Gemini Deep Research 現已推出預先發布版，提供協作規劃、視覺化、MCP 支援等功能。

圖像理解

Gemini 模型從一開始就建構於多模態的基礎上，因此可執行各種圖像處理和電腦視覺工作，包括但不限於生成圖像說明、分類和回答圖像問題，無須訓練專門的機器學習模型。

除了提供一般多模態功能，Gemini 模型還透過額外訓練，針對特定用途 (例如物體偵測) 提升準確度。

將圖片傳送給 Gemini

你可以透過兩種方式將圖片提供給 Gemini 做為輸入內容：

傳遞內嵌圖片資料：適合較小的檔案 (包括提示在內，要求總大小小於 20 MB)。
使用 File API 上傳圖片：建議用於較大的檔案，或在多個要求中重複使用圖片。

傳遞內嵌圖片資料

您可以在對 generateContent 的要求中傳遞內嵌圖片資料。您可以提供 Base64 編碼字串形式的圖片資料，也可以直接讀取本機檔案 (視語言而定)。

以下範例說明如何從本機檔案讀取圖片，並傳遞至 generateContent API 進行處理。

Python

  from google import genai
  from google.genai import types

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

  client = genai.Client()
  response = client.models.generate_content(
    model='gemini-3-flash-preview',
    contents=[
      types.Part.from_bytes(
        data=image_bytes,
        mime_type='image/jpeg',
      ),
      'Caption this image.'
    ]
  )

  print(response.text)

JavaScript

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

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

const contents = [
  {
    inlineData: {
      mimeType: "image/jpeg",
      data: base64ImageFile,
    },
  },
  { text: "Caption this image." },
];

const response = await ai.models.generateContent({
  model: "gemini-3-flash-preview",
  contents: contents,
});
console.log(response.text);

Go

bytes, _ := os.ReadFile("path/to/small-sample.jpg")

parts := []*genai.Part{
  genai.NewPartFromBytes(bytes, "image/jpeg"),
  genai.NewPartFromText("Caption this image."),
}

contents := []*genai.Content{
  genai.NewContentFromParts(parts, genai.RoleUser),
}

result, _ := client.Models.GenerateContent(
  ctx,
  "gemini-3-flash-preview",
  contents,
  nil,
)

fmt.Println(result.Text())

REST

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

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

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-X POST \
-d '{
    "contents": [{
    "parts":[
        {
            "inline_data": {
            "mime_type":"image/jpeg",
            "data": "'"$(base64 $B64FLAGS $IMG_PATH)"'"
            }
        },
        {"text": "Caption this image."},
    ]
    }]
}' 2> /dev/null

您也可以從網址擷取圖片、轉換為位元組，然後傳遞至 generateContent，如以下範例所示。

Python

from google import genai
from google.genai import types

import requests

image_path = "https://goo.gle/instrument-img"
image_bytes = requests.get(image_path).content
image = types.Part.from_bytes(
  data=image_bytes, mime_type="image/jpeg"
)

client = genai.Client()

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents=["What is this image?", image],
)

print(response.text)

JavaScript

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

async function main() {
  const ai = new GoogleGenAI({});

  const imageUrl = "https://goo.gle/instrument-img";

  const response = await fetch(imageUrl);
  const imageArrayBuffer = await response.arrayBuffer();
  const base64ImageData = Buffer.from(imageArrayBuffer).toString('base64');

  const result = await ai.models.generateContent({
    model: "gemini-3-flash-preview",
    contents: [
    {
      inlineData: {
        mimeType: 'image/jpeg',
        data: base64ImageData,
      },
    },
    { text: "Caption this image." }
  ],
  });
  console.log(result.text);
}

main();

Go

package main

import (
  "context"
  "fmt"
  "os"
  "io"
  "net/http"
  "google.golang.org/genai"
)

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

  // Download the image.
  imageResp, _ := http.Get("https://goo.gle/instrument-img")

  imageBytes, _ := io.ReadAll(imageResp.Body)

  parts := []*genai.Part{
    genai.NewPartFromBytes(imageBytes, "image/jpeg"),
    genai.NewPartFromText("Caption this image."),
  }

  contents := []*genai.Content{
    genai.NewContentFromParts(parts, genai.RoleUser),
  }

  result, _ := client.Models.GenerateContent(
    ctx,
    "gemini-3-flash-preview",
    contents,
    nil,
  )

  fmt.Println(result.Text())
}

REST

IMG_URL="https://goo.gle/instrument-img"

