是否可以使用 google 视觉 api 一次扫描 10 张图像?到目前为止只做 1
is it possible to scan 10 images at once ocr using google vision api? so far manage to do only 1
我们目前正在使用 google 视觉 API 做一个 ocr 项目,其中图像 return 文本值...但到目前为止我们只设法做 1 张图像,是可以做10张图片吗?我正在使用 python 并且此代码仅运行一张图像..谢谢
import os, io
from google.cloud import vision
from google.cloud.vision import types
import pandas as pd
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = r'anjir.json'
client = vision.ImageAnnotatorClient()
FILE_NAME = 'receipttest2.jpg'
FOLDER_PATH = r'C:\Users\Fadhlan\Desktop\Python venv\image\text'
with io.open(os.path.join(FOLDER_PATH, FILE_NAME), 'rb') as image_file:
content = image_file.read()
image = vision.types.Image(content=content)
response = client.text_detection(image=image)
texts = response.text_annotations
df = pd.DataFrame(columns=['locale', 'description'])
for text in texts:
df = df.append(
dict(
locale=text.locale,
description=text.description
),
ignore_index=True
)
print(df['description'][0])
可以使用 batch image annotation offline since the "TEXT_DETECTION" feature is supported in the asynchronous mode. You can find a sample code for Python in here,如您所见,需要为每个图像创建一个请求元素并将其添加到请求数组中:
client = vision_v1.ImageAnnotatorClient()
//image one
source1 = {"image_uri": image_uri_1}
image1 = {"source": source1}
features1 = [
{"type": enums.Feature.Type.LABEL_DETECTION},
{"type": enums.Feature.Type.IMAGE_PROPERTIES}
]
//image two
source2 = {"image_uri": image_uri_2}
image2 = {"source": source2}
features2 = [
{"type": enums.Feature.Type.LABEL_DETECTION}
]
# Each requests element corresponds to a single image
requests = [{"image": image1, "features": features1}, {"image": image2, "features": features2}]
gcs_destination = {"uri": output_uri}
# The max number of responses to output in each JSON file
batch_size = 2
output_config = {"gcs_destination": gcs_destination,
"batch_size": batch_size}
operation = client.async_batch_annotate_images(requests, output_config)
我们目前正在使用 google 视觉 API 做一个 ocr 项目,其中图像 return 文本值...但到目前为止我们只设法做 1 张图像,是可以做10张图片吗?我正在使用 python 并且此代码仅运行一张图像..谢谢
import os, io
from google.cloud import vision
from google.cloud.vision import types
import pandas as pd
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = r'anjir.json'
client = vision.ImageAnnotatorClient()
FILE_NAME = 'receipttest2.jpg'
FOLDER_PATH = r'C:\Users\Fadhlan\Desktop\Python venv\image\text'
with io.open(os.path.join(FOLDER_PATH, FILE_NAME), 'rb') as image_file:
content = image_file.read()
image = vision.types.Image(content=content)
response = client.text_detection(image=image)
texts = response.text_annotations
df = pd.DataFrame(columns=['locale', 'description'])
for text in texts:
df = df.append(
dict(
locale=text.locale,
description=text.description
),
ignore_index=True
)
print(df['description'][0])
可以使用 batch image annotation offline since the "TEXT_DETECTION" feature is supported in the asynchronous mode. You can find a sample code for Python in here,如您所见,需要为每个图像创建一个请求元素并将其添加到请求数组中:
client = vision_v1.ImageAnnotatorClient()
//image one
source1 = {"image_uri": image_uri_1}
image1 = {"source": source1}
features1 = [
{"type": enums.Feature.Type.LABEL_DETECTION},
{"type": enums.Feature.Type.IMAGE_PROPERTIES}
]
//image two
source2 = {"image_uri": image_uri_2}
image2 = {"source": source2}
features2 = [
{"type": enums.Feature.Type.LABEL_DETECTION}
]
# Each requests element corresponds to a single image
requests = [{"image": image1, "features": features1}, {"image": image2, "features": features2}]
gcs_destination = {"uri": output_uri}
# The max number of responses to output in each JSON file
batch_size = 2
output_config = {"gcs_destination": gcs_destination,
"batch_size": batch_size}
operation = client.async_batch_annotate_images(requests, output_config)