画像の理解

Gemini モデルは、マルチモーダル AI として一から構築されています。そのため、画像キャプション、分類、視覚的な質問応答など、さまざまな画像処理タスクやコンピュータ ビジョン タスクを、専用の ML モデルをトレーニングすることなく実行できます。

Gemini モデルは、一般的なマルチモーダル機能に加えて、追加のトレーニングにより、オブジェクト検出やセグメンテーションなどの特定のユースケースで精度が向上します。

Gemini に画像を渡す

Gemini に画像を渡すには、次の複数の方法があります。

URL を使用して画像を渡す

Files API を使用して画像をアップロードし、リクエストで渡すことができます。

Python

from google import genai

client = genai.Client()

uploaded_file = client.files.upload(file="path/to/organ.jpg")

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "text", "text": "Caption this image."},
        {
            "type": "image",
            "uri": uploaded_file.uri,
            "mime_type": uploaded_file.mime_type
        }
    ]
)
print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const uploadedFile = await client.files.upload({
    file: "path/to/organ.jpg",
    config: { mimeType: "image/jpeg" }
});

const interaction = await client.interactions.create({
    model: "gemini-3.8-flash",
    input: [
        {type: "text", text: "Caption this image."},
        {
            type: "image",
            uri: uploadedFile.uri,
            mime_type: uploadedFile.mimeType
        }
    ]
});
console.log(interaction.output_text);

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.ImageContent;
import com.google.genai.gaos.models.interactions.ImageContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.types.File;
import com.google.genai.types.UploadFileConfig;
import java.util.Arrays;
import java.util.List;

Client client = new Client();

File uploadedFile =
    client.files.upload(
        new java.io.File("path/to/organ.jpg"),
        UploadFileConfig.builder().mimeType("image/jpeg").build());

Content textContent = TextContent.builder().text("Caption this image.").build();
Content imageContent =
    ImageContent.builder()
        .uri(uploadedFile.uri().orElse(""))
        .mimeType(ImageContentMimeType.of(uploadedFile.mimeType().orElse("image/jpeg")))
        .build();

List<Content> contents = Arrays.asList(textContent, imageContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();
System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    uploadedFile, err := client.Files.UploadFromPath(ctx, "path/to/organ.jpg", &genai.UploadFileConfig{
        MIMEType: "image/jpeg",
    })
    if err != nil {
        log.Fatal(err)
    }

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput([]interactions.Content{
                interactions.NewContent(interactions.TextContent{
                    Text: "Caption this image.",
                }),
                interactions.NewContent(interactions.ImageContent{
                    URI:      genai.Ptr(uploadedFile.URI),
                    MimeType: interactions.ImageContentMimeType(uploadedFile.MIMEType).ToPointer(),
                }),
            }),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

REST

# First upload the file using the Files API, then use the URI:
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.8-flash",
    "input": [
      {"type": "text", "text": "Caption this image."},
      {
        "type": "image",
        "uri": "YOUR_FILE_URI",
        "mime_type": "image/jpeg"
      }
    ]
  }'

インライン画像データを渡す

画像データは Base64 エンコード文字列として指定できます。

Python

import base64
from google import genai

with open('path/to/small-sample.jpg', 'rb') as f:
    image_bytes = f.read()

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "text", "text": "Caption this image."},
        {
            "type": "image",
            "data": base64.b64encode(image_bytes).decode('utf-8'),
            "mime_type": "image/jpeg"
        }
    ]
)
print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";
import * as fs from "node:fs";

const client = new GoogleGenAI({});
const base64ImageFile = fs.readFileSync("path/to/small-sample.jpg", {
  encoding: "base64",
});

const interaction = await client.interactions.create({
    model: "gemini-3.8-flash",
    input: [
        {type: "text", text: "Caption this image."},
        {
            type: "image",
            data: base64ImageFile,
            mime_type: "image/jpeg"
        }
    ]
});
console.log(interaction.output_text);

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.ImageContent;
import com.google.genai.gaos.models.interactions.ImageContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.Arrays;
import java.util.Base64;
import java.util.List;

byte[] imageBytes = Files.readAllBytes(Paths.get("path/to/small-sample.jpg"));
String base64Image = Base64.getEncoder().encodeToString(imageBytes);

Client client = new Client();

