> ## Content Index > Fetch the complete content index at: https://serpapi.com/blog/llms.txt > Use this file to discover other available public pages before exploring further. # Scrape Google Lens with Python - URL: https://serpapi.com/blog/scrape-google-lens/ - Published: 2023-01-25T11:00:00.000Z - Updated: 2025-04-04T00:28:33.000Z - Description: This blog post is a step-by-step tutorial to scraping Google Lens using Python. - Author: Artur Chukhrai - Tags: Google Lens, Web Scraping, Python ## What will be scraped ![wwbs-google-lens](https://user-images.githubusercontent.com/81998012/210693341-3474574d-cb2e-4a12-a2e7-b82d1abcfd23.png) > Google has introduced a new layout for Google Lens. Here is an updated tutorial on [how to scrape Google Lens results using Python](https://serpapi.com/blog/scraping-google-lens-new-layout-visual-matches/) ## Using Google Lens API from SerpApi If you don't need an explanation, have a look at [the full code example in the online IDE](https://replit.com/@chukhraiartur/blog-google-lens-api#main.py). ```python from serpapi import GoogleSearch import json params = { 'api_key': '...', 'engine': 'google_lens', 'url': 'https://user-images.githubusercontent.com/81998012/210290011-c175603d-f319-4620-b886-1eaad5c94d84.jpg', 'hl': 'en', } search = GoogleSearch(params) # data extraction on the SerpApi backend google_lens_results = search.get_dict() # JSON -> Python dict del google_lens_results['search_metadata'] del google_lens_results['search_parameters'] print(json.dumps(google_lens_results, indent=2, ensure_ascii=False)) ``` ### Why using API? There're a couple of reasons that may use API, ours in particular: - No need to create a parser from scratch and maintain it. - Bypass blocks from Google: solve CAPTCHA or solve IP blocks. - Pay for proxies, and CAPTCHA solvers. - Don't need to use browser automation. SerpApi handles everything on the backend with fast response times under \~4.3 seconds per request and without browser automation, which becomes much faster. Response times and status rates are shown under [SerpApi Status page](https://serpapi.com/status): ![serpapi-status-product](https://user-images.githubusercontent.com/81998012/210670143-c6216e06-7bce-4fe2-a997-dbe27032605f.png) Head to the [Google Lens playground](https://serpapi.com/playground?engine=google%5Flens) for a live and interactive demo. ### Preparation Install library: ```lang-none pip install google-search-results ``` [google-search-results](https://github.com/serpapi/google-search-results-python) is a SerpApi API package. ### Code Explanation Import libraries: ```python from serpapi import GoogleSearch import json ``` | Library | Purpose | | --------------------------------------------------- | ---------------------------------------------------------------------------------------------- | | GoogleSearch | to scrape and parse Google results using [SerpApi](https://serpapi.com/) web scraping library. | | [json](https://docs.python.org/3/library/json.html) | to convert extracted data to a JSON object. | The parameters are defined for generating the URL. If you want to pass other parameters to the URL, you can do so using the `params` dictionary: ```python params = { 'api_key': '...', 'engine': 'google_lens', 'url': 'https://user-images.githubusercontent.com/81998012/210290011-c175603d-f319-4620-b886-1eaad5c94d84.jpg', 'hl': 'en', } ``` | Parameters | Explanation | | ----------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [api\_key](https://serpapi.com/google-lens-api#api-parameters-serpapi-parameters-api-key) | Parameter defines the SerpApi private key to use. You can find it under [your account -> API key](https://serpapi.com/manage-api-key). | | [engine](https://serpapi.com/google-lens-api#api-parameters-serpapi-parameters-engine) | Set parameter to google\_lens to use the Google Lens API engine. | | [url](https://serpapi.com/google-lens-api#api-parameters-search-query-url) | Parameter defines the URL of an image to perform the Google Lens search. | | [hl](https://serpapi.com/google-lens-api#api-parameters-localization-hl) | Parameter defines the language to use for the Google Lens search. It's a two-letter language code. Head to the [Google languages page](https://serpapi.com/google-languages) for a full list of supported Google languages. | ๐Ÿ“ŒNote: You can also add other [API Parameters](https://serpapi.com/google-lens-api#api-parameters). Then, we create a `search` object where the data is retrieved from the SerpApi backend. In the `google_lens_results` dictionary we get data from JSON: ```python search = GoogleSearch(params) # data extraction on the SerpApi backend google_lens_results = search.get_dict() # JSON -> Python dict ``` The `google_lens_results` dictionary, in addition to the necessary data, contains information about the request. The request information is not needed, so we remove the corresponding keys using the [del statement](https://docs.python.org/3/tutorial/datastructures.html#the-del-statement): ```python del google_lens_results['search_metadata'] del google_lens_results['search_parameters'] ``` After all the data is retrieved, it is output in JSON format: ```python print(json.dumps(google_lens_results, indent=2, ensure_ascii=False)) ``` ### Output ```json { "reverse_image_search": { "link": "https://www.google.com/search?tbs=sbi:AMhZZiurdULpuTy4_1HSkPv2ZrEBN9afXDH2j7s2drhaSQmdFuOJlf9HaxhrjxEfBrWzj1xi-ZONFSwWi3UlhnMtRXlu68S24Kv5fLuNstTqFQfpUQXGbPBuplF8jDJuvLTDAJow06N44R7keGB1GOU5fRzsc4rirzA" }, "knowledge_graph": [ { "title": "Black cat", "link": "https://www.google.com/search?q=Black+cat&kgmid=/m/03dj64&hl=en&gl=US", "more_images": { "link": "https://www.google.com/search?q=Black+cat&kgmid=/m/03dj64&ved=0EOTpBwgAKAAwAA&source=.lens.button&tbm=isch&hl=en&gl=US", "serpapi_link": "https://serpapi.com/search.json?device=desktop&engine=google&gl=US&google_domain=google.com&hl=en&q=Black+cat&tbm=isch" }, "thumbnail": "https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcQEdppH7x_edJGSKSky2KSKK773r4HOp55AnejH0-sYBpO3-M5w", "images": [ { "title": "Image #1 for Black cat", "source": "https://vbspca.com/tag/stigma/", "link": "https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcQEdppH7x_edJGSKSky2KSKK773r4HOp55AnejH0-sYBpO3-M5w", "size": { "width": 293, "height": 172 } }, ... other images ] }, ... other knowledge graph results ], "visual_matches": [ { "position": 1, "title": "Pet Talk: Smoke can create problems quickly for your cat | VailDaily.com", "link": "https://www.vaildaily.com/opinion/pet-talk-smoke-can-create-problems-quickly-for-your-cat/", "source": "vaildaily.com", "source_icon": "https://encrypted-tbn0.gstatic.com/favicon-tbn?q=tbn:ANd9GcSXpzpJuQgYt20Jd-moiGdOr6HoDpS-WQ_vjcfrNvtLJy_gjDrYJIs3abOVeBb7g24x5kLNBg2T-KGdiQ_NkFkcBjt2s7exhkQg46swp-DMTF3S1_lemg", "thumbnail": "https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcQCjR3dx5H8xz9fSevbe6JqPtBlakSxJwrECbaMS64UcP05CwC4" }, ... other visual matches results ] } ``` ## DIY solution This section is to show the comparison between our solution and the DIY solution. The fact is that when you click on a regular link, it changes to another link. The GIF below shows this: ![google-lens](https://user-images.githubusercontent.com/81998012/210688661-855c42c5-1e3b-4329-82ff-394ba0c7d1b7.gif) The data is correspondingly different and there is no way to extract it without reverse engineering. For simplicity, the DIY solution uses [playwright](https://pypi.org/project/playwright/). It helps to extract data from the modified link. The data extraction itself is done with [selectolax](https://pypi.org/project/selectolax/) because it has Lexbor parser which is incredibly fast. In terms of syntax, it is very similar to both `bs4` and `parsel`, making it easy to use. Please note that [selectolax does not currently support XPath](https://news.ycombinator.com/item?id=28144159). Example code to integrate: ```python from playwright.sync_api import sync_playwright from selectolax.lexbor import LexborHTMLParser import json def run(playwright): image_url = 'https://user-images.githubusercontent.com/81998012/210290011-c175603d-f319-4620-b886-1eaad5c94d84.jpg' page = playwright.chromium.launch(headless=True).new_page() page.goto(f'https://lens.google.com/uploadbyurl?url={image_url}&hl=en') parser = LexborHTMLParser(page.content()) page.close() reverse_image_search = { 'link': parser.root.css_first('.kuwdsf .VfPpkd-RLmnJb').attributes['href'] } knowledge_graph = { 'title': parser.root.css_first('.DeMn2d').text(), 'subtitle': parser.root.css_first('.XNTym').text() if parser.root.css_first('.XNTym') else None, 'link': parser.root.css_first('.OCDsub .VfPpkd-RLmnJb').attributes['href'], 'more_images': parser.root.css_first('[aria-label="More Images"]').attributes['href'], 'thumbnail': parser.root.css_first('.oLfv5c .FH8DCc').attributes['src'], 'images': [ { 'title': image.attributes['aria-label'], 'source': image.attributes['href'], 'link': image.css_first('.wETe9b').attributes['src'] } for image in parser.root.css('.Y02Gld a') ] } visual_matches = [ { 'title': result.css_first('.UAiK1e').text(), 'link': result.css_first('.GZrdsf').attributes['href'], 'source': result.css_first('.fjbPGe').text(), 'source_icon': result.css_first('.KRdrw').attributes['src'], 'thumbnail': result.css_first('.jFVN1').attributes['src'] } for result in parser.root.css('.xuQ19b') ] google_lens_results = { 'reverse_image_search': reverse_image_search, 'knowledge_graph': knowledge_graph, 'visual_matches': visual_matches } print(json.dumps(google_lens_results, indent=2, ensure_ascii=False)) with sync_playwright() as playwright: run(playwright) ``` ๐Ÿ“ŒNote: In the [online IDE](https://replit.com/@chukhraiartur/blog-google-lens-api#main.py) this code does not work because the Replit does not support the playwright. You can do all the manipulations described below to check how the DIY solution works. ### Preparation Install library: ```lang-none pip install playwright selectolax ``` Install the required browser: ```lang-none playwright install chromium ``` ### Code Explanation Import libraries: ```python from playwright.sync_api import sync_playwright from selectolax.lexbor import LexborHTMLParser import json ``` | Library | Purpose | | -------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- | | [sync\_playwright](https://playwright.dev/python/docs/api/class-playwright) | for synchronous API. [playwright have asynchronous API](https://playwright.dev/python/docs/api/class-playwright) as well using asyncio module. | | [LexborHTMLParser](https://selectolax.readthedocs.io/en/latest/lexbor.html#lexborhtmlparser) | a fast HTML5 parser with CSS selectors using [Lexbor](https://github.com/rushter/selectolax#available-backends) engine. | | [json](https://docs.python.org/3/library/json.html) | to convert extracted data to a JSON object. | Declare a function: ```python def run(playwright): # further code ... ``` The `image_url` variable is defined, which contains the URL of the image: ```python image_url = 'https://user-images.githubusercontent.com/81998012/210290011-c175603d-f319-4620-b886-1eaad5c94d84.jpg' ``` Initialize `playwright`, connect to `chromium`, `launch()` a browser `new_page()` and `goto()` a given URL: ```python page = playwright.chromium.launch(headless=True).new_page() page.goto(f'https://lens.google.com/uploadbyurl?url={image_url}&hl=en') ``` | Parameters | Explanation | | -------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | | [playwright.chromium](https://playwright.dev/python/docs/api/class-playwright#playwright-chromium) | is a connection to the Chromium browser instance. | | [launch()](https://playwright.dev/python/docs/api/class-browsertype#browser-type-launch) | will launch the browser, and headless argument will run it in headless mode. Default is True. | | [new\_page()](https://playwright.dev/python/docs/api/class-browser#browser-new-page) | creates a new page in a new browser context. | | [page.goto()](https://playwright.dev/python/docs/api/class-page#page-goto) | will make a request to provided website. | After the page has loaded, pass HTML content to `Lexbor` and `close` the browser: ```python parser = LexborHTMLParser(page.content()) page.close() ``` The first thing to extract is the reverse image search link. To do this, you need to pass the `.kuwdsf .VfPpkd-RLmnJb` selector that is responsible for this element to the [css\_first() method](https://selectolax.readthedocs.io/en/latest/lexbor.html#selectolax.lexbor.LexborHTMLParser.css%5Ffirst). Then extract the value of the `href` attribute from [attributes](https://selectolax.readthedocs.io/en/latest/lexbor.html#selectolax.lexbor.LexborNode.attributes): ```python reverse_image_search = { 'link': parser.root.css_first('.kuwdsf .VfPpkd-RLmnJb').attributes['href'] } ``` The algorithm for extracting data from the knowledge graph works similarly. There is a difference in extracting `title` and `subtitle`. For them, the text content is retrieved, so