def runTrainning(epochs):
config = ProjectConfig()
config.NAME = "MyMRCNN_origin_Model"
config.display()
dataSetFact = dataSetlib.ImageDataSetForMaskFactory(WORK_DIR)
dataSetFact.initialize(WORK_DIR + "/transformed_train.xlsx",
WORK_DIR + '/data_train.xlsx',
WORK_DIR + '/data_val.xlsx')
dataSetFact.preload_images()
dataSetFact.preload_images()
dataset_train, dataset_val = dataSetFact.getDataSet()
dataset_train.prepare()
dataset_val.prepare()
# Validation dataset
model = modellib.MaskRCNN(mode="training",
config=config,
model_dir=MODEL_DIR)
model.load_weights(COCO_MODEL_PATH,
by_name=True,
exclude=[
"mrcnn_class_logits", "mrcnn_bbox_fc", "mrcnn_bbox",
"mrcnn_mask"
])
model.train(dataset_train,
dataset_val,
learning_rate=config.LEARNING_RATE,
epochs=50,
layers='heads')
def runTrainning(epochs):
config = Config()
config.NAME = "MyMRCNN_WHOLE_Model"
config.display()
config.MEAN_PIXEL = np.array([66.47, 71.05, 83.27])
dataSetFact = dataSetlib.ImageDataTrainningFactory(WORK_DIR)
dataSetFact.initialize(WORK_DIR + "/clean_train_images.xlsx",
WORK_DIR + '/clean_data_train.xlsx',
WORK_DIR + '/clean_data_val.xlsx')
dataSetFact.preload_images()
dataset_train, dataset_val = dataSetFact.getDataSet()
dataset_train.prepare()
dataset_val.prepare()
dataset_train.noMask = dataset_val.noMask = True
# Validation dataset
model = modellib.MyBackboneModel(mode="training",
config=config,
model_dir=MODEL_DIR)
model.load_weights(model.find_last(), by_name=True)
learning_rate = 0.001
model.train(dataset_train,
dataset_val,
learning_rate=learning_rate,
epochs=50,
layers='all')
def runTrainning(epochs):
config = cfg.CloudPatternConfig()
config.NAME = "MyMRCNN_Inherited_Model"
config.BACKBONE = graph.dense_graph_simple_long
config.COMPUTE_BACKBONE_SHAPE = compute_backbone_shapes
config.IMAGE_SHAPE = [384, 576, 3]
config.display()
dataSetFact = dataSetlib.ImageDataSetForMRCNNFactory(WORK_DIR)
dataSetFact.initialize(WORK_DIR + "/mrcnn_training_images.xlsx",
WORK_DIR + '/mrcnn_data_train.xlsx',
WORK_DIR + '/mrcnn_data_val.xlsx')
dataSetFact.preload_images()
dataSetFact.preload_images()
dataset_train, dataset_val = dataSetFact.getDataSet()
dataset_train.prepare()
dataset_val.prepare()
# Validation dataset
model = modellib.MaskRCNN(mode="training",
config=config,
model_dir=MODEL_DIR)
model.load_weights(INITIAL_MODEL_PATH,
by_name=True,
exclude=[
"mrcnn_class_logits", "mrcnn_bbox_fc", "mrcnn_bbox",
"mrcnn_mask"
])
model.load_weights(model.find_last(), by_name=True)
model.train(dataset_train,
dataset_val,
learning_rate=config.LEARNING_RATE,
epochs=150,
layers='heads')
def runTesting():
config = Config()
config.NAME = "MyMRCNN_WHOLE_Model"
config.display()
dataSetFact = dataSetlib.ImageDataSetForMaskFactory(WORK_DIR)
dataSetFact.initialize(WORK_DIR + "/transformed_train.xlsx",
WORK_DIR + '/data_train.xlsx',
WORK_DIR + '/data_val.xlsx')
dataSetFact.preload_images()
dataset_train, dataset_val = dataSetFact.getDataSet()
dataset_train.prepare()
dataset_val.prepare()
# Validation dataset
df = pd.read_excel(WORK_DIR + "/data_val.xlsx")
model = modellib.MyBackboneModel(mode="training",
config=config,
model_dir=MODEL_DIR)
model.load_weights(model.find_last(), by_name=True)
totalScore = 0
counts = 0
classes = ["Gravel", "Sugar", "Fish", "Flower"]
trueSum = 0
predSum = 0
intersects = 0
for index, row in df.iterrows():
img_key = row["image_id"]
trueclzMasks = np.full((384, 576, 4), 0.)
