File size: 3,812 Bytes
c18b721 56964c4 c18b721 5877e39 183bd3e c18b721 fc0b387 c18b721 8c7151e c18b721 d2b2be7 c18b721 5877e39 c18b721 8c7151e 183bd3e c18b721 183bd3e c18b721 183bd3e c18b721 37db9a5 c18b721 56964c4 dc58ce3 183bd3e d2b2be7 56964c4 d2b2be7 56964c4 c18b721 183bd3e c18b721 183bd3e c18b721 183bd3e c18b721 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 |
import argparse
import cv2
from . import *
from .classes import *
from .funcs import *
from abcli import file
import abcli.logging
import logging
logger = logging.getLogger(__name__)
parser = argparse.ArgumentParser(name, description=f"{name}-{version}")
parser.add_argument(
"task",
type=str,
default="",
help="eval,ingest,list,predict,predict_image,preprocess,train",
)
parser.add_argument(
"--objects",
type=str,
default="",
)
parser.add_argument(
"--color",
type=int,
default=0,
help="0/1",
)
parser.add_argument(
"--convnet",
type=int,
default=1,
help="0/1",
)
parser.add_argument(
"--count",
type=int,
default=-1,
)
parser.add_argument(
"--data_path",
type=str,
default="",
)
parser.add_argument(
"--epochs",
default=10,
type=int,
help="",
)
parser.add_argument(
"--infer_annotation",
type=int,
default=1,
help="0/1",
)
parser.add_argument(
"--input_path",
type=str,
default="",
)
parser.add_argument(
"--is_url",
type=int,
default=0,
help="0/1",
)
parser.add_argument(
"--model_path",
type=str,
default="",
)
parser.add_argument(
"--output_path",
type=str,
default="",
)
parser.add_argument(
"--purpose",
type=str,
default="",
help="predict/train",
)
parser.add_argument(
"--window_size",
type=int,
default=default_window_size,
)
args = parser.parse_args()
success = False
if args.task == "eval":
success = eval(args.input_path, args.output_path)
elif args.task == "list":
Image_Classifier().load(args.model_path)
success = True
elif args.task == "predict":
classifier = Image_Classifier()
if classifier.load(args.model_path):
success, test_images = file.load(f"{args.data_path}/test_images.pyndarray")
if success:
_, test_labels = file.load(
f"{args.data_path}/test_labels.pyndarray",
civilized=True,
default=None,
)
success, prediction = classifier.predict(
test_images / 255.0,
test_labels,
args.output_path,
)
elif args.task == "predict_image":
success = True
classifier = Image_Classifier()
success = classifier.load(args.model_path)
if success:
if args.is_url:
image_filename = file.auxiliary("image", file.extension(args.data_path))
if not file.download(args.data_path, image_filename):
success = False
else:
image_filename = args.data_path
if success:
success, image = file.load_image(image_filename)
if success:
image = cv2.resize(
image, (classifier.params["window_size"], classifier.params["window_size"])
)
if not classifier.params["color"]:
image = np.mean(image, axis=2)
image = np.expand_dims(image, axis=0)
success, prediction = classifier.predict(
image / 255.0,
output_path=args.output_path,
)
if success:
index = np.argmax(prediction)
logger.info(
f"prediction: {classifier.class_names[index]} - {prediction[0][index]:.2f}"
)
elif args.task == "preprocess":
success = preprocess(
args.output_path,
objects=args.objects,
infer_annotation=args.infer_annotation,
purpose=args.purpose,
window_size=args.window_size,
)
elif args.task == "train":
success = Image_Classifier.train(
args.data_path,
args.model_path,
color=args.color,
convnet=args.convnet,
epochs=args.epochs,
)
else:
logger.error(f"-{name}: {args.task}: command not found.")
if not success:
logger.error(f"-{name}: {args.task}: failed.")
|