From 59713a5f03298533bdd13593bdb9c136ec3f949b Mon Sep 17 00:00:00 2001 From: Robert Dill Date: Fri, 2 Feb 2018 16:45:35 -0500 Subject: [PATCH] updated C09 with better recognition - added face service and now draw rectangle around first face --- .../Documentation/answers/images_complete.js | 78 ++++---- .../answers/pageStyles_complete.css | 4 +- .../answers/z2c-image_complete.js | 105 ++++++++--- Chapter09/controller/restapi/router.js | 2 +- Chapter09/readme.MD | 172 +++++++++++++++--- Chapter09/readme.pdf | Bin 69032 -> 91558 bytes README.md | 24 +-- 7 files changed, 285 insertions(+), 100 deletions(-) diff --git a/Chapter09/Documentation/answers/images_complete.js b/Chapter09/Documentation/answers/images_complete.js index e69fc595..9965bfaf 100644 --- a/Chapter09/Documentation/answers/images_complete.js +++ b/Chapter09/Documentation/answers/images_complete.js @@ -54,48 +54,58 @@ exports.upload= function(req, res, next){ exports.classify= function(req, res, next){ req.pipe(req.busboy); - req.busboy.on('file', function(fieldname, file, filename) { + req.busboy.on('file', function(fieldname, file, filename) + { // not all file systems are friendly to names with spaces in them. // if this name has spaces in it, replace them with an underscore. - var fileName = filename.replace(/ /g,"_"); - var newFile = path.join(path.dirname(require.main.filename),'images',fileName); - var fd = fs.openSync(newFile, 'w'); - var fstream = fs.createWriteStream(newFile, {fd: fd}); - file.pipe(fstream); - // now that we have the image stored on the server, send it to watson - fstream.on('close', function () { - var params = {images_file: fs.createReadStream(newFile), classifier_ids: [vr_classifier] }; - var visual_recognition = watson.visual_recognition({ - api_key: apiKey, - version: 'v3', version_date: '2016-05-20' - }); - visual_recognition.classify(params, function(err, classify_results) { - // if there is an error, log it. - // this should be extended to include a res.send() so the browser does not repeat the request - // which it will do on requests with no response - if (err) {console.log(err);} - else - // the request was successful, send the results back to the browser - { res.send(classify_results); } - }); - } ); + var fileName = filename.replace(/ /g,"_"); + var newFile = path.join(path.dirname(require.main.filename),'images',fileName); + var fd = fs.openSync(newFile, 'w'); + var fstream = fs.createWriteStream(newFile, {fd: fd}); + var _res = {}; + file.pipe(fstream); + // now that we have the image stored on the server, send it to watson + fstream.on('close', function () + { + var params = {images_file: fs.createReadStream(newFile), classifier_ids: [vr_classifier] }; + var visual_recognition = watson.visual_recognition( + { + api_key: apiKey, + version: 'v3', version_date: '2016-05-20' + }); + visual_recognition.detectFaces(params, function(err, faces) + { + if (err) {console.log(err); res.send({'results': 'failed', 'where': 'detectFaces', 'error': err});} + else + { + console.log('detecFaces successful: '+JSON.stringify(faces)); _res.faces = faces; + var params = {images_file: fs.createReadStream(newFile), classifier_ids: [vr_classifier] }; + visual_recognition.classify(params, function(err, classify_results) + { + if (err) {console.log(err); res.send({'results': 'failed', 'where': 'classify', 'error': err});} + else + { + console.log('classify successful: '+JSON.stringify(classify_results)); _res.classify = classify_results; + res.send({'results': 'success', 'data': _res}); + } + }); + } + }); }); - } + }); +} /** - * find looks in a collection of images to find those images which most closely match the provided image + * detect faces looks at an image to find the faces in it * @param {object} req - nodejs object with the request information * req.body holds post parameters - * req.body.collection - the id of the collection to use * @param {object} res - nodejs response object * @param {object} next - nodejs next object - used if this routine does not provide a response */ -exports.find= function(req, res, next){ +exports.detect= function(req, res, next){ // get rid of blanks in the file name var imageName = req.body.image.replace(/ /g,"_"); var newFile = path.join(path.dirname(require.main.filename),'images',imageName); - // save the collection name - var collectionName = req.body.collection; // read in the (previously transferred) file name var params = {image_file: fs.createReadStream(newFile), collection_id: collectionName }; // set the visual_recognition parameters @@ -103,12 +113,12 @@ exports.find= function(req, res, next){ api_key: apiKey, version: 'v3', version_date: '2016-05-20' }); - // request similar images - visual_recognition.findSimilar(params, function(err, similar_results) { - // on