MIME_TYPE=$(curl -sIL "$IMG_URL" | grep -i '^content-type:' | awk -F ': ' '{print $2}' | sed 's/\r$//' | head -n 1)
if [[ -z "$MIME_TYPE" || ! "$MIME_TYPE" == image/* ]]; then
  MIME_TYPE="image/jpeg"
fi

# Check for macOS
if [[ "$(uname)" == "Darwin" ]]; then
  IMAGE_B64=$(curl -sL "$IMG_URL" | base64 -b 0)
elif [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
  IMAGE_B64=$(curl -sL "$IMG_URL" | base64)
else
  IMAGE_B64=$(curl -sL "$IMG_URL" | base64 -w0)
fi

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
            {
              "inline_data": {
                "mime_type":"'"$MIME_TYPE"'",
                "data": "'"$IMAGE_B64"'"
              }
            },
            {"text": "Caption this image."}
        ]
      }]
    }' 2> /dev/null

使用 File API 上傳圖片

如要處理大型檔案或重複使用同一張圖片，請使用 Files API。下列程式碼會上傳圖片檔案，然後在呼叫 generateContent 時使用該檔案。如需更多資訊和範例，請參閱 Files API 指南。

Python

from google import genai

client = genai.Client()

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

response = client.models.generate_content(
    model="gemini-3-flash-preview",
    contents=[my_file, "Caption this image."],
)

print(response.text)

JavaScript

import {
  GoogleGenAI,
  createUserContent,
  createPartFromUri,
} from "@google/genai";

const ai = new GoogleGenAI({});

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

  const response = await ai.models.generateContent({
    model: "gemini-3-flash-preview",
    contents: createUserContent([
      createPartFromUri(myfile.uri, myfile.mimeType),
      "Caption this image.",
    ]),
  });
  console.log(response.text);
}

await main();

Go

package main

import (
  "context"
  "fmt"
  "os"
  "google.golang.org/genai"
)

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

  uploadedFile, _ := client.Files.UploadFromPath(ctx, "path/to/sample.jpg", nil)

  parts := []*genai.Part{
      genai.NewPartFromText("Caption this image."),
      genai.NewPartFromURI(uploadedFile.URI, uploadedFile.MIMEType),
  }

  contents := []*genai.Content{
      genai.NewContentFromParts(parts, genai.RoleUser),
  }

  result, _ := client.Models.GenerateContent(
      ctx,
      "gemini-3-flash-preview",
      contents,
      nil,
  )

  fmt.Println(result.Text())
}

REST

IMAGE_PATH="path/to/sample.jpg"
MIME_TYPE=$(file -b --mime-type "${IMAGE_PATH}")
NUM_BYTES=$(wc -c < "${IMAGE_PATH}")
DISPLAY_NAME=IMAGE

tmp_header_file=upload-header.tmp

# Initial resumable request defining metadata.
# The upload url is in the response headers dump them to a file.
curl "https://generativelanguage.googleapis.com/upload/v1beta/files" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -D upload-header.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME}'}}" 2> /dev/null

upload_url=$(grep -i "x-goog-upload-url: " "${tmp_header_file}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file}"

# Upload the actual bytes.
curl "${upload_url}" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Length: ${NUM_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${IMAGE_PATH}" 2> /dev/null > file_info.json

file_uri=$(jq -r ".file.uri" file_info.json)
echo file_uri=$file_uri

# Now generate content using that file
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"file_data":{"mime_type": "'"${MIME_TYPE}"'", "file_uri": "'"${file_uri}"'"}},
          {"text": "Caption this image."}]
        }]
      }' 2> /dev/null > response.json

cat response.json
echo

jq ".candidates[].content.parts[].text" response.json

使用多張圖片撰寫提示

您可以在 contents 陣列中加入多個圖片 Part 物件，在單一提示中提供多張圖片。這些可以是內嵌資料 (本機檔案或網址) 和 File API 參照的組合。

Python

from google import genai
from google.genai import types

client = genai.Client()