Content textContent = TextContent.builder().text("Caption this image.").build();
Content imageContent =
    ImageContent.builder()
        .data(base64Image)
        .mimeType(ImageContentMimeType.IMAGE_JPEG)
        .build();

List<Content> contents = Arrays.asList(textContent, imageContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();
System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "encoding/base64"
    "fmt"
    "log"
    "os"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    imageBytes, err := os.ReadFile("path/to/small-sample.jpg")
    if err != nil {
        log.Fatal(err)
    }
    base64Image := base64.StdEncoding.EncodeToString(imageBytes)

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput([]interactions.Content{
                interactions.NewContent(interactions.TextContent{
                    Text: "Caption this image.",
                }),
                interactions.NewContent(interactions.ImageContent{
                    Data:     genai.Ptr(base64Image),
                    MimeType: interactions.ImageContentMimeTypeImageJpeg.ToPointer(),
                }),
            }),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

REST

IMG_PATH="/path/to/your/image1.jpg"

if [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
  B64FLAGS="--input"
else
  B64FLAGS="-w0"
fi

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.8-flash",
    "input": [
      {"type": "text", "text": "Caption this image."},
      {
        "type": "image",
        "data": "'"$(base64 $B64FLAGS $IMG_PATH)"'",
        "mime_type": "image/jpeg"
      }
    ]
  }'

File API を使用して画像をアップロードする

大きなファイルの場合や、同じ画像ファイルを繰り返し使用できるようにするには、Files API を使用します。Files API ガイドをご覧ください。

Python

from google import genai

client = genai.Client()

my_file = client.files.upload(file="path/to/sample.jpg")

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "text", "text": "Caption this image."},
        {
            "type": "image",
            "uri": my_file.uri,
            "mime_type": my_file.mime_type
        }
    ]
)
print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const myfile = await client.files.upload({
    file: "path/to/sample.jpg",
    config: { mimeType: "image/jpeg" },
});

const interaction = await client.interactions.create({
    model: "gemini-3.8-flash",
    input: [
        {type: "text", text: "Caption this image."},
        {
            type: "image",
            uri: myfile.uri,
            mime_type: myfile.mimeType
        }
    ]
});
console.log(interaction.output_text);

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.ImageContent;
import com.google.genai.gaos.models.interactions.ImageContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.types.File;
import com.google.genai.types.UploadFileConfig;
import java.util.Arrays;
import java.util.List;

Client client = new Client();

File myFile =
    client.files.upload(
        new java.io.File("path/to/sample.jpg"),
        UploadFileConfig.builder().mimeType("image/jpeg").build());

Content textContent = TextContent.builder().text("Caption this image.").build();
Content imageContent =
    ImageContent.builder()
        .uri(myFile.uri().orElse(""))
        .mimeType(ImageContentMimeType.of(myFile.mimeType().orElse("image/jpeg")))
        .build();

List<Content> contents = Arrays.asList(textContent, imageContent);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();
System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    myFile, err := client.Files.UploadFromPath(ctx, "path/to/sample.jpg", &genai.UploadFileConfig{
        MIMEType: "image/jpeg",
    })
    if err != nil {
        log.Fatal(err)
    }

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput([]interactions.Content{
                interactions.NewContent(interactions.TextContent{
                    Text: "Caption this image.",
                }),
                interactions.NewContent(interactions.ImageContent{
                    URI:      genai.Ptr(myFile.URI),
                    MimeType: interactions.ImageContentMimeType(myFile.MIMEType).ToPointer(),
                }),
            }),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

REST

# First upload the file (see Files API guide for details)
# Then use the file URI in the request:

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.8-flash",
    "input": [
      {"type": "text", "text": "Caption this image."},
      {
        "type": "image",
        "uri": "YOUR_FILE_URI",
        "mime_type": "image/jpeg"
      }
    ]
  }'

複数の画像を使用したプロンプト

input 配列に複数の画像オブジェクトを含めることで、1 つのプロンプトで複数の画像を提供できます。

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "text", "text": "What is different between these two images?"},
        {
            "type": "image",
            "uri": "https://example.com/image1.jpg",
            "mime_type": "image/jpeg"
        },
        {
            "type": "image",
            "uri": "https://example.com/image2.jpg",
            "mime_type": "image/jpeg"
        }
    ]
)
print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    model: "gemini-3.8-flash",
    input: [
        {type: "text", text: "What is different between these two images?"},
        {
            type: "image",
            uri: "https://example.com/image1.jpg",
            mime_type: "image/jpeg"
        },
        {
            type: "image",
            uri: "https://example.com/image2.jpg",
            mime_type: "image/jpeg"
        }
    ]
});
console.log(interaction.output_text);

