遷移到 Python 用戶端程式庫 v0.25.1
透過集合功能整理內容你可以依據偏好儲存及分類內容。

相較於先前的程式庫，Python 用戶端程式庫 v0.25.1 在設計上有一些重大的變更，以下是摘要說明：

將模組合併成較少的類型
將不具類型的參數取代為強類型的類別與列舉

這個主題詳細說明您需要對 Cloud Vision API 用戶端程式庫的 Python 程式碼進行哪些變更，才能使用 v0.25.1 Python 用戶端程式庫。

執行舊版用戶端程式庫

您不需要將 Python 用戶端程式庫升級至 v0.25.1。如果您要繼續使用舊版 Python 用戶端程式庫，且不想遷移程式碼，請指定應用程式使用的 Python 用戶端程式庫版本。如要指定特定的程式庫版本，請編輯 requirements.txt 檔案，如以下所示：

google-cloud-vision==0.25

移除的模組

下列模組已從 Python 用戶端程式庫 v0.25.1 套件中移除。

google.cloud.vision.annotations
google.cloud.vision.batch
google.cloud.vision.client
google.cloud.vision.color
google.cloud.vision.crop_hint
google.cloud.vision.entity
google.cloud.vision.face
google.cloud.vision.feature
google.cloud.vision.geometry
google.cloud.vision.image
google.cloud.vision.likelihood
google.cloud.vision.safe_search
google.cloud.vision.text
google.cloud.vision.web

必要程式碼變更

匯入

包含新的 google.cloud.vision.types 模組，以存取 Python Client Library v0.25.1 中的新類型。

types 模組包含建立要求時所需的新類別，例如 types.Image。

from google.cloud import vision

此外，新的 google.cloud.vision.enums 模組包含適合用於剖析與理解 API 回應的列舉，例如 enums.Likelihood.UNLIKELY 和 enums.FaceAnnotation.Landmark.Type.LEFT_EYE。

建立用戶端

Client 類別已由 ImageAnnotatorClient 類別取代。將 Client 類別的參照替換為 ImageAnnotatorClient。

舊版用戶端程式庫：

old_client = vision.Client()

Python 用戶端程式庫 v0.25.1：

client = vision.ImageAnnotatorClient()

建構代表圖片內容的物件

如要識別本機檔案、Google Cloud Storage URI 或網頁 URI 中的圖片內容，請使用新的 Image 類別。

建構表示本機檔案中圖片內容的物件

以下範例顯示表示本機檔案中圖片內容的新方式。

舊版用戶端程式庫：

with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = old_client.image(content=content)

Python 用戶端程式庫 v0.25.1：

with open(path, "rb") as image_file:
    content = image_file.read()

image = vision.Image(content=content)

建構表示 URI 中圖片內容的物件

以下範例顯示表示 Google Cloud Storage URI 或網路 URI 中圖片內容的新方式。uri 是 Google Cloud Storage 或網路上圖片檔案的 URI。

舊版用戶端程式庫：

image = old_client.image(source_uri=uri)

Python 用戶端程式庫 v0.25.1：

image = vision.Image()
image.source.image_uri = uri

建立要求並處理回應

在 Python 用戶端程式庫 v.0.25.1 中，face_detection 等 API 方法屬於 ImageAnnotatorClient 物件，而非 Image 物件。

不同方法傳回的值不同，詳情請參閱下文。

特別是，邊界框架頂點現在會儲存在 bounding_poly.vertices 中，而非 bounds.vertices。每個頂點的座標都儲存在 vertex.x 和 vertex.y 中，而非 vertex.x_coordinate 和 vertex.y_coordinate。

邊界框變更會影響 face_detection、logo_detection、text_detection、document_text_detection 和 crop_hints。

提出臉部偵測要求並處理回應

情緒可能性現在會以列舉形式傳回，並儲存在 face.surprise_likelihood 中，而非 face.emotions.surprise。匯入 google.cloud.vision.enums.Likelihood 即可復原可能性標籤的名稱。

舊版用戶端程式庫：

with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = old_client.image(content=content)

faces = image.detect_faces()

for face in faces:
    print('anger: {}'.format(face.emotions.anger))
    print('joy: {}'.format(face.emotions.joy))
    print('surprise: {}'.format(face.emotions.surprise))

    vertices = (['({},{})'.format(bound.x_coordinate, bound.y_coordinate)
                for bound in face.bounds.vertices])

    print('face bounds: {}'.format(','.join(vertices)))