the corresponding [text() method](https://selectolax.readthedocs.io/en/latest/lexbor.html#selectolax.lexbor.LexborHTMLParser.text) is used. Sometimes there may not be a `subtitle`, so a [ternary expression](https://docs.python.org/3/reference/expressions.html#conditional-expressions) is used for such cases: ```python knowledge_graph = { 'title': parser.root.css_first('.DeMn2d').text(), 'subtitle': parser.root.css_first('.XNTym').text() if parser.root.css_first('.XNTym') else None, 'link': parser.root.css_first('.OCDsub .VfPpkd-RLmnJb').attributes['href'], 'more_images': parser.root.css_first('[aria-label="More Images"]').attributes['href'], 'thumbnail': parser.root.css_first('.oLfv5c .FH8DCc').attributes['src'], 'images': [ { 'title': image.attributes['aria-label'], 'source': image.attributes['href'], 'link': image.css_first('.wETe9b').attributes['src'] } for image in parser.root.css('.Y02Gld a') ] } ``` For both knowledge graph images and visual matches, [list comprehensions](https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions) are used to provide a concise way to create lists. To find multiple elements and iterate them, the [css() method](https://selectolax.readthedocs.io/en/latest/lexbor.html#selectolax.lexbor.LexborHTMLParser.css) was used: ```python visual_matches = [ { 'title': result.css_first('.UAiK1e').text(), 'link': result.css_first('.GZrdsf').attributes['href'], 'source': result.css_first('.fjbPGe').text(), 'source_icon': result.css_first('.KRdrw').attributes['src'], 'thumbnail': result.css_first('.jFVN1').attributes['src'] } for result in parser.root.css('.xuQ19b') ] ``` The `google_lens_results` dictionary is created and previously extracted data is added to the corresponding keys: ```python google_lens_results = { 'reverse_image_search': reverse_image_search, 'knowledge_graph': knowledge_graph, 'visual_matches': visual_matches } ``` After the all data is retrieved, it is output in JSON format: ```python print(json.dumps(google_lens_results, indent=2, ensure_ascii=False)) ``` Run your code using [context manager](https://realpython.com/python-with-statement/): ```python with sync_playwright() as playwright: run(playwright) ``` ### Output ```json { "reverse_image_search": { "link": "https://www.google.com/search?tbs=sbi:AMhZZivbhNZ5ZFwCBpcEUAlEHVFDQnaZIC-4PcD5za7g6xuScvksUbf8osCVDaAg70m3b2eMkaodmPSm_1PiNZgCOEV5wma9PX1piaCV3GtLReFcsjRlP7On4aF3HUJAyPinMnEYGIATNPvQ7PLMoMZlmUXj4uQ1xHw" }, "knowledge_graph": { "title": "Black cat", "subtitle": null, "link": "https://www.google.com/search?q=Black+cat&kgmid=/m/03dj64&hl=en&gl=US", "more_images": "https://www.google.com/search?q=Black+cat&kgmid=/m/03dj64&ved=0EOTpBwgAKAAwAA&source=.lens.button&tbm=isch&hl=en&gl=US", "thumbnail": "https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcQEdppH7x_edJGSKSky2KSKK773r4HOp55AnejH0-sYBpO3-M5w", "images": [ { "title": "Image #1 for Black cat", "source": "https://vbspca.com/tag/stigma/", "link": "https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcQEdppH7x_edJGSKSky2KSKK773r4HOp55AnejH0-sYBpO3-M5w" }, ... other images ] }, "visual_matches": [ { "title": "Pet Talk: Smoke can create problems quickly for your cat | VailDaily.com", "link": "https://www.vaildaily.com/opinion/pet-talk-smoke-can-create-problems-quickly-for-your-cat/", "source": "vaildaily.com", "source_icon": "https://encrypted-tbn0.gstatic.com/favicon-tbn?q=tbn:ANd9GcSXpzpJuQgYt20Jd-moiGdOr6HoDpS-WQ_vjcfrNvtLJy_gjDrYJIs3abOVeBb7g24x5kLNBg2T-KGdiQ_NkFkcBjt2s7exhkQg46swp-DMTF3S1_lemg", "thumbnail": "https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcQCjR3dx5H8xz9fSevbe6JqPtBlakSxJwrECbaMS64UcP05CwC4" }, ... other visual matches results ] } ``` ## Links - [Code in the online IDE](https://replit.com/@chukhraiartur/blog-google-lens-api#main.py) - [Google Lens API](https://serpapi.com/google-lens-api) - [Google Lens playground](https://serpapi.com/playground?engine=google%5Flens) - [Playwright](https://playwright.dev/python/) - [Selectolax Lexbor module](https://selectolax.readthedocs.io/en/latest/lexbor.html) --- Join us on [Twitter](https://twitter.com/serp%5Fapi) | [YouTube](https://www.youtube.com/channel/UCUgIHlYBOD3yA3yDIRhg%5Fmg) Add a [Feature Request](https://github.com/serpapi/public-roadmap/issues)๐Ÿ’ซ or a [Bug](https://github.com/serpapi/public-roadmap/issues)๐Ÿž