inputmasks = np.full((24, 36, 4), 0)
classVect = generateClassVec(row)
for idx, clz in enumerate(classes):
if classVect[idx] == 1:
print(WORK_DIR + "/masks_shrinked/" + img_key + "_" + clz +
".png")
clzMask = cv2.imread(WORK_DIR + "/masks_shrinked/" + img_key +
"_" + clz + ".png")
clzMask = clzMask[:, :, [0]]
clzMask = np.logical_and(clzMask, clzMask).astype(np.float32)
trueclzMasks[:, :, [idx]] = clzMask
image = cv2.imread(WORK_DIR + "/train_image_shrinked/" + img_key +
".jpg")
image = cv2.resize(image, (config.IMAGE_MAX_DIM, config.IMAGE_MIN_DIM),
interpolation=cv2.INTER_LINEAR)
outputs = model.keras_model.predict([[image], [inputmasks]])
logits = outputs[0]
predclzMasks = logits[0]
resizedPredClzMasks = cv2.resize(predclzMasks.astype(np.float32),
(576, 384),
interpolation=cv2.INTER_NEAREST)
resizedPredClzMasks[resizedPredClzMasks >= 0.3] = 1.
resizedPredClzMasks[resizedPredClzMasks < 0.3] = 0.
# for i in range(4):
# resizedPredClzMasks[:,:,[i]] = postProcess(resizedPredClzMasks[:,:,[i]],image)
trueSum += np.sum(trueclzMasks)
predSum += np.sum(resizedPredClzMasks)
intersects += np.sum(
np.logical_and(resizedPredClzMasks,
trueclzMasks).astype(np.float32))
print("The score is ", 2 * intersects / (predSum + trueSum))
def runTesting():
config = Config()
config.NAME = "MyMRCNN_WHOLE_Model"
config.display()
dataSetFact = dataSetlib.ImageDataTrainningFactory(WORK_DIR)
dataSetFact.initialize(WORK_DIR + "/transformed_train.xlsx",
WORK_DIR + '/data_train.xlsx',
WORK_DIR + '/data_val.xlsx')
dataSetFact.preload_images()
dataset_train, dataset_val = dataSetFact.getDataSet()
dataset_train.prepare()
dataset_val.prepare()
dataset_train.noMask = dataset_val.noMask = True
model = modellib.MyBackboneModel(mode="inference",
config=config,
model_dir=MODEL_DIR)
model.load_weights(model.find_last(), by_name=True)
image_ids = dataset_val._image_ids
df_data = pd.read_excel(WORK_DIR + '/data_val.xlsx')
df = pd.DataFrame(columns=["image_id","True_Gravel","True_Sugar","True_Fish","True_Flower","Pred_Gravel",\
"Pred_Sugar","Pred_Fish","Pred_Flower","Match"],index=[])
for id in image_ids:
image, gt_class_ids, gt_masks = datagenerator.load_image_gt(
dataset_val,
config,
id,
augment=False,
augmentation=None,
use_mini_mask=config.USE_MINI_MASK)
batch_images = np.zeros((1, ) + image.shape, dtype=np.float32)
batch_gt_class_ids = np.zeros((1, config.NUM_CLASSES), dtype=np.int32)
batch_gt_masks = np.zeros(
(1, gt_masks.shape[0], gt_masks.shape[1], config.NUM_CLASSES),
dtype=gt_masks.dtype)
batch_images[0] = datagenerator.mold_image(image.astype(np.float32),
config)
output = model.keras_model.predict([batch_images, batch_gt_class_ids])
img_file_id = dataset_val.image_info[id]["id"]
true_classes = df_data.loc[img_file_id].values[1:]
classes = np.reshape(np.round(output[2]), [-1])
Match = True
for idx, v in enumerate(classes):
if v != true_classes[idx]:
Match = False
if img_file_id not in df.index:
df.loc[img_file_id] = [img_file_id] + true_classes.tolist(
) + classes.tolist() + [1. if Match else 0.]