Python 用戶端程式庫 v0.25.1：

with open(path, "rb") as image_file:
    content = image_file.read()

image = vision.Image(content=content)

response = client.face_detection(image=image)
faces = response.face_annotations

# Names of likelihood from google.cloud.vision.enums
likelihood_name = (
    "UNKNOWN",
    "VERY_UNLIKELY",
    "UNLIKELY",
    "POSSIBLE",
    "LIKELY",
    "VERY_LIKELY",
)
print("Faces:")

for face in faces:
    print(f"anger: {likelihood_name[face.anger_likelihood]}")
    print(f"joy: {likelihood_name[face.joy_likelihood]}")
    print(f"surprise: {likelihood_name[face.surprise_likelihood]}")

    vertices = [
        f"({vertex.x},{vertex.y})" for vertex in face.bounding_poly.vertices
    ]

    print("face bounds: {}".format(",".join(vertices)))

if response.error.message:
    raise Exception(
        "{}\nFor more info on error messages, check: "
        "https://cloud.google.com/apis/design/errors".format(response.error.message)
    )

發出標籤偵測要求並處理回應

舊版用戶端程式庫：

with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = old_client.image(content=content)

labels = image.detect_labels()

for label in labels:
    print(label.description)

Python 用戶端程式庫 v0.25.1：

with open(path, "rb") as image_file:
    content = image_file.read()

image = vision.Image(content=content)

response = client.label_detection(image=image)
labels = response.label_annotations
print("Labels:")

for label in labels:
    print(label.description)

if response.error.message:
    raise Exception(
        "{}\nFor more info on error messages, check: "
        "https://cloud.google.com/apis/design/errors".format(response.error.message)
    )

發出地標偵測要求並處理回應

舊版用戶端程式庫：

地標位置的經緯度現在會儲存在 location.lat_lng.latitude 和 location.lat_lng.longitude 中，而非 location.latitude 和 location.longitude。

with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = old_client.image(content=content)

landmarks = image.detect_landmarks()

for landmark in landmarks:
    print(landmark.description, landmark.score)
    for location in landmark.locations:
        print('Latitude'.format(location.latitude))
        print('Longitude'.format(location.longitude))

Python 用戶端程式庫 v0.25.1：

with open(path, "rb") as image_file:
    content = image_file.read()

image = vision.Image(content=content)

response = client.landmark_detection(image=image)
landmarks = response.landmark_annotations
print("Landmarks:")

for landmark in landmarks:
    print(landmark.description)
    for location in landmark.locations:
        lat_lng = location.lat_lng
        print(f"Latitude {lat_lng.latitude}")
        print(f"Longitude {lat_lng.longitude}")

if response.error.message:
    raise Exception(
        "{}\nFor more info on error messages, check: "
        "https://cloud.google.com/apis/design/errors".format(response.error.message)
    )

提出標誌偵測要求並處理回應

舊版用戶端程式庫：

with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = old_client.image(content=content)

logos = image.detect_logos()

for logo in logos:
    print(logo.description, logo.score)

Python 用戶端程式庫 v0.25.1：

with open(path, "rb") as image_file:
    content = image_file.read()

image = vision.Image(content=content)

response = client.logo_detection(image=image)
logos = response.logo_annotations
print("Logos:")

for logo in logos:
    print(logo.description)

if response.error.message:
    raise Exception(
        "{}\nFor more info on error messages, check: "
        "https://cloud.google.com/apis/design/errors".format(response.error.message)
    )

提出 SafeSearch 偵測要求並處理回應

安全搜尋機率現在會以列舉方式傳回。匯入 google.cloud.vision.enums.Likelihood 即可復原可能性標籤的名稱。

舊版用戶端程式庫：

with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = old_client.image(content=content)

safe = image.detect_safe_search()
print('Safe search:')
print('adult: {}'.format(safe.adult))
print('medical: {}'.format(safe.medical))
print('spoofed: {}'.format(safe.spoof))
print('violence: {}'.format(safe.violence))

Python 用戶端程式庫 v0.25.1：

with open(path, "rb") as image_file:
    content = image_file.read()

image = vision.Image(content=content)

response = client.safe_search_detection(image=image)
safe = response.safe_search_annotation

# Names of likelihood from google.cloud.vision.enums
likelihood_name = (
    "UNKNOWN",
    "VERY_UNLIKELY",
    "UNLIKELY",
    "POSSIBLE",
    "LIKELY",
    "VERY_LIKELY",
)
print("Safe search:")

print(f"adult: {likelihood_name[safe.adult]}")
print(f"medical: {likelihood_name[safe.medical]}")
print(f"spoofed: {likelihood_name[safe.spoof]}")
print(f"violence: {likelihood_name[safe.violence]}")
print(f"racy: {likelihood_name[safe.racy]}")

if response.error.message:
    raise Exception(
        "{}\nFor more info on error messages, check: "
        "https://cloud.google.com/apis/design/errors".format(response.error.message)
    )