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.ImageContent;
import com.google.genai.gaos.models.interactions.ImageContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;
import java.util.List;

Client client = new Client();

Content textContent =
    TextContent.builder().text("What is different between these two images?").build();
Content image1 =
    ImageContent.builder()
        .uri("https://example.com/image1.jpg")
        .mimeType(ImageContentMimeType.IMAGE_JPEG)
        .build();
Content image2 =
    ImageContent.builder()
        .uri("https://example.com/image2.jpg")
        .mimeType(ImageContentMimeType.IMAGE_JPEG)
        .build();

List<Content> contents = Arrays.asList(textContent, image1, image2);

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(contents))
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();
System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput([]interactions.Content{
                interactions.NewContent(interactions.TextContent{
                    Text: "What is different between these two images?",
                }),
                interactions.NewContent(interactions.ImageContent{
                    URI:      genai.Ptr("https://example.com/image1.jpg"),
                    MimeType: interactions.ImageContentMimeTypeImageJpeg.ToPointer(),
                }),
                interactions.NewContent(interactions.ImageContent{
                    URI:      genai.Ptr("https://example.com/image2.jpg"),
                    MimeType: interactions.ImageContentMimeTypeImageJpeg.ToPointer(),
                }),
            }),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.8-flash",
    "input": [
      {"type": "text", "text": "What is different between these two images?"},
      {
        "type": "image",
        "uri": "https://example.com/image1.jpg",
        "mime_type": "image/jpeg"
      },
      {
        "type": "image",
        "uri": "https://example.com/image2.jpg",
        "mime_type": "image/jpeg"
      }
    ]
  }'

オブジェクト検出

モデルは、画像内のオブジェクトを検出し、その境界ボックスの座標を取得するようにトレーニングされます。画像のサイズを基準とした座標は、[0, 1000] にスケーリングされます。元の画像サイズに基づいて、これらの座標をスケールダウンする必要があります。

Python

from google import genai
from pydantic import BaseModel, Field
from typing import List
import json

client = genai.Client()
prompt = "Detect the all of the prominent items in the image. The box_2d should be [ymin, xmin, ymax, xmax] normalized to 0-1000."

class BoundingBox(BaseModel):
    box_2d: List[int] = Field(description="The 2D bounding box of the item as [ymin, xmin, ymax, xmax] normalized to 0-1000.")
    mask: List[List[int]] = Field(description="The segmentation mask of the item as a polygon of [x,y] coordinates, normalized to 0-1000.")
    label: str = Field(description="A descriptive label for the item.")

class BoundingBoxes(BaseModel):
    boxes: List[BoundingBox]

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "text", "text": prompt},
        {
            "type": "image",
            "uri": "https://example.com/image.png",
            "mime_type": "image/png"
        }
    ],
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": BoundingBoxes.model_json_schema()
    }
)

bounding_boxes = BoundingBoxes.model_validate_json(interaction.output_text)
print(bounding_boxes)

JavaScript

import { GoogleGenAI } from "@google/genai";
import * as z from "zod";

const client = new GoogleGenAI({});
const prompt = "Detect the all of the prominent items in the image. The box_2d should be [ymin, xmin, ymax, xmax] normalized to 0-1000.";

const boundingBoxesSchema = z.object({
  boxes: z.array(z.object({
    box_2d: z.array(z.number()),
    mask: z.array(z.array(z.number())),
    label: z.string()
  }))
});

const interaction = await client.interactions.create({
  model: "gemini-3.8-flash",
  input: [
    { type: "text", text: prompt },
    {
      type: "image",
      uri: "https://example.com/image.png",
      mime_type: "image/png"
    }
  ],
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: z.toJSONSchema(boundingBoxesSchema)
  },
});

const result = boundingBoxesSchema.parse(JSON.parse(interaction.output_text));
console.log(result);

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.CreateModelInteractionResponseFormat;
import com.google.genai.gaos.models.interactions.ImageContent;
import com.google.genai.gaos.models.interactions.ImageContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.ResponseFormat;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.interactions.TextResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormatMimeType;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;
import java.util.List;
import java.util.Map;

