Interpretacja obrazu

  from google.genai import types    with open('path/to/small-sample.jpg', 'rb') as f:       image_bytes = f.read()    response = client.models.generate_content(     model='gemini-2.5-flash',     contents=[       types.Part.from_bytes(         data=image_bytes,         mime_type='image/jpeg',       ),       'Caption this image.'     ]   )    print(response.text) 

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-2.5-flash",   contents: contents, }); console.log(response.text); 

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-2.5-flash",   contents,   nil, )  fmt.Println(result.Text()) 

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-2.5-flash: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 

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-2.5-flash",     contents=["What is this image?", image], )  print(response.text) 

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-2.5-flash",     contents: [     {       inlineData: {         mimeType: 'image/jpeg',         data: base64ImageData,       },     },     { text: "Caption this image." }   ],   });   console.log(result.text); }  main(); 

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-2.5-flash",     contents,     nil,   )    fmt.Println(result.Text()) } 

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-2.5-flash: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 

from google import genai  client = genai.Client()  my_file = client.files.upload(file="path/to/sample.jpg")  response = client.models.generate_content(     model="gemini-2.5-flash",     contents=[my_file, "Caption this image."], )  print(response.text) 

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-2.5-flash",     contents: createUserContent([       createPartFromUri(myfile.uri, myfile.mimeType),       "Caption this image.",     ]),   });   console.log(response.text); }  await main(); 

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-2.5-flash",       contents,       nil,   )    fmt.Println(result.Text()) } 

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-2.5-flash: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 

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-2.5-flash",     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) 

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-2.5-flash",     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(); 

// 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-2.5-flash",   contents,   nil, )  fmt.Println(result.Text()) 

# 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-2.5-flash: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 

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-2.5-flash",                                           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)  

from google import genai from google.genai import types from PIL import Image, ImageDraw import io import base64 import json import numpy as np import os  client = genai.Client()  def parse_json(json_output: str):   # Parsing out the markdown fencing   lines = json_output.splitlines()   for i, line in enumerate(lines):     if line == "```json":       json_output = "\n".join(lines[i+1:])  # Remove everything before "```json"       output = json_output.split("```")[0]  # Remove everything after the closing "```"       break  # Exit the loop once "```json" is found   return json_output  def extract_segmentation_masks(image_path: str, output_dir: str = "segmentation_outputs"):   # Load and resize image   im = Image.open(image_path)   im.thumbnail([1024, 1024], Image.Resampling.LANCZOS)    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.   """    config = types.GenerateContentConfig(     thinking_config=types.ThinkingConfig(thinking_budget=0) # set thinking_budget to 0 for better results in object detection   )    response = client.models.generate_content(     model="gemini-2.5-flash",     contents=[prompt, im], # Pillow images can be directly passed as inputs (which will be converted by the SDK)     config=config   )    # Parse JSON response   items = json.loads(parse_json(response.text))    # Create output directory   os.makedirs(output_dir, exist_ok=True)    # Process each mask   for i, item in enumerate(items):       # Get bounding box coordinates       box = item["box_2d"]       y0 = int(box[0] / 1000 * im.size[1])       x0 = int(box[1] / 1000 * im.size[0])       y1 = int(box[2] / 1000 * im.size[1])       x1 = int(box[3] / 1000 * im.size[0])        # Skip invalid boxes       if y0 >= y1 or x0 >= x1:           continue        # Process mask       png_str = item["mask"]       if not png_str.startswith("data:image/png;base64,"):           continue        # Remove prefix       png_str = png_str.removeprefix("data:image/png;base64,")       mask_data = base64.b64decode(png_str)       mask = Image.open(io.BytesIO(mask_data))        # Resize mask to match bounding box       mask = mask.resize((x1 - x0, y1 - y0), Image.Resampling.BILINEAR)        # Convert mask to numpy array for processing       mask_array = np.array(mask)        # Create overlay for this mask       overlay = Image.new('RGBA', im.size, (0, 0, 0, 0))       overlay_draw = ImageDraw.Draw(overlay)        # Create overlay for the mask       color = (255, 255, 255, 200)       for y in range(y0, y1):           for x in range(x0, x1):               if mask_array[y - y0, x - x0] > 128:  # Threshold for mask                   overlay_draw.point((x, y), fill=color)        # Save individual mask and its overlay       mask_filename = f"{item['label']}_{i}_mask.png"       overlay_filename = f"{item['label']}_{i}_overlay.png"        mask.save(os.path.join(output_dir, mask_filename))        # Create and save overlay       composite = Image.alpha_composite(im.convert('RGBA'), overlay)       composite.save(os.path.join(output_dir, overlay_filename))       print(f"Saved mask and overlay for {item['label']} to {output_dir}")  # Example usage if __name__ == "__main__":   extract_segmentation_masks("path/to/image.png")