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demo.py
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demo.py
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import torch
import cv2
from PIL import Image
import matplotlib.pyplot as plt
from models import EfficientDet
from torchvision import transforms
import numpy as np
import skimage
from datasets import get_augumentation, VOC_CLASSES
from timeit import default_timer as timer
import argparse
import copy
from utils import vis_bbox, EFFICIENTDET
parser = argparse.ArgumentParser(description='EfficientDet')
parser.add_argument('-n', '--network', default='efficientdet-d0',
help='efficientdet-[d0, d1, ..]')
parser.add_argument('-s', '--score', default=True,
action="store_true", help='Show score')
parser.add_argument('-t', '--threshold', default=0.6,
type=float, help='Visualization threshold')
parser.add_argument('-it', '--iou_threshold', default=0.6,
type=float, help='Visualization threshold')
parser.add_argument('-w', '--weight', default='./weights/voc0712.pth',
type=str, help='Weight model path')
parser.add_argument('-c', '--cam',
action="store_true", help='Use camera')
parser.add_argument('-f', '--file_name', default='pic.jpg',
help='Image path')
parser.add_argument('--num_class', default=21, type=int,
help='Number of class used in model')
args = parser.parse_args()
class Detect(object):
"""
dir_name: Folder or image_file
"""
def __init__(self, weights, num_class=21, network='efficientdet-d0', size_image=(512, 512)):
super(Detect, self).__init__()
self.weights = weights
self.size_image = size_image
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else 'cpu')
self.transform = get_augumentation(phase='test')
if(self.weights is not None):
print('Load pretrained Model')
checkpoint = torch.load(
self.weights, map_location=lambda storage, loc: storage)
params = checkpoint['parser']
num_class = params.num_class
network = params.network
self.model = EfficientDet(num_classes=num_class,
network=network,
W_bifpn=EFFICIENTDET[network]['W_bifpn'],
D_bifpn=EFFICIENTDET[network]['D_bifpn'],
D_class=EFFICIENTDET[network]['D_class'],
is_training=False
)
if(self.weights is not None):
state_dict = checkpoint['state_dict']
self.model.load_state_dict(state_dict)
if torch.cuda.is_available():
self.model = self.model.cuda()
self.model.eval()
def process(self, file_name=None, img=None, show=False):
if file_name is not None:
img = cv2.imread(file_name)
origin_img = copy.deepcopy(img)
augmentation = self.transform(image=img)
img = augmentation['image']
img = img.to(self.device)
img = img.unsqueeze(0)
with torch.no_grad():
scores, classification, transformed_anchors = self.model(img)
bboxes = list()
labels = list()
bbox_scores = list()
colors = list()
for j in range(scores.shape[0]):
bbox = transformed_anchors[[j], :][0].data.cpu().numpy()
x1 = int(bbox[0]*origin_img.shape[1]/self.size_image[1])
y1 = int(bbox[1]*origin_img.shape[0]/self.size_image[0])
x2 = int(bbox[2]*origin_img.shape[1]/self.size_image[1])
y2 = int(bbox[3]*origin_img.shape[0]/self.size_image[0])
bboxes.append([x1, y1, x2, y2])
label_name = VOC_CLASSES[int(classification[[j]])]
labels.append(label_name)
if(args.cam):
cv2.rectangle(origin_img, (x1, y1),
(x2, y2), (179, 255, 179), 2, 1)
if args.score:
score = np.around(
scores[[j]].cpu().numpy(), decimals=2) * 100
if(args.cam):
labelSize, baseLine = cv2.getTextSize('{} {}'.format(
label_name, int(score)), cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)
cv2.rectangle(
origin_img, (x1, y1-labelSize[1]), (x1+labelSize[0], y1+baseLine), (223, 128, 255), cv2.FILLED)
cv2.putText(
origin_img, '{} {}'.format(label_name, int(score)),
(x1, y1), cv2.FONT_HERSHEY_SIMPLEX,
0.8, (0, 0, 0), 2
)
bbox_scores.append(int(score))
else:
if(args.cam):
labelSize, baseLine = cv2.getTextSize('{}'.format(
label_name), cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)
cv2.rectangle(
origin_img, (x1, y1-labelSize[1]), (x1+labelSize[0], y1+baseLine), (0, 102, 255), cv2.FILLED)
cv2.putText(
origin_img, '{} {}'.format(label_name, int(score)),
(x1, y1), cv2.FONT_HERSHEY_SIMPLEX,
0.8, (0, 0, 0), 2
)
if show:
fig, ax = vis_bbox(img=origin_img, bbox=bboxes,
label=labels, score=bbox_scores)
fig.savefig('./docs/demo.png')
plt.show()
else:
return origin_img
def camera(self):
cap = cv2.VideoCapture(0)
if not cap.isOpened():
print("Unable to open camera")
exit(-1)
count_tfps = 1
accum_time = 0
curr_fps = 0
fps = "FPS: ??"
prev_time = timer()
while True:
res, img = cap.read()
curr_time = timer()
exec_time = curr_time - prev_time
prev_time = curr_time
accum_time = accum_time + exec_time
curr_fps = curr_fps + 1
if accum_time > 1:
accum_time = accum_time - 1
fps = curr_fps
curr_fps = 0
if res:
show_image = self.process(img=img)
cv2.putText(
show_image, "FPS: " + str(fps), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (204, 51, 51), 2
)
cv2.imshow("Detection", show_image)
k = cv2.waitKey(1)
if k == 27:
break
else:
print("Unable to read image")
exit(-1)
count_tfps += 1
cap.release()
cv2.destroyAllWindows()
if __name__ == '__main__':
detect = Detect(weights=args.weight)
print('cam: ', args.cam)
if args.cam:
detect.camera()
else:
detect.process(file_name=args.file_name, show=True)