Client client = new Client();
String prompt =
    "Detect the all of the prominent items in the image. The box_2d should be [ymin, xmin, ymax, xmax] normalized to 0-1000.";

Map<String, Object> boundingBoxSchema =
    Map.of(
        "type", "object",
        "properties",
            Map.of(
                "box_2d",
                    Map.of(
                        "type", "array",
                        "items", Map.of("type", "integer"),
                        "description",
                            "The 2D bounding box of the item as [ymin, xmin, ymax, xmax] normalized to 0-1000."),
                "mask",
                    Map.of(
                        "type", "array",
                        "items", Map.of("type", "array", "items", Map.of("type", "integer")),
                        "description",
                            "The segmentation mask of the item as a polygon of [x,y] coordinates, normalized to 0-1000."),
                "label",
                    Map.of("type", "string", "description", "A descriptive label for the item.")),
        "required", List.of("box_2d", "mask", "label"));

Map<String, Object> boundingBoxesSchema =
    Map.of(
        "type", "object",
        "properties", Map.of("boxes", Map.of("type", "array", "items", boundingBoxSchema)),
        "required", List.of("boxes"));

CreateModelInteractionResponseFormat format =
    CreateModelInteractionResponseFormat.of(
        ResponseFormat.of(
            TextResponseFormat.builder()
                .mimeType(TextResponseFormatMimeType.APPLICATION_JSON)
                .schema(boundingBoxesSchema)
                .build()));

Content textContent = TextContent.builder().text(prompt).build();
Content imageContent =
    ImageContent.builder()
        .uri("https://example.com/image.png")
        .mimeType(ImageContentMimeType.IMAGE_PNG)
        .build();

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(Arrays.asList(textContent, imageContent)))
        .responseFormat(format)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();
System.out.println(interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    prompt := "Detect the all of the prominent items in the image. The box_2d should be [ymin, xmin, ymax, xmax] normalized to 0-1000."

    boundingBoxSchema := map[string]any{
        "type": "object",
        "properties": map[string]any{
            "box_2d": map[string]any{
                "type":        "array",
                "items":       map[string]any{"type": "integer"},
                "description": "The 2D bounding box of the item as [ymin, xmin, ymax, xmax] normalized to 0-1000.",
            },
            "mask": map[string]any{
                "type":        "array",
                "items":       map[string]any{"type": "array", "items": map[string]any{"type": "integer"}},
                "description": "The segmentation mask of the item as a polygon of [x,y] coordinates, normalized to 0-1000.",
            },
            "label": map[string]any{
                "type":        "string",
                "description": "A descriptive label for the item.",
            },
        },
        "required": []string{"box_2d", "mask", "label"},
    }

    boundingBoxesSchema := map[string]any{
        "type": "object",
        "properties": map[string]any{
            "boxes": map[string]any{
                "type":  "array",
                "items": boundingBoxSchema,
            },
        },
        "required": []string{"boxes"},
    }

    format := interactions.NewCreateModelInteractionResponseFormat(
        interactions.NewResponseFormat(interactions.TextResponseFormat{
            MimeType: interactions.TextResponseFormatMimeTypeApplicationJSON.ToPointer(),
            Schema:   boundingBoxesSchema,
        }),
    )

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput([]interactions.Content{
                interactions.NewContent(interactions.TextContent{
                    Text: prompt,
                }),
                interactions.NewContent(interactions.ImageContent{
                    URI:      genai.Ptr("https://example.com/image.png"),
                    MimeType: interactions.ImageContentMimeTypeImagePng.ToPointer(),
                }),
            }),
            ResponseFormat: genai.Ptr(format),
        }),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.Interaction.OutputText != nil {
        fmt.Println(*res.Interaction.OutputText)
    }
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.8-flash",
    "input": [
      {"type": "text", "text": "Detect the all of the prominent items in the image. The box_2d should be [ymin, xmin, ymax, xmax] normalized to 0-1000."},
      {
        "type": "image",
        "uri": "https://example.com/image.png",
        "mime_type": "image/png"
      }
    ],
    "response_format": {
      "type": "text",
      "mime_type": "application/json",
      "schema": {
        "type": "object",
        "properties": {
          "boxes": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "box_2d": { "type": "array", "items": { "type": "integer" } },
                "mask": { "type": "array", "items": { "type": "array", "items": { "type": "integer" } } },
                "label": { "type": "string" }
              },
              "required": ["box_2d", "mask", "label"]
            }
          }
        },
        "required": ["boxes"]
      }
    }
  }'