error, log the erro - if (err) {console.log(err);} + // request face recognition + visual_recognition.detectFaces(params, function(err, response) { + // on error, log the erro + if (err) {console.log(err); res.send({'results': 'failed', 'where': 'detectFaces', 'error': err});} else // send the results back to the browser - {res.send(similar_results);} + {console.log(JSON.stringify(response)); res.send({'results': 'success', 'data': response});} }); } diff --git a/Chapter09/Documentation/answers/pageStyles_complete.css b/Chapter09/Documentation/answers/pageStyles_complete.css index a09c42e5..599d05c0 100755 --- a/Chapter09/Documentation/answers/pageStyles_complete.css +++ b/Chapter09/Documentation/answers/pageStyles_complete.css @@ -6,8 +6,8 @@ .imageReceiver { background-color: #E0F8E0; - width: 200px; - height: 200px + min-width: 200px; + min-height: 200px; } .dd_upload { outline: 2px solid #298A08; diff --git a/Chapter09/Documentation/answers/z2c-image_complete.js b/Chapter09/Documentation/answers/z2c-image_complete.js index 42bb90e3..e5929b82 100644 --- a/Chapter09/Documentation/answers/z2c-image_complete.js +++ b/Chapter09/Documentation/answers/z2c-image_complete.js @@ -18,6 +18,7 @@ var b_Droppable, _url, _image, droppedFiles, $form, c_res ; // visual recognition has an image limit of 2Mb var maxSize = 2097152; +var _factor; /** * initialize the visual recognition page. @@ -86,7 +87,9 @@ function initiateVR() // remove drag target highlighting when the mouse leaves the drag area _image.on('dragleave dragend drop', function() { _image.removeClass('dragover'); }); // do the following when the image is dragged in and dropped - _image.on('drop', function(e) { droppedFiles = e.originalEvent.dataTransfer.files; + _image.on('drop', function(e) + { + droppedFiles = e.originalEvent.dataTransfer.files; console.log("dropped file name: "+droppedFiles[0].name); // build a table to display image information var fileSpecs = ""; @@ -100,12 +103,37 @@ function initiateVR() c_res.empty(); // display the table c_res.append(fileSpecs); - // display the image + // load the image to get the original size of the inbound image. + // we need this information to correctly draw a box around the face later on. var reader = new FileReader(); reader.onload = function(e) { - var __image = '
' - _image.empty(); - _image.append(__image); } + var __image = '' + _image.empty(); _image.append(__image); _image.hide(); + var _img = $("#fileToLoad"); + window.setTimeout(function() + { + // the display window is 200 pixels high. calculate the multiplication factor to fit this window and save it for later use + _factor = 200/_img[0].naturalHeight; + // calculate the target width + var _width = _factor*_img[0].naturalWidth; + var _height = 200; + // create a drawing canvas and center it in the imageReceiver div + __image = '
' + // empty the div of the image we just loaded and append this canvas to the now empty div + _image.empty(); _image.append(__image); + // get the drawing context for the canvas + var ctx = $("#fileCanvas")[0].getContext("2d"); + // create a drawable image + var imageObj = new Image(); + // link the source to the image dropped on the imageReceiver div + imageObj.src = e.target.result; + // when the image has loaded into memory, draw it + imageObj.onload = function () { + ctx.drawImage(imageObj, 0, 0, _width, _height); + } + _image.show(); + }, 100); + } reader.readAsDataURL(droppedFiles[0]); }); // update the image area css @@ -122,29 +150,58 @@ function initiateVR() */ function displayImageClassificationResults(_target, _data) { - // empty the html target area - _target.empty(); - console.log("displayImageClassificationResults entered with: "+_data); - // turn the returned string back into a JSON object - var imageResults = JSON.parse(_data); - console.log("displayImageClassificationResults parsed results: ",imageResults); + // empty the html target area + _target.empty(); + // turn the returned string back into a JSON object + var imageResults = JSON.parse(_data); + // check to see if the request for face detection and clssification were both successful + if (imageResults.results !== 'success') + { + _target.append('Visual Recognition failed at: '+imageResults.where+' with error:
