".roi")
#print("Compressed "+parseInt(j+1)+"-"+parseInt(file_sum)+"-0000"+".roi")
os.remove(
parseInt(j + 1) + "-" + parseInt(file_sum) + "-0000" +
".roi")