# Upload the first image
image1_path = "path/to/image1.jpg"
uploaded_file = client.files.upload(file=image1_path)

# Prepare the second image as inline data
image2_path = "path/to/image2.png"
with open(image2_path, 'rb') as f:
    img2_bytes = f.read()

# Create the prompt with text and multiple images
response = client.models.generate_content(

    model="gemini-3-flash-preview",
    contents=[
        "What is different between these two images?",
        uploaded_file,  # Use the uploaded file reference
        types.Part.from_bytes(
            data=img2_bytes,
            mime_type='image/png'
        )
    ]
)

print(response.text)

JavaScript

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

const ai = new GoogleGenAI({});

async function main() {
  // Upload the first image
  const image1_path = "path/to/image1.jpg";
  const uploadedFile = await ai.files.upload({
    file: image1_path,
    config: { mimeType: "image/jpeg" },
  });

  // Prepare the second image as inline data
  const image2_path = "path/to/image2.png";
  const base64Image2File = fs.readFileSync(image2_path, {
    encoding: "base64",
  });

  // Create the prompt with text and multiple images

  const response = await ai.models.generateContent({

    model: "gemini-3-flash-preview",
    contents: createUserContent([
      "What is different between these two images?",
      createPartFromUri(uploadedFile.uri, uploadedFile.mimeType),
      {
        inlineData: {
          mimeType: "image/png",
          data: base64Image2File,
        },
      },
    ]),
  });
  console.log(response.text);
}

await main();

Go

// Upload the first image
image1Path := "path/to/image1.jpg"
uploadedFile, _ := client.Files.UploadFromPath(ctx, image1Path, nil)

// Prepare the second image as inline data
image2Path := "path/to/image2.jpeg"
imgBytes, _ := os.ReadFile(image2Path)

parts := []*genai.Part{
  genai.NewPartFromText("What is different between these two images?"),
  genai.NewPartFromBytes(imgBytes, "image/jpeg"),
  genai.NewPartFromURI(uploadedFile.URI, uploadedFile.MIMEType),
}

contents := []*genai.Content{
  genai.NewContentFromParts(parts, genai.RoleUser),
}

result, _ := client.Models.GenerateContent(
  ctx,
  "gemini-3-flash-preview",
  contents,
  nil,
)

fmt.Println(result.Text())

REST

# Upload the first image
IMAGE1_PATH="path/to/image1.jpg"
MIME1_TYPE=$(file -b --mime-type "${IMAGE1_PATH}")
NUM1_BYTES=$(wc -c < "${IMAGE1_PATH}")
DISPLAY_NAME1=IMAGE1

tmp_header_file1=upload-header1.tmp

curl "https://generativelanguage.googleapis.com/upload/v1beta/files" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -D upload-header1.tmp \
  -H "X-Goog-Upload-Protocol: resumable" \
  -H "X-Goog-Upload-Command: start" \
  -H "X-Goog-Upload-Header-Content-Length: ${NUM1_BYTES}" \
  -H "X-Goog-Upload-Header-Content-Type: ${MIME1_TYPE}" \
  -H "Content-Type: application/json" \
  -d "{'file': {'display_name': '${DISPLAY_NAME1}'}}" 2> /dev/null

upload_url1=$(grep -i "x-goog-upload-url: " "${tmp_header_file1}" | cut -d" " -f2 | tr -d "\r")
rm "${tmp_header_file1}"

curl "${upload_url1}" \
  -H "Content-Length: ${NUM1_BYTES}" \
  -H "X-Goog-Upload-Offset: 0" \
  -H "X-Goog-Upload-Command: upload, finalize" \
  --data-binary "@${IMAGE1_PATH}" 2> /dev/null > file_info1.json

file1_uri=$(jq ".file.uri" file_info1.json)
echo file1_uri=$file1_uri

# Prepare the second image (inline)
IMAGE2_PATH="path/to/image2.png"
MIME2_TYPE=$(file -b --mime-type "${IMAGE2_PATH}")

if [[ "$(base64 --version 2>&1)" = *"FreeBSD"* ]]; then
  B64FLAGS="--input"
else
  B64FLAGS="-w0"
fi
IMAGE2_BASE64=$(base64 $B64FLAGS $IMAGE2_PATH)