print(img_file_id)
df.to_excel(WORK_DIR + "/MyMRCNN_WHOLE_Model_multi_class_pred_6970.xlsx")
def runTesting():
config = Config()
config.NAME = "MyMRCNN_WHOLE_Model"
config.display()
dataSetFact = dataSetlib.ImageDataTrainningFactory(WORK_DIR)
dataSetFact.initialize(WORK_DIR + "/clean_train_images.xlsx",
WORK_DIR + '/clean_data_train.xlsx',
WORK_DIR + '/clean_data_val.xlsx')
dataSetFact.preload_images()
dataset_train, dataset_val = dataSetFact.getDataSet()
dataset_train.prepare()
dataset_val.prepare()
dataset_train.noMask = dataset_val.noMask = True
model = modellib.MyBackboneModel(mode="inference",
config=config,
model_dir=MODEL_DIR)
model.load_weights(WEIGHT_DIR, by_name=True)
image_ids = dataset_val._image_ids
df = pd.DataFrame(columns=["image_id", "Trueth", "Pred", "Match"],
index=[])
for id in image_ids:
image, gt_class_ids, gt_masks = datagenerator.load_image_gt(
dataset_val,
config,
id,
augment=False,
augmentation=None,
use_mini_mask=config.USE_MINI_MASK)
batch_images = np.zeros((1, ) + image.shape, dtype=np.float32)
batch_gt_class_ids = np.zeros((1, config.NUM_CLASSES), dtype=np.int32)
batch_gt_masks = np.zeros(
(1, gt_masks.shape[0], gt_masks.shape[1], config.NUM_CLASSES),
dtype=gt_masks.dtype)
batch_images[0] = datagenerator.mold_image(image.astype(np.float32),
config)
output = model.keras_model.predict([batch_images, batch_gt_class_ids])
class_id = np.argmax(output[0])
class_names = ["Gravel", "Sugar", "Fish", "Flower"]
pred_class_name = class_names[class_id]
img_file_id = dataset_val.image_info[id]["id"]
img_true_class = img_file_id.split("_")[1]
if img_file_id not in df.index:
df.loc[img_file_id] = [
img_file_id, img_true_class, pred_class_name,
1. if img_true_class == pred_class_name else 0.
]
print(img_file_id)
df.to_excel(WORK_DIR + "/MyMRCNN_WHOLE_Model_pred_MEAN_37_epoc.xlsx")
def runTesting():
config = Config()
config.NAME = "MyMRCNN_WHOLE_Model"
config.display()
dataSetFact = dataSetlib.ImageDataTrainningFactory(WORK_DIR)
dataSetFact.initialize(WORK_DIR + "/transformed_train.csv",
WORK_DIR + '/data_train_s.xlsx',
WORK_DIR + '/data_val_s.xlsx')
dataSetFact.preload_images()
dataset_train, dataset_val = dataSetFact.getDataSet()
dataset_train.prepare()
dataset_val.prepare()
image, gt_class_ids, gt_masks = datagenerator.load_image_gt(
dataset_val,
config,
1,
augment=False,
augmentation=None,
use_mini_mask=config.USE_MINI_MASK)
batch_images = np.zeros((1, ) + image.shape, dtype=np.float32)
batch_gt_class_ids = np.zeros((1, config.NUM_CLASSES), dtype=np.int32)
batch_gt_masks = np.zeros(
(1, gt_masks.shape[0], gt_masks.shape[1], config.NUM_CLASSES),
dtype=gt_masks.dtype)
batch_images[0] = datagenerator.mold_image(image.astype(np.float32),
config)
model = modellib.MyBackboneModel(mode="training",
config=config,
model_dir=MODEL_DIR)
model.load_weights(model.find_last(), by_name=True)
output = model.keras_model.predict(
[batch_images, batch_gt_class_ids, batch_gt_masks])
masks = output[0][0]
masks = np.round(masks) * 255
masks = cv2.resize(masks, (2100, 1400))
masklst = []
class_names = ["Gravel", "Sugar", "Fish", "Flower"]
for i in range(masks.shape[-1]):
masklst.append(masks[:, :, i])
for i, name in enumerate(class_names):
fname = dataset_val.image_info[1]["id"] + "_" + name + ".png"
cv2.imwrite(WORK_DIR + "/" + fname, masklst[i])
print(output[0].shape)
print('hai')
def runTrainning(epochs):
config = Config()
config.NAME = "MyMRCNN_WHOLE_Model"
config.display()
dataSetFact = dataSetlib.ImageDataTrainningFactory(WORK_DIR)
dataSetFact.initialize(WORK_DIR + "/transformed_train.csv",
WORK_DIR + '/data_train_s.xlsx',
WORK_DIR + '/data_val_s.xlsx')
dataSetFact.preload_images()
dataset_train, dataset_val = dataSetFact.getDataSet()
dataset_train.prepare()
dataset_val.prepare()
# Validation dataset
model = modellib.MyBackboneModel(mode="training",
config=config,
model_dir=MODEL_DIR)
# model.load_weights(model.find_last(), by_name=True)
learning_rate = 0.001
model.train(dataset_train,
dataset_val,
learning_rate=learning_rate,
epochs=epochs,
layers='all')