その他の例については、Gemini クックブックをご覧ください。

セグメンテーション

Gemini モデルは、アイテムを検出するだけでなく、セグメント化して輪郭マスクを提供します。

モデルは JSON リストを予測します。各項目はセグメンテーション マスクを表します。各アイテムには、0 ～ 1, 000 の間で正規化された座標を持つ [ymin, xmin, ymax, xmax] 形式の境界ボックス（「box_2d」）、オブジェクトを識別するラベル（「label」）、最後に境界ボックス内のセグメンテーション マスク（0 ～ 1, 000 に正規化された [x, y] 座標のポリゴン）があります。

Python

from google import genai
from pydantic import BaseModel, Field
from typing import List
import json

client = genai.Client()

prompt = """
Give the segmentation masks for the wooden and glass items.
Output a JSON list of segmentation masks where each entry contains the 2D
bounding box in the key "box_2d", the segmentation mask in key "mask", and
the text label in the key "label". Use descriptive labels.
"""

class BoundingBox(BaseModel):
    box_2d: List[int] = Field(description="The 2D bounding box of the item as [ymin, xmin, ymax, xmax] normalized to 0-1000.")
    mask: List[List[int]] = Field(description="The segmentation mask of the item as a polygon of [x,y] coordinates, normalized to 0-1000.")
    label: str = Field(description="A descriptive label for the item.")

class BoundingBoxes(BaseModel):
    boxes: List[BoundingBox]

interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input=[
        {"type": "text", "text": prompt},
        {
            "type": "image",
            "uri": "https://example.com/image.png",
            "mime_type": "image/png"
        }
    ],
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": BoundingBoxes.model_json_schema()
    },
    generation_config={
        "thinking_level": "minimal"
    }
)

items = BoundingBoxes.model_validate_json(interaction.output_text)
print("Segmentation results:", items)

JavaScript

import { GoogleGenAI } from "@google/genai";
import * as z from "zod";

const client = new GoogleGenAI({});
const prompt = `
Give the segmentation masks for the wooden and glass items.
Output a JSON list of segmentation masks where each entry contains the 2D
bounding box in the key "box_2d", the segmentation mask in key "mask", and
the text label in the key "label". Use descriptive labels.
`;

const boundingBoxesSchema = z.object({
  boxes: z.array(z.object({
    box_2d: z.array(z.number()),
    mask: z.array(z.array(z.number())),
    label: z.string()
  }))
});

const interaction = await client.interactions.create({
  model: "gemini-3.8-flash",
  input: [
    { type: "text", text: prompt },
    {
      type: "image",
      uri: "https://example.com/image.png",
      mime_type: "image/png"
    }
  ],
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: z.toJSONSchema(boundingBoxesSchema)
  },
  generation_config: {
    thinking_level: "minimal"
  }
});

const result = boundingBoxesSchema.parse(JSON.parse(interaction.output_text));
console.log(result);

Java

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.CreateModelInteractionResponseFormat;
import com.google.genai.gaos.models.interactions.GenerationConfig;
import com.google.genai.gaos.models.interactions.ImageContent;
import com.google.genai.gaos.models.interactions.ImageContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.Model;
import com.google.genai.gaos.models.interactions.ResponseFormat;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.interactions.TextResponseFormat;
import com.google.genai.gaos.models.interactions.TextResponseFormatMimeType;
import com.google.genai.gaos.models.interactions.ThinkingLevel;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;
import java.util.List;
import java.util.Map;

Client client = new Client();

String prompt =
    "Give the segmentation masks for the wooden and glass items.\n"
        + "Output a JSON list of segmentation masks where each entry contains the 2D\n"
        + "bounding box in the key \"box_2d\", the segmentation mask in key \"mask\", and\n"
        + "the text label in the key \"label\". Use descriptive labels.";

Map<String, Object> boundingBoxSchema =
    Map.of(
        "type", "object",
        "properties",
            Map.of(
                "box_2d",
                    Map.of(
                        "type", "array",
                        "items", Map.of("type", "integer"),
                        "description",
                            "The 2D bounding box of the item as [ymin, xmin, ymax, xmax] normalized to 0-1000."),
                "mask",
                    Map.of(
                        "type", "array",
                        "items", Map.of("type", "array", "items", Map.of("type", "integer")),
                        "description",
                            "The segmentation mask of the item as a polygon of [x,y] coordinates, normalized to 0-1000."),
                "label",
                    Map.of("type", "string", "description", "A descriptive label for the item.")),
        "required", List.of("box_2d", "mask", "label"));