提出文字偵測要求並處理回應

舊版用戶端程式庫：

with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = old_client.image(content=content)

texts = image.detect_text()

for text in texts:
    print('\n"{}"'.format(text.description))

    vertices = (['({},{})'.format(bound.x_coordinate, bound.y_coordinate)
                for bound in text.bounds.vertices])

    print('bounds: {}'.format(','.join(vertices)))

Python 用戶端程式庫 v0.25.1：

with open(path, "rb") as image_file:
    content = image_file.read()

image = vision.Image(content=content)

response = client.text_detection(image=image)
texts = response.text_annotations
print("Texts:")

for text in texts:
    print(f'\n"{text.description}"')

    vertices = [
        f"({vertex.x},{vertex.y})" for vertex in text.bounding_poly.vertices
    ]

    print("bounds: {}".format(",".join(vertices)))

if response.error.message:
    raise Exception(
        "{}\nFor more info on error messages, check: "
        "https://cloud.google.com/apis/design/errors".format(response.error.message)
    )

提出文件文字偵測要求並處理回應

舊版用戶端程式庫：

with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = old_client.image(content=content)

document = image.detect_full_text()

for page in document.pages:
    for block in page.blocks:
        block_words = []
        for paragraph in block.paragraphs:
            block_words.extend(paragraph.words)

        block_symbols = []
        for word in block_words:
            block_symbols.extend(word.symbols)

        block_text = ''
        for symbol in block_symbols:
            block_text = block_text + symbol.text

        print('Block Content: {}'.format(block_text))
        print('Block Bounds:\n {}'.format(block.bounding_box))

Python 用戶端程式庫 v0.25.1：

with open(path, "rb") as image_file:
    content = image_file.read()

image = vision.Image(content=content)

response = client.document_text_detection(image=image)

for page in response.full_text_annotation.pages:
    for block in page.blocks:
        print(f"\nBlock confidence: {block.confidence}\n")

        for paragraph in block.paragraphs:
            print("Paragraph confidence: {}".format(paragraph.confidence))

            for word in paragraph.words:
                word_text = "".join([symbol.text for symbol in word.symbols])
                print(
                    "Word text: {} (confidence: {})".format(
                        word_text, word.confidence
                    )
                )

                for symbol in word.symbols:
                    print(
                        "\tSymbol: {} (confidence: {})".format(
                            symbol.text, symbol.confidence
                        )
                    )

if response.error.message:
    raise Exception(
        "{}\nFor more info on error messages, check: "
        "https://cloud.google.com/apis/design/errors".format(response.error.message)
    )

提出圖片屬性要求並處理回應

主色資訊現在會儲存在 props.dominant_colors.colors 中，而非 props.colors。

舊版用戶端程式庫：

with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = old_client.image(content=content)

props = image.detect_properties()

for color in props.colors:
    print('fraction: {}'.format(color.pixel_fraction))
    print('\tr: {}'.format(color.color.red))
    print('\tg: {}'.format(color.color.green))
    print('\tb: {}'.format(color.color.blue))
    print('\ta: {}'.format(color.color.alpha))

Python 用戶端程式庫 v0.25.1：

with open(path, "rb") as image_file:
    content = image_file.read()

image = vision.Image(content=content)

response = client.image_properties(image=image)
props = response.image_properties_annotation
print("Properties:")

for color in props.dominant_colors.colors:
    print(f"fraction: {color.pixel_fraction}")
    print(f"\tr: {color.color.red}")
    print(f"\tg: {color.color.green}")
    print(f"\tb: {color.color.blue}")
    print(f"\ta: {color.color.alpha}")

if response.error.message:
    raise Exception(
        "{}\nFor more info on error messages, check: "
        "https://cloud.google.com/apis/design/errors".format(response.error.message)
    )

提出網路偵測要求並處理回應

舊版用戶端程式庫：

with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = old_client.image(content=content)

notes = image.detect_web()

if notes.pages_with_matching_images:
    print('\n{} Pages with matching images retrieved')

    for page in notes.pages_with_matching_images:
        print('Score : {}'.format(page.score))
        print('Url   : {}'.format(page.url))

if notes.full_matching_images:
    print ('\n{} Full Matches found: '.format(
           len(notes.full_matching_images)))

    for image in notes.full_matching_images:
        print('Score:  {}'.format(image.score))
        print('Url  : {}'.format(image.url))

if notes.partial_matching_images:
    print ('\n{} Partial Matches found: '.format(
           len(notes.partial_matching_images)))

    for image in notes.partial_matching_images:
        print('Score: {}'.format(image.score))
        print('Url  : {}'.format(image.url))

if notes.web_entities:
    print ('\n{} Web entities found: '.format(len(notes.web_entities)))

    for entity in notes.web_entities:
        print('Score      : {}'.format(entity.score))
        print('Description: {}'.format(entity.description))

