def evaluate_carla_segmentation_model(path_to_checkpoint, data_folder):
segm_dir = data_folder / "segmentation"
img_dir = data_folder / "rgb"
results = evaluate(checkpoints_path=path_to_checkpoint, inp_images_dir=img_dir, annotations_dir=segm_dir)
return results
def evaluate_mapillary_segmentation_model(path_to_checkpoint, data_folder):
segm_dir = data_folder / "mapillary_depth_segm/segm_annotations_prepped_val"
img_dir = data_folder / "mapillary_depth_segm/images_prepped_val"
results = evaluate(checkpoints_path=path_to_checkpoint, inp_images_dir=img_dir, annotations_dir=segm_dir)
return results
def test_model():
model_name = "fcn_8"
h = 224
w = 256
n_c = 100
check_path = tempfile.mktemp()
m = all_models.model_from_name[model_name](n_c, input_height=h, input_width=w)
m.train(train_images=tr_im,
train_annotations=tr_an,
steps_per_epoch=2,
epochs=2,
checkpoints_path=check_path
)
m.predict_segmentation(np.zeros((h, w, 3))).shape
predict_multiple(
inp_dir=te_im, checkpoints_path=check_path, out_dir="/tmp")
predict_multiple(inps=[np.zeros((h, w, 3))]*3,
checkpoints_path=check_path, out_dir="/tmp")
ev = m.evaluate_segmentation( inp_images_dir=te_im , annotations_dir=te_an )
assert ev['frequency_weighted_IU'] > 0.01
print(ev)
o = predict(inp=np.zeros((h, w, 3)), checkpoints_path=check_path)
o.shape
ev = evaluate( inp_images_dir=te_im , annotations_dir=te_an , checkpoints_path=check_path)
assert ev['frequency_weighted_IU'] > 0.01
def main():
# Setup the testing parameters, input paths etc.,
checkpoints_path = "./checkpoints_mobilenet_unet_2class/"
test_images_path = "./dataset2/images_prepped_test/"
test_annotations_path = "./dataset2/annotations_prepped_test_2class/"
# Model checkpoint path should exist, else it is an error.
if not os.path.exists(checkpoints_path):
print("[ERROR] invalid checkpoints path {}".format(checkpoints_path))
sys.exit(1)
# Load model from checkpoint path.
model = model_from_checkpoint_path(checkpoints_path)
# Get Intersection over Union (IoU) results for the test data set.
# The model is picked up from the latest model in the checkpoints_path.
# The input images and masks (annotations) are picked up from their paths.
test_results = evaluate(inp_images_dir=test_images_path,
annotations_dir=test_annotations_path,
model=model)
print(test_results)
print("Evaluation complete")
def main():
# Setup the training parameters, input paths etc.,
checkpoints_path = "./checkpoints_mobilenet_unet_2class/"
test_images_path = "./dataset2/images_prepped_test/"
test_masks_path = "./dataset2/masks_prepped_test_2class/"
predicted_masks_path = "./dataset2/masks_predicted/"
predicted_overlay_path = "./dataset2/overlay_predicted/"
# Enable printing of IoU scores for tests.
print_test_results = 0
# Model checkpoint path should exist, else it is an error.
if not os.path.exists(checkpoints_path):
print("[ERROR] invalid checkpoints path {}".format(checkpoints_path))
sys.exit(1)
# Get Intersection over Union (IoU) results for the test data set.
# The model is picked up from the latest model in the checkpoints_path.
# The input images and masks (masks) are picked up from their paths.
if print_test_results:
test_results = evaluate(inp_images_dir=test_images_path,
masks_dir=test_masks_path,
checkpoints_path=checkpoints_path)
print(test_results)
print("Test results complete")
# Create output directory to store predicted masks and overlays.
# Remove any existing png files.
if not os.path.exists(predicted_masks_path):
os.makedirs(predicted_masks_path)
if not os.path.exists(predicted_overlay_path):
os.makedirs(predicted_overlay_path)
for f in glob.glob(predicted_masks_path + '/*.png'):
os.remove(f)
for f in glob.glob(predicted_overlay_path + '/*.png'):
os.remove(f)
# Predict results for the input images and store in output directory.
# Store both the predicted masks and the overlaid images with masks.
# Step 1. Load model from checkpoint path.
model = model_from_checkpoint_path(checkpoints_path)
# Step 2. Read images one by one and feed it in into predict function.
# Output predicted mask is written to the predicted masks directory.
# Assume that input images are in png format.
for infile in glob.glob(test_images_path + '/*.png'):
filename = os.path.split(infile)[1]
outfile = os.path.join(predicted_masks_path, filename)
overlayfile = os.path.join(predicted_overlay_path, filename)
print("Predicting mask for file {}, output in {}".format(
infile, outfile))
# Create predicted mask from input file, and store it in outfile.
predict(inp=infile, out_fname=outfile, model=model)
# Overlay the predicted mask file over input file for display.
overlay_image_with_mask(image_file=infile,
mask_file=outfile,
overlay_file=overlayfile)
print("Prediction complete")
from keras_segmentation.predict import predict_multiple, evaluate, model_from_checkpoint_path
from keras_segmentation.data_utils.visualize_dataset import visualize_segmentation_dataset
checkpoints_path = []
checkpoints_path.append("./saveModel/vgg_unet_1")
# checkpoints_path.append("./saveModel/vgg_pspnet_1")
# checkpoints_path.append("./saveModel/vgg_segnet_1")
# checkpoints_path.append("./saveModel/fcn_32_vgg_1")
dictModels = {}
dictModels[0] = "vgg_unet"
dictModels[1] = "vgg_pspnet"
dictModels[2] = "vgg_segnet"
dictModels[3] = "fcn_32_vgg"
for ia in dictModels:
output = evaluate(
checkpoints_path=f"./saveModel/{dictModels[ia]}_1",
inp_images_dir="./dataset1/test",
annotations_dir = "./dataset1/ano_test",
)
print(dictModels[ia], end=" | ")
print(output)