# Now generate content using both images
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3-flash-preview:generateContent" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[
          {"text": "What is different between these two images?"},
          {"file_data":{"mime_type": "'"${MIME1_TYPE}"'", "file_uri": '$file1_uri'}},
          {
            "inline_data": {
              "mime_type":"'"${MIME2_TYPE}"'",
              "data": "'"$IMAGE2_BASE64"'"
            }
          }
        ]
      }]
    }' 2> /dev/null > response.json

cat response.json
echo

jq ".candidates[].content.parts[].text" response.json

物件偵測

模型經過訓練後，可偵測圖片中的物件並取得定界框座標。相對於圖片尺寸的座標會縮放至 [0, 1000]。您需要根據原始圖片大小，縮放這些座標。

Python

from google import genai
from google.genai import types
from PIL import Image
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."

image = Image.open("/path/to/image.png")

config = types.GenerateContentConfig(
  response_mime_type="application/json"
  )

response = client.models.generate_content(model="gemini-3-flash-preview",
                                          contents=[image, prompt],
                                          config=config
                                          )

width, height = image.size
bounding_boxes = json.loads(response.text)

converted_bounding_boxes = []
for bounding_box in bounding_boxes:
    abs_y1 = int(bounding_box["box_2d"][0]/1000 * height)
    abs_x1 = int(bounding_box["box_2d"][1]/1000 * width)
    abs_y2 = int(bounding_box["box_2d"][2]/1000 * height)
    abs_x2 = int(bounding_box["box_2d"][3]/1000 * width)
    converted_bounding_boxes.append([abs_x1, abs_y1, abs_x2, abs_y2])

print("Image size: ", width, height)
print("Bounding boxes:", converted_bounding_boxes)

如需更多範例，請參閱 Gemini 教戰手冊中的下列筆記本：

支援的圖片格式

Gemini 支援下列圖片格式 MIME 類型：

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

如要瞭解其他檔案輸入方式，請參閱「檔案輸入方式」指南。

功能

所有 Gemini 模型版本都是多模態模型，可用於各種圖像處理和電腦視覺工作，包括但不限於圖片說明、視覺問答、圖片分類和物件偵測。

視品質和效能需求而定，Gemini 可減少使用專業機器學習模型的需求。

最新模型版本經過特別訓練，除了強化物件偵測等一般功能外，還能提升特定工作的準確度。

限制和重要技術資訊

檔案限制

Gemini 模型每項要求最多可支援 3,600 個圖片檔案。

計算權杖

如果長邊和短邊都小於或等於 384 像素，則為 258 個權杖。較大的圖片會分割成 768x768 像素的圖塊，每個圖塊需支付 258 個權杖。

計算圖塊數量的粗略公式如下：

計算裁剪單元大小，大約是：floor(min(width, height) / 1.5)。
將每個維度除以裁剪單元大小，然後相乘，即可取得圖塊數量。

舉例來說，如果圖片尺寸為 960x540，裁剪單位大小為 360。將每個維度除以 360，圖塊數量為 3 * 2 = 6。

媒體解析度

Gemini 3 推出 media_resolution 參數，可精細控管多模態視覺處理作業。media_resolution 參數會決定每個輸入圖片或影片影格分配到的詞元數量上限。 解析度越高，模型就越能辨識細小文字或細節，但也會增加權杖用量和延遲時間。

如要進一步瞭解參數及其對權杖計算的影響，請參閱「媒體解析度」指南。

提示與最佳做法

確認圖片已正確旋轉。
使用清晰的圖片，避免模糊不清。
如果使用含有文字的單一圖片，請將文字提示詞放在 contents 陣列的圖片部分之後。

後續步驟

本指南說明如何上傳圖片檔案，以及如何從圖片輸入內容生成文字輸出內容。如要進一步瞭解相關內容，請參閱下列資源：

Files API：進一步瞭解如何上傳及管理檔案，以供 Gemini 使用。
系統指令：系統指令可根據特定需求和用途，引導模型行為。
檔案提示策略：Gemini API 支援使用文字、圖片、音訊和影片資料提示，也就是多模態提示。
安全指南：生成式 AI 模型有時會產生出乎意料的輸出內容，例如不準確、有偏見或令人反感的內容。後續處理和人工評估是不可或缺的環節，有助於降低這類輸出內容造成危害的風險。