Python 用戶端程式庫 v0.25.1：

with open(path, "rb") as image_file:
    content = image_file.read()

image = vision.Image(content=content)

response = client.web_detection(image=image)
annotations = response.web_detection

if annotations.best_guess_labels:
    for label in annotations.best_guess_labels:
        print(f"\nBest guess label: {label.label}")

if annotations.pages_with_matching_images:
    print(
        "\n{} Pages with matching images found:".format(
            len(annotations.pages_with_matching_images)
        )
    )

    for page in annotations.pages_with_matching_images:
        print(f"\n\tPage url   : {page.url}")

        if page.full_matching_images:
            print(
                "\t{} Full Matches found: ".format(len(page.full_matching_images))
            )

            for image in page.full_matching_images:
                print(f"\t\tImage url  : {image.url}")

        if page.partial_matching_images:
            print(
                "\t{} Partial Matches found: ".format(
                    len(page.partial_matching_images)
                )
            )

            for image in page.partial_matching_images:
                print(f"\t\tImage url  : {image.url}")

if annotations.web_entities:
    print("\n{} Web entities found: ".format(len(annotations.web_entities)))

    for entity in annotations.web_entities:
        print(f"\n\tScore      : {entity.score}")
        print(f"\tDescription: {entity.description}")

if annotations.visually_similar_images:
    print(
        "\n{} visually similar images found:\n".format(
            len(annotations.visually_similar_images)
        )
    )

    for image in annotations.visually_similar_images:
        print(f"\tImage url    : {image.url}")

if response.error.message:
    raise Exception(
        "{}\nFor more info on error messages, check: "
        "https://cloud.google.com/apis/design/errors".format(response.error.message)
    )

提出裁剪提示要求並處理回應

舊版用戶端程式庫：

with io.open(file_name, 'rb') as image_file:
    content = image_file.read()

image = old_client.image(content=content)

hints = image.detect_crop_hints(aspect_ratios=[1.77])

for n, hint in enumerate(hints):
    print('\nCrop Hint: {}'.format(n))

    vertices = (['({},{})'.format(bound.x_coordinate, bound.y_coordinate)
                for bound in hint.bounds.vertices])

    print('bounds: {}'.format(','.join(vertices)))

Python 用戶端程式庫 v0.25.1：

with open(path, "rb") as image_file:
    content = image_file.read()
image = vision.Image(content=content)

crop_hints_params = vision.CropHintsParams(aspect_ratios=[1.77])
image_context = vision.ImageContext(crop_hints_params=crop_hints_params)

response = client.crop_hints(image=image, image_context=image_context)
hints = response.crop_hints_annotation.crop_hints

for n, hint in enumerate(hints):
    print(f"\nCrop Hint: {n}")

    vertices = [
        f"({vertex.x},{vertex.y})" for vertex in hint.bounding_poly.vertices
    ]

    print("bounds: {}".format(",".join(vertices)))

if response.error.message:
    raise Exception(
        "{}\nFor more info on error messages, check: "
        "https://cloud.google.com/apis/design/errors".format(response.error.message)
    )

請注意，顯示比例必須透過 CropHintsParams 和 ImageContext 傳入。

遷移到 Python 用戶端程式庫 v0.25.1透過集合功能整理內容 你可以依據偏好儲存及分類內容。

執行舊版用戶端程式庫

移除的模組

必要程式碼變更

匯入

建立用戶端

建構代表圖片內容的物件

建構表示本機檔案中圖片內容的物件

建構表示 URI 中圖片內容的物件

建立要求並處理回應

提出臉部偵測要求並處理回應

發出標籤偵測要求並處理回應

發出地標偵測要求並處理回應

提出標誌偵測要求並處理回應

提出 SafeSearch 偵測要求並處理回應

提出文字偵測要求並處理回應

提出文件文字偵測要求並處理回應

提出圖片屬性要求並處理回應

提出網路偵測要求並處理回應

提出裁剪提示要求並處理回應

遷移到 Python 用戶端程式庫 v0.25.1
透過集合功能整理內容你可以依據偏好儲存及分類內容。