app2.py 文件源码

def predict(url, mod, synsets):
     req = urllib2.urlopen(url)
     arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
     cv2_img = cv2.imdecode(arr, -1)
     img = cv2.cvtColor(cv2_img, cv2.COLOR_BGR2RGB)
     if img is None:
         return None
     img = cv2.resize(img, (224, 224))
     img = np.swapaxes(img, 0, 2)
     img = np.swapaxes(img, 1, 2)
     img = img[np.newaxis, :]

     mod.forward(Batch([mx.nd.array(img)]))
     prob = mod.get_outputs()[0].asnumpy()
     prob = np.squeeze(prob)

     a = np.argsort(prob)[::-1]
     out = ''
     for i in a[0:5]:
         out += 'probability=%f, class=%s' %(prob[i], synsets[i])
     out += "\n"
     return out