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8_demo.py
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8_demo.py
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import numpy as np
import sys,os
import cv2
caffe_root = '/workspace/dl/ssd/caffe/'
sys.path.insert(0, caffe_root + 'python')
import caffe
net_file= './tmp/deploy.prototxt'
caffe_model='/workspace/D2/sanjun/snapshot/mobilenetv2/_iter_60000.caffemodel'
#MobileNetSSD_deploy.caffemodel'
test_dir = "images"
if not os.path.exists(caffe_model):
print("MobileNetSSD_deploy.caffemodel does not exist,")
print("use merge_bn.py to generate it.")
exit()
net = caffe.Net(net_file,caffe_model,caffe.TEST)
CLASSES = ('background', 'person')
#CLASSES = ('background',
# 'aeroplane', 'bicycle', 'bird', 'boat',
# 'bottle', 'bus', 'car', 'cat', 'chair',
# 'cow', 'diningtable', 'dog', 'horse',
# 'motorbike', 'person', 'pottedplant',
# 'sheep', 'sofa', 'train', 'tvmonitor')
def preprocess(src):
img = cv2.resize(src, (300,300))
img = img - 127.5
img = img * 0.007843
return img
def postprocess(img, out):
h = img.shape[0]
w = img.shape[1]
box = out['detection_out'][0,0,:,3:7] * np.array([w, h, w, h])
cls = out['detection_out'][0,0,:,1]
conf = out['detection_out'][0,0,:,2]
return (box.astype(np.int32), conf, cls)
def detect(imgfile):
origimg = cv2.imread(imgfile)
image_name = os.path.basename(imgfile)
img = preprocess(origimg)
img = img.astype(np.float32)
img = img.transpose((2, 0, 1))
net.blobs['data'].data[...] = img
out = net.forward()
box, conf, cls = postprocess(origimg, out)
for i in range(len(box)):
p1 = (box[i][0], box[i][1])
p2 = (box[i][2], box[i][3])
cv2.rectangle(origimg, p1, p2, (0,255,0))
p3 = (max(p1[0], 15), max(p1[1], 15))
title = "%s:%.2f" % (CLASSES[int(cls[i])], conf[i])
cv2.putText(origimg, title, p3, cv2.FONT_ITALIC, 0.6, (0, 255, 0), 1)
#cv2.imshow("SSD", origimg)
cv2.imwrite(os.path.join("output_tmp", image_name), origimg)
k = cv2.waitKey(0) & 0xff
#Exit if ESC pressed
if k == 27 : return False
return True
for f in os.listdir(test_dir):
if detect(test_dir + "/" + f) == False:
break