Map<String, Object> boundingBoxesSchema =
    Map.of(
        "type", "object",
        "properties", Map.of("boxes", Map.of("type", "array", "items", boundingBoxSchema)),
        "required", List.of("boxes"));

CreateModelInteractionResponseFormat format =
    CreateModelInteractionResponseFormat.of(
        ResponseFormat.of(
            TextResponseFormat.builder()
                .mimeType(TextResponseFormatMimeType.APPLICATION_JSON)
                .schema(boundingBoxesSchema)
                .build()));

Content textContent = TextContent.builder().text(prompt).build();
Content imageContent =
    ImageContent.builder()
        .uri("https://example.com/image.png")
        .mimeType(ImageContentMimeType.IMAGE_PNG)
        .build();

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model(Model.of("gemini-3.8-flash"))
        .input(InteractionsInput.ofContent(Arrays.asList(textContent, imageContent)))
        .responseFormat(format)
        .generationConfig(GenerationConfig.builder().thinkingLevel(ThinkingLevel.MINIMAL).build())
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();
System.out.println("Segmentation results: " + interaction.outputText().orElse(""));

Go

package main

import (
    "context"
    "fmt"
    "log"

    "google.golang.org/genai"
    "google.golang.org/genai/interactions/models/interactions"
    "google.golang.org/genai/interactions/models/operations"
)

func main() {
    ctx := context.Background()
    client, err := genai.NewClient(ctx, nil)
    if err != nil {
        log.Fatal(err)
    }

    prompt := "Give the segmentation masks for the wooden and glass items.\n" +
        "Output a JSON list of segmentation masks where each entry contains the 2D\n" +
        "bounding box in the key \"box_2d\", the segmentation mask in key \"mask\", and\n" +
        "the text label in the key \"label\". Use descriptive labels."

    boundingBoxSchema := map[string]any{
        "type": "object",
        "properties": map[string]any{
            "box_2d": map[string]any{
                "type":        "array",
                "items":       map[string]any{"type": "integer"},
                "description": "The 2D bounding box of the item as [ymin, xmin, ymax, xmax] normalized to 0-1000.",
            },
            "mask": map[string]any{
                "type":        "array",
                "items":       map[string]any{"type": "array", "items": map[string]any{"type": "integer"}},
                "description": "The segmentation mask of the item as a polygon of [x,y] coordinates, normalized to 0-1000.",
            },
            "label": map[string]any{
                "type":        "string",
                "description": "A descriptive label for the item.",
            },
        },
        "required": []string{"box_2d", "mask", "label"},
    }

    boundingBoxesSchema := map[string]any{
        "type": "object",
        "properties": map[string]any{
            "boxes": map[string]any{
                "type":  "array",
                "items": boundingBoxSchema,
            },
        },
        "required": []string{"boxes"},
    }

    format := interactions.NewCreateModelInteractionResponseFormat(
        interactions.NewResponseFormat(interactions.TextResponseFormat{
            MimeType: interactions.TextResponseFormatMimeTypeApplicationJSON.ToPointer(),
            Schema:   boundingBoxesSchema,
        }),
    )