'+imageResults.err.message); + return; + } + // check to make sure that there was at least one face in the provided image + if (imageResults.data.faces.images[0].faces.length === 0) + { + _target.append('There is no face in the provided image: '+imageResults.data.faces.images[0].image); + return; + } + else + { + // get the rectangle of the first face found in the image + var _imgRect = imageResults.data.faces.images[0].faces[0].face_location; + // the top value provided is consistently about 1.3 times the actual vertical offset of the face + // the following correction factor is to address that bug + var vert_correction = 0.3; + // get the drawing context of the canvas + var ctx = $("#fileCanvas")[0].getContext("2d"); + // create a rectangle to fit around the face + ctx.rect(_factor*_imgRect.left, vert_correction*_factor*_imgRect.top, _factor*_imgRect.width, _factor*_imgRect.height); + // set the line width to 6 pixels + ctx.lineWidth="6"; + // set the line color to blue + ctx.strokeStyle="blue"; + // draw the rectangle. + ctx.stroke(); - // create a display table - var _tbl = "
File Name"+droppedFiles[0].name+"
"; + } - var _image = imageResults.images[0].image; - // iterate through the classification results, displaying one table row for each result row - if (imageResults.images[0].classifiers.length === 0) - { _tbl += ""} - else - { - for (each in imageResults.images[0].classifiers[0].classes) + // create a display table + var _tbl = "
Image ClassProbability
No Results with higher than 50% probability
"; + + var _image = imageResults.data.classify.images[0].image; + // iterate through the classification results, displaying one table row for each result row + if (imageResults.data.classify.images[0].classifiers.length === 0) + { _tbl += ""} + else { - (function (_idx, _obj) { - _tbl += ''; - })(each, imageResults.images[0].classifiers[0].classes) + for (each in imageResults.data.classify.images[0].classifiers[0].classes) + { + (function (_idx, _obj) { + _tbl += ''; + })(each, imageResults.data.classify.images[0].classifiers[0].classes) + } } - } // close the table _tbl += "
Image ClassProbability
No Results with higher than 50% probability
'+_obj[_idx].class+''+_obj[_idx].score+'
'+_obj[_idx].class+''+_obj[_idx].score+'
"; // and append it to the target. diff --git a/Chapter09/controller/restapi/router.js b/Chapter09/controller/restapi/router.js index 371621de..d02d756b 100644 --- a/Chapter09/controller/restapi/router.js +++ b/Chapter09/controller/restapi/router.js @@ -34,4 +34,4 @@ router.post('/auth/logout*', auth.logout); router.post('/images/upload', images.upload); router.post('/images/classify', images.classify); -router.post('/images/find', images.find); +router.post('/images/detect', images.detect); diff --git a/Chapter09/readme.MD b/Chapter09/readme.MD index 4367664c..ebfdf7d9 100644 --- a/Chapter09/readme.MD +++ b/Chapter09/readme.MD @@ -83,12 +83,73 @@ We worked with three files in the original version: - HTML/CSS/pageStyles.css - HTML/js/z2c-image.js - Follow the original tutorial for changes to images.js and pageStyles.css, we will use those files in their original format. + Follow the original tutorial for changes to pageStyles.css, we will use that file in its original format. + ## images.js + +We no longer need the find routine, as that service is no longer available in Visual Recognition. We will execute two requests in the classification routine to extract both face information and ckassification information. +### classification + +This function receives an image from the browser and then performs two transforms on that image. First, it looks to see if the image has a face in it and then classifies the image. We could, if we chose, stop the process after the face identification if it comes back with no identified faces. In this case we would need to update this function to execute a res.send() with an appropriately packaged JSON object. The display routine on the browser side would also have to be updated if the structure of the returned object changed. +```javascript +/** + * classify saves the provided image and then sends that image to watson for classification + * @param {object} req - nodejs object with the request information + * req.body holds post parameters + * @param {object} res - nodejs response object + * @param {object} next - nodejs next object - used if this routine does not provide a response + */ +exports.classify= function(req, res, next){ + + req.pipe(req.busboy); + req.busboy.on('file', function(fieldname, file, filename) + { + // not all file systems are friendly to names with spaces in them. + // if this name has spaces in it, replace them with an underscore. + var fileName = filename.replace(/ /g,"_"); + var newFile = path.join(path.dirname(require.main.filename),'images',fileName); + var fd = fs.openSync(newFile, 'w'); + var fstream = fs.createWriteStream(newFile, {fd: fd}); + var _res = {}; + file.pipe(fstream); + // now that we have the image stored on the server, send it to watson + fstream.on('close', function () + { + var params = {images_file: fs.createReadStream(newFile), classifier_ids: [vr_classifier] }; + var visual_recognition = watson.visual_recognition( + { + api_key: apiKey, + version: 'v3', version_date: '2016-05-20' + }); + visual_recognition.detectFaces(params, function(err, faces) + { + if (err) {console.log(err); res.send({'results': 'failed', 'where': 'detectFaces', 'error': err});} + else + { + console.log('detecFaces successful: '+JSON.stringify(faces)); _res.faces = faces; + var params = {images_file: fs.createReadStream(newFile), classifier_ids: [vr_classifier] }; + visual_recognition.classify(params, function(err, classify_results) + { + if (err) {console.log(err); res.send({'results': 'failed', 'where': 'classify', 'error': err});} + else + { + console.log('classify successful: '+JSON.stringify(classify_results)); _res.classify = classify_results; + res.send({'results': 'success', 'data': _res}); + } + }); + } + }); + }); + }); +} +``` + + ## z2c-images.js + We have less work to do in z2c-image.js than before. functions: - ***initiateVR*** is called after authentication has succeeded. This function is unchanged from the original and looks like this: + ***initiateVR*** is called after authentication has succeeded. This function was changed from the original, specifically to support face identification, so the image is now displayed into an HTML canvas element instead of an img element. You'll see those changes near the end of the function. ```javascript /** * initialize the visual recognition page. @@ -157,7 +218,9 @@ function initiateVR() // remove drag target highlighting when the mouse leaves the drag area _image.on('dragleave dragend drop', function() { _image.removeClass('dragover'); }); // do the following when the image is dragged in and dropped - _image.on('drop', function(e) { droppedFiles = e.originalEvent.dataTransfer.files; + _image.on('drop', function(e) + { + droppedFiles = e.originalEvent.dataTransfer.files; console.log("dropped file name: "+droppedFiles[0].name); // build a table to display image information var fileSpecs = ""; @@ -171,12 +234,37 @@ function initiateVR() c_res.empty(); // display the table c_res.append(fileSpecs); - // display the image + // load the image to get the original size of the inbound image. + // we need this information to correctly draw a box around the face later on. var reader = new FileReader(); reader.onload = function(e) { - var __image = '
' - _image.empty(); - _image.append(__image); } + var __image = '' + _image.empty(); _image.append(__image); _image.hide(); + var _img = $("#fileToLoad"); + window.setTimeout(function() + { + // the display window is 200 pixels high. calculate the multiplication factor to fit this window and save it for later use + _factor = 200/_img[0].naturalHeight; + // calculate the target width + var _width = _factor*_img[0].naturalWidth; + var _height = 200; + // create a drawing canvas and center it in the imageReceiver div + __image = '
' + // empty the div of the image we just loaded and append this canvas to the now empty div + _image.empty(); _image.append(__image); + // get the drawing context for the canvas + var ctx = $("#fileCanvas")[0].getContext("2d"); + // create a drawable image + var imageObj = new Image(); + // link the source to the image dropped on the imageReceiver div + imageObj.src = e.target.result; + // when the image has loaded into memory, draw it + imageObj.onload = function () { + ctx.drawImage(imageObj, 0, 0, _width, _height); + } + _image.show(); + }, 100); + } reader.readAsDataURL(droppedFiles[0]); }); // update the image area css @@ -205,33 +293,62 @@ we're going to remove the hyperlink `````` processing so that we just display */ function displayImageClassificationResults(_target, _data) { - // empty the html target area - _target.empty(); - console.log("displayImageClassificationResults entered with: "+_data); - // turn the returned string back into a JSON object - var imageResults = JSON.parse(_data); - console.log("displayImageClassificationResults parsed results: ",imageResults); + // empty the html target area + _target.empty(); + // turn the returned string back into a JSON object + var imageResults = JSON.parse(_data); + // check to see if the request for face detection and clssification were both successful + if (imageResults.results !== 'success') + { + _target.append('Visual Recognition failed at: '+imageResults.where+' with error:
