def detect_img(indir,outdir):
ssd = SSD()
#遍历该目录下的所有图片文件
for filename in glob.glob(indir):
print("Start, the detect image is:",filename)
img = Image.open(filename)
img = ssd.detect_image(img)
#img.show() # 显示图片
img.save(os.path.join(outdir,os.path.basename(filename)))
print("End, the detection of this image")
print('---------------------------------')
#-------------------------------------#
from keras.layers import Input
from ssd import SSD
from PIL import Image
import numpy as np
import cv2
ssd = SSD()
# 调用摄像头
capture = cv2.VideoCapture(0) # capture=cv2.VideoCapture("1.mp4")
while (True):
# 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(ssd.detect_image(frame))
# RGBtoBGR满足opencv显示格式
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
cv2.imshow("video", frame)
c = cv2.waitKey(30) & 0xff
if c == 27:
capture.release()
break
yolo.close_session()
1、如果想要进行检测完的图片的保存，利用r_image.save("img.jpg")即可保存，直接在predict.py里进行修改即可。
2、如果想要获得预测框的坐标，可以进入ssd.detect_image函数，在绘图部分读取top，left，bottom，right这四个值。
3、如果想要利用预测框截取下目标，可以进入ssd.detect_image函数，在绘图部分利用获取到的top，left，bottom，right这四个值
在原图上利用矩阵的方式进行截取。
4、如果想要在预测图上写额外的字，比如检测到的特定目标的数量，可以进入ssd.detect_image函数，在绘图部分对predicted_class进行判断，
比如判断if predicted_class == 'car': 即可判断当前目标是否为车，然后记录数量即可。利用draw.text即可写字。
'''
while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
r_image = ssd.detect_image(image, crop = crop, count=count)
r_image.show()
elif mode == "video":
capture = cv2.VideoCapture(video_path)
if video_save_path!="":
fourcc = cv2.VideoWriter_fourcc(*'XVID')
size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
out = cv2.VideoWriter(video_save_path, fourcc, video_fps, size)
ref, frame = capture.read()
if not ref:
raise ValueError("未能正确读取摄像头（视频），请注意是否正确安装摄像头（是否正确填写视频路径）。")
fps = 0.0
while(True):
from keras.layers import Input
from ssd import SSD
from PIL import Image
ssd = SSD()
while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
r_image = ssd.detect_image(image)
r_image.show()
2、如果想要进行检测完的图片的保存，利用r_image.save("img.jpg")即可保 存，直接在predict.py里进行修改即可。
3、如果想要获得预测框的坐标，可以进入ssd.detect_image函数，在绘图部分读取top，left，bottom，right这四个值。
4、如果想要利用预测框截取下目标，可以进入ssd.detect_image函数，在绘图部分利用获取到的top，left，bottom，right这四个值
在原图上利用矩阵的方式进行截取。
5、如果想要在预测图上写额外的字，比如检测到的特定目标的数量，可以进入ssd.detect_image函数，在绘图部分对predicted_class进行判断，
比如判断if predicted_class == 'car': 即可判断当前目标是否为车，然后记录数量即可。利用draw.text即可写字。
'''
while True:
img = input('Input image filename:')
try:
image = Image.open(img)
except:
print('Open Error! Try again!')
continue
else:
r_image = ssd.detect_image(image)
r_image.show()
elif mode == "video":
capture = cv2.VideoCapture(video_path)
if video_save_path != "":
fourcc = cv2.VideoWriter_fourcc(*'XVID')
size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
out = cv2.VideoWriter(video_save_path, fourcc, video_fps, size)
fps = 0.0
while (True):
t1 = time.time()
# 读取某一帧
ref, frame = capture.read()
help='root dir filled with *.jpg',
default='VOCdevkit/VOC2007/JPEGImages')
parser.add_argument('-i', "--filename", type=str, help='filename', default='')
args = parser.parse_args()
model = SSD(args.model_path, 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 = model.detect_image(img)
ress.append(res)
clss.append(cls)
scores.append(score)
show_imgs(ress, clss, scores)
