Gemini 3.8 Flash 现已推出。试试看。

Google 会使用 AI 技术将内容翻译成您偏好的语言。AI 翻译可能包含错误。

图片理解

Gemini 模型从一开始就具有多模态特性，可用于执行各种图片处理和计算机视觉任务，包括但不限于图片配文、分类和视觉问答，而无需训练专门的机器学习模型。

除了通用多模态功能之外，Gemini 模型还通过额外训练，针对特定应用场景（例如对象检测和分割）提供更高的准确率。

将图片传递给 Gemini

您可以使用多种方法向 Gemini 提供图片作为输入：

使用网址传递图片：非常适合可公开访问的图片。
传递内嵌图片数据：用于传递 base64 编码的图片数据。
使用 File API 上传图片：建议用于较大的文件，或在多个请求中重复使用图片。

使用网址传递图片

您可以使用 Files API 上传图片，并在请求中传递该图片：

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"
      }
    ]
  }'

传递内嵌图片数据

您可以以 base64 编码的字符串形式提供图片数据：

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"
      }
    ]
  }'

使用 File API 上传图片

对于大型文件，或者为了能够重复使用同一图片文件，请使用 Files API。请参阅 Files API 指南。

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"
      }
    ]
  }'

使用多张图片进行提示

您可以在单个提示中提供多张图片，只需在 input 数组中添加多个图片对象即可：

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"
      }
    ]
  }'

对象检测

模型经过训练，可以检测图片中的对象并获取其边界框坐标。相对于图片尺寸的坐标会缩放到 [0, 1000]。您需要根据原始图片大小对这些坐标进行反缩放。

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"]
      }
    }
  }'

如需查看更多示例，请访问 Gemini Cookbook。

分割

Gemini 模型不仅可以检测商品，还可以对商品进行分割并提供其轮廓遮罩。

模型会预测一个 JSON 列表，其中每个项都表示一个分割掩码。每个商品都有一个边界框（“box_2d”），格式为 [ymin, xmin, ymax, xmax]，其中包含介于 0 到 1000 之间的归一化坐标；一个用于标识对象的标签（“label”）；最后是边界框内的分割掩码，以归一化为 0-1000 的 [x, y] 坐标多边形表示。

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"
    }
  }'

一张放有纸杯蛋糕的桌子，其中木制和玻璃物品突出显示 — 包含对象和分割遮盖的分割输出示例

支持的图片格式

Gemini 支持以下图片格式 MIME 类型：

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

如需了解其他文件输入方法，请参阅文件输入方法指南。

功能

所有 Gemini 模型版本都是多模态模型，可用于各种图像处理和计算机视觉任务，包括但不限于图片配文、视觉问答、图像分类、对象检测和分割。

Gemini 可以减少对专业机器学习模型的需求，具体取决于您的质量和性能要求。

最新模型版本经过专门训练，除了通用功能外，还可提高专业任务的准确性，例如增强的对象检测和分割。

限制和关键技术信息

文件数量限制

Gemini 模型支持的每个请求的图片文件数量上限为 3,600 个。

token 计算

如果两个维度均小于或等于 384 像素，则为 258 个 token。较大的图片会被分块为 768x768 像素的图块，每个图块需要 258 个 token。

计算图块数量的粗略公式如下：

计算裁剪单元大小（大致为：floor(min(width, height) / 1.5）。
将每个维度除以裁剪单元大小，然后将结果相乘，即可得到图块数量。

例如，对于尺寸为 960x540 的图片，剪裁单位大小为 360。将每个维度除以 360，得到的图块数量为 3 * 2 = 6。

媒体分辨率

Gemini 3 通过 media_resolution 参数引入了对多模态视觉处理的精细控制。media_resolution 参数用于确定为每个输入图片或视频帧分配的 token 数量上限。分辨率越高，模型读取细小文字或识别细微细节的能力就越强，但 token 用量和延迟时间也会增加。

技巧和最佳做法

验证图片是否已正确旋转。
使用清晰且不模糊的图片。
如果使用包含文本的单张图片，请在 input 数组中将文本提示放在图片之前。

后续步骤

本指南介绍了如何上传图片文件并根据图片输入生成文本输出。如需了解详情，请参阅以下资源：

Files API：详细了解如何上传和管理文件以供 Gemini 使用。
系统指令：系统指令可让您根据自己的特定需求和使用情形来控制模型的行为。
文件提示策略：Gemini API 支持使用文本、图片、音频和视频数据进行提示，也称为多模态提示。
安全指南：生成式 AI 模型有时会生成意料之外的输出，例如不准确、有偏见或令人反感的输出。后处理和人工评估对于限制此类输出造成的危害风险至关重要。