#-------------------------------------#
# 对单张图片进行预测
#-------------------------------------#
from efficientdet import EfficientDet
from PIL import Image
efficientdet = EfficientDet()
while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
r_image = efficientdet.detect_image(image)
r_image.show()
tf.config.experimental.set_memory_growth(gpu, True)
efficientdet = EfficientDet()
capture = cv2.VideoCapture(0) # capture=cv2.VideoCapture("1.mp4")
fps = 0.0
while (True):
t1 = time.time()
# 读取某一帧
ref, frame = capture.read()
# 格式转变，BGRtoRGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 转变成Image
frame = Image.fromarray(np.uint8(frame))
# 进行检测
frame = np.array(efficientdet.detect_image(frame))
# RGBtoBGR满足opencv显示格式
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
fps = (fps + (1. / (time.time() - t1))) / 2
print("fps= %.2f" % (fps))
frame = cv2.putText(frame, "fps= %.2f" % (fps), (0, 40),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.imshow("video", frame)
c = cv2.waitKey(1) & 0xff
if c == 27:
capture.release()
break
help='num img 2 show',
default=1)
parser.add_argument('-r',
"--root",
type=str,
help='root dir filled with *.jpg')
parser.add_argument('-i', "--filename", type=str, help='filename', default='')
args = parser.parse_args()
efficientdet = EfficientDet(args.model_path, args.version, args.conf,
args.cuda)
if args.num2show == 1:
image = Image.open(os.path.join(args.root, args.filename))
res, cls, score = efficientdet.detect_image(image)
print(cls, score)
# r_image.show()
else:
print('结果将会保存到temp.png')
files = os.listdir(args.root)
idx = [
int(len(os.listdir(args.root)) * random.random())
for i in range(args.num2show)
]
imgs = [Image.open(os.path.join(args.root, files[id])) for id in idx]
ress, clss, scores = [], [], []
print(len(imgs))
for img in imgs:
res, cls, score = efficientdet.detect_image(img)