import IPython
\nfrom google.colab import output
\nimport cv2
\nimport numpy as np
\nfrom PIL import Image
\nfrom io import BytesIO
\nimport base64<\/p>\n
def run(img_str):
\n #decimg to image
\n decimg = base64.b64decode(img_str.split(‘,’)[1], validata=True)
\n decimg = Image.open(BytesIO(decimg))
\n decimg = np.array(decimg, dtype=np.uint8);
\n decimg = cv2.cvtColor(decimg, cv2.COLOR_BGR2RGB)<\/p>\n
# image classify
\n img = cv2.resize(decimg, (64, 64))
\n img = img_to_array(img)
\n img = img \/ 255.0
\n img = img[np.newaxis]
\n img = np.asarray(img)
\n preds = keras_model.predict(img)
\n result = np.argmax(preds[0])<\/p>\n
# draw output
\n out_img = decimg
\n cv2.putText(out_img, labels[result], (10,50),
\n cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 1, cv2.LINE_AA)
\n #encode to string
\n _, encimg = cv2.imencode(“.jpg”, out_img,
\n [int(cv2.IMWRITE_JPEG_QUALITY), 80])
\n img_str = encimg.tostring()
\n img_str = (“data:imge\/jpeg:base64,” +
\n base64.b64encode(img_str).decode(‘utf-8’))
\n return IPython.display.JSON({‘img_str’: img_str})<\/p>\n
output.register_callback(‘notebook.run’,run)<\/p>\n
https:\/\/colab.research.google.com\/drive\/1cF–C6PRT2j7hPdn5ia1I7SlwGqdvB-d?hl=ja#scrollTo=eT8PXuywDsmO<\/p>\n","protected":false},"excerpt":{"rendered":"