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run_ssd_example.py
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run_ssd_example.py
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from vision.ssd.vgg_ssd import create_vgg_ssd, create_vgg_ssd_predictor
from vision.ssd.mobilenetv1_ssd import create_mobilenetv1_ssd, create_mobilenetv1_ssd_predictor
from vision.ssd.mobilenetv1_ssd_lite import create_mobilenetv1_ssd_lite, create_mobilenetv1_ssd_lite_predictor
from vision.ssd.squeezenet_ssd_lite import create_squeezenet_ssd_lite, create_squeezenet_ssd_lite_predictor
from vision.ssd.mobilenet_v2_ssd_lite import create_mobilenetv2_ssd_lite, create_mobilenetv2_ssd_lite_predictor
from vision.utils.misc import Timer
import cv2
import sys
from vision.ssd.mobilenet_v3_ssd_lite import create_mobilenetv3_ssd_lite,create_mobilenetv3_ssd_lite_predictor
if len(sys.argv) < 5:
print('Usage: python run_ssd_example.py <net type> <model path> <label path> <image path>')
sys.exit(0)
net_type = sys.argv[1]
model_path = sys.argv[2]
label_path = sys.argv[3]
image_path = sys.argv[4]
class_names = [name.strip() for name in open(label_path).readlines()]
if net_type == 'vgg16-ssd':
net = create_vgg_ssd(len(class_names), is_test=True)
elif net_type == 'mb1-ssd':
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
elif net_type == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(len(class_names), is_test=True)
elif net_type == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(len(class_names), is_test=True)
elif net_type == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(len(class_names), is_test=True)
elif net_type == 'mb3-ssd-lite':
net = create_mobilenetv3_ssd_lite(len(class_names), is_test=True)
else:
print("The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite.")
sys.exit(1)
net.load(model_path)
if net_type == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(net, candidate_size=200)
elif net_type == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(net, candidate_size=200)
elif net_type == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(net, candidate_size=200)
elif net_type == 'mb3-ssd-lite':
predictor = create_mobilenetv3_ssd_lite_predictor(net, candidate_size=200)
else:
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
orig_image = cv2.imread(image_path)
image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)
boxes, labels, probs = predictor.predict(image, 10, 0.4)
for i in range(boxes.size(0)):
box = boxes[i, :]
cv2.rectangle(orig_image, (box[0], box[1]), (box[2], box[3]), (255, 255, 0), 4)
#label = f"""{voc_dataset.class_names[labels[i]]}: {probs[i]:.2f}"""
label = f"{class_names[labels[i]]}: {probs[i]:.2f}"
cv2.putText(orig_image, label,
(box[0] + 20, box[1] + 40),
cv2.FONT_HERSHEY_SIMPLEX,
1, # font scale
(255, 0, 255),
2) # line type
path = "run_ssd_example_output.jpg"
cv2.imwrite(path, orig_image)
print(f"Found {len(probs)} objects. The output image is {path}")