    res, err := client.Interactions.Create(ctx, operations.CreateInteractionRequest{
        Body: operations.NewCreateInteractionRequestBody(interactions.CreateModelInteraction{
            Model: interactions.Model("gemini-3.8-flash"),
            Input: interactions.NewInteractionsInput([]interactions.Content{
                interactions.NewContent(interactions.TextContent{
                    Text: prompt,
                }),
                interactions.NewContent(interactions.ImageContent{
                    URI:      genai.Ptr("https://example.com/image.png"),
                    MimeType: interactions.ImageContentMimeTypeImagePng.ToPointer(),
                }),
            }),
            ResponseFormat: genai.Ptr(format),
            GenerationConfig: &interactions.GenerationConfig{
                ThinkingLevel: interactions.ThinkingLevelMinimal.ToPointer(),
            },
        }),
    })
    if err != nil {
        log.Fatal(err)
    }
    if res.Interaction.OutputText != nil {
        fmt.Println("Segmentation results: " + *res.Interaction.OutputText)
    }
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.8-flash",
    "input": [
      {"type": "text", "text": "Give the segmentation masks for the wooden and glass items.\nOutput a JSON list of segmentation masks where each entry contains the 2D\nbounding box in the key \"box_2d\", the segmentation mask in key \"mask\", and\nthe text label in the key \"label\". Use descriptive labels."},
      {
        "type": "image",
        "uri": "https://example.com/image.png",
        "mime_type": "image/png"
      }
    ],
    "response_format": {
      "type": "text",
      "mime_type": "application/json",
      "schema": {
        "type": "object",
        "properties": {
          "boxes": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "box_2d": { "type": "array", "items": { "type": "integer" } },
                "mask": { "type": "array", "items": { "type": "array", "items": { "type": "integer" } } },
                "label": { "type": "string" }
              },
              "required": ["box_2d", "mask", "label"]
            }
          }
        },
        "required": ["boxes"]
      }
    },
    "generation_config": {
      "thinking_level": "minimal"
    }
  }'
カップケーキが並べられたテーブル。木製とガラス製のオブジェクトがハイライト表示されている
オブジェクトとセグメンテーション マスクを含むセグメンテーション出力の例

サポートされている画像形式

Gemini は、次の画像形式の MIME タイプをサポートしています。

  • PNG - image/png
  • JPEG - image/jpeg
  • WEBP - image/webp
  • HEIC - image/heic
  • HEIF - image/heif

その他のファイル入力方法については、ファイル入力方法ガイドをご覧ください。

機能

すべての Gemini モデル バージョンはマルチモーダルであり、画像キャプション、Visual Question & Answering、画像分類、オブジェクト検出、セグメンテーションなど、幅広い画像処理タスクやコンピュータ ビジョン タスクで使用できます。

Gemini は、品質とパフォーマンスの要件に応じて、特殊な ML モデルを使用する必要性を軽減できます。

最新のモデル バージョンは、汎用的な機能に加えて、オブジェクト検出やセグメンテーションなどの高度なタスクの精度を向上させるように特別にトレーニングされています。

制限事項と主な技術情報

ファイルの上限

Gemini モデルは、リクエストあたり最大 3,600 個の画像ファイルをサポートしています。

トークンの計算

  • 両方の寸法が 384 ピクセル以下の場合は 258 個のトークン。大きな画像は 768x768 ピクセルのタイルに分割され、それぞれ 258 個のトークンを消費します。

タイルの数を計算するおおよその式は次のとおりです。

  • 切り抜き単位のサイズ（おおよそ floor(min(width, height) / 1.5）を計算します。
  • 各ディメンションをクロップ単位サイズで割り、乗算してタイルの数を取得します。

たとえば、960x540 の画像の場合、切り抜き単位サイズは 360 になります。各ディメンションを 360 で割ると、タイルの数は 3 × 2 = 6 になります。

メディアの解像度

Gemini 3 では、media_resolution パラメータを使用して、マルチモーダル ビジョン処理をきめ細かく制御できます。media_resolution パラメータは、入力画像または動画フレームごとに割り当てられるトークンの最大数を決定します。解像度が高いほど、モデルが細かいテキストを読み取ったり、小さな詳細を識別する能力が向上しますが、トークンの使用量とレイテンシが増加します。

おすすめの方法やお役立ち情報

  • 画像が正しく回転していることを確認します。
  • 鮮明でぼやけていない画像を使用します。
  • テキストを含む 1 つの画像を使用する場合は、input 配列の画像の前にテキスト プロンプトを配置します。

次のステップ

このガイドでは、画像ファイルをアップロードし、画像入力からテキスト出力を生成する方法について説明します。詳細については、次のリソースをご覧ください。

  • Files API: Gemini で使用するファイルのアップロードと管理について説明します。
  • システム指示: システム指示を使用すると、特定のニーズやユースケースに基づいてモデルの動作を制御できます。
  • ファイル プロンプト戦略: Gemini API は、テキスト、画像、音声、動画データによるプロンプト（マルチモーダル プロンプトとも呼ばれます）をサポートしています。
  • 安全に関するガイダンス: 生成 AI モデルは、不正確、偏見がある、不快な出力など、予期しない出力を生成することがあります。このような出力による危害のリスクを制限するには、後処理と人間による評価が不可欠です。