'+imageResults.err.message); + return; + } + // check to make sure that there was at least one face in the provided image + if (imageResults.data.faces.images[0].faces.length === 0) + { + _target.append('There is no face in the provided image: '+imageResults.data.faces.images[0].image); + return; + } + else + { + // get the rectangle of the first face found in the image + var _imgRect = imageResults.data.faces.images[0].faces[0].face_location; + // the top value provided is consistently about 1.3 times the actual vertical offset of the face + // the following correction factor is to address that bug + var vert_correction = 0.3; + // get the drawing context of the canvas + var ctx = $("#fileCanvas")[0].getContext("2d"); + // create a rectangle to fit around the face + ctx.rect(_factor*_imgRect.left, vert_correction*_factor*_imgRect.top, _factor*_imgRect.width, _factor*_imgRect.height); + // set the line width to 6 pixels + ctx.lineWidth="6"; + // set the line color to blue + ctx.strokeStyle="blue"; + // draw the rectangle. + ctx.stroke(); - // create a display table - var _tbl = "
File Name"+droppedFiles[0].name+"
"; + } - var _image = imageResults.images[0].image; - // iterate through the classification results, displaying one table row for each result row - if (imageResults.images[0].classifiers.length === 0) - { _tbl += ""} - else - { - for (each in imageResults.images[0].classifiers[0].classes) + // create a display table + var _tbl = "
Image ClassProbability
No Results with higher than 50% probability
"; + + var _image = imageResults.data.classify.images[0].image; + // iterate through the classification results, displaying one table row for each result row + if (imageResults.data.classify.images[0].classifiers.length === 0) + { _tbl += ""} + else { - (function (_idx, _obj) { - var _disabled = (collections[_obj[_idx].class] == null) ? ", mic_disabled" : ""; - _tbl += '"'; - })(each, imageResults.images[0].classifiers[0].classes) + for (each in imageResults.data.classify.images[0].classifiers[0].classes) + { + (function (_idx, _obj) { + _tbl += ''; + })(each, imageResults.data.classify.images[0].classifiers[0].classes) + } } - } // close the table _tbl += "
Image ClassProbability
No Results with higher than 50% probability
'+_obj[_idx].class+''+_obj[_idx].score+'
'+_obj[_idx].class+''+_obj[_idx].score+'
"; // and append it to the target. + console.log(_tbl); _target.append(_tbl); } ``` @@ -257,4 +374,5 @@ Now that you've completed your code updates, what will this program do? - male - not you, but probably a male - female - not you, but probably a female - you - this will display the name of your zip file (hence the earlier recommendation to make the name memorable) + - The application will also draw a box around the first (highest probability) face found in the provided image. - you can find test images simply by doing a web search for face images, which allows you to use different images in your demonstration. \ No newline at end of file diff --git a/Chapter09/readme.pdf b/Chapter09/readme.pdf index 5459bf43cd3a9eb163b6ba8b675b30db089a8796..712ae6ad1f8430c224a34e0b2bb38fa1289e9f7e 100644 GIT binary patch delta 72226 zcmeFXWpEwKvL-5K28)@=VrH-`W@ct)USej+VrFKtn3>sPW@cG5@;>{Vdr#bX5pU+b zm_Ku?t2+v^sxq_ltIS%xO47lK#=v6@p_v$%m>8ManHX!WxDf%ido9SFkJZ!ezUSNd 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z+f=^@d8u&zE79}+$a|T84i45|b?^R%_>0EQ_Wv;d0;m7pkCXFPiS)nIxHRy}Y6dW9s-#Dt(hI!i&eh3 iZ5CxaJ7=;NO<&FvBTOMtkbxj}005DiT1-J4@&5pUn=dN> diff --git a/README.md b/README.md index fedd7b2e..a19c5264 100644 --- a/README.md +++ b/README.md @@ -9,47 +9,47 @@ We are currently at release 1.3, which includes updates to Chapter 8 (Watson Dis ##The chapter structure is: -###1). The Story, Architecture for this app +### 1). The Story, Architecture for this app -###2). [Setting up Bluemix](./Chapter02/README.pdf) +### 2). [Setting up Bluemix](./Chapter02/README.pdf) -###3). Building your first Watson App +### 3). Building your first Watson App - demo: [https://z2c-c03.mybluemix.net/](https://z2c-c03.mybluemix.net/) -###4). [Getting Watson to talk back](./Chapter04/README.pdf) +### 4). [Getting Watson to talk back](./Chapter04/README.pdf) - demo: [https://z2c-c04.mybluemix.net/](https://z2c-c04.mybluemix.net/) -###5). [Understanding Classifiers](./Chapter05/README.pdf) +### 5). [Understanding Classifiers](./Chapter05/README.pdf) - demo: [https://z2c-c05.mybluemix.net/](https://z2c-c05.mybluemix.net/) -###6). [Creating a custom dialog with Watson](./Chapter06/README.pdf) +### 6). [Creating a custom dialog with Watson](./Chapter06/README.pdf) - demo: [https://z2c-c06.mybluemix.net/](https://z2c-c06.mybluemix.net/) -###7). SSL, https: sessions and users (set up security, use cloudant for sessionStore, manage user profiles) +### 7). SSL, https: sessions and users (set up security, use cloudant for sessionStore, manage user profiles) - demo: [https://z2c-c07.mybluemix.net/](https://z2c-c07.mybluemix.net/) -###8). [Understanding the Watson Discovery News APIs](./Chapter08/README.pdf) +### 8). [Understanding the Watson Discovery News APIs](./Chapter08/README.pdf) - demo: [https://z2c-c08.mybluemix.net/](https://z2c-c08.mybluemix.net/) -###9). Visual Recognition [updated directions are in the Chapter09 Readme.pdf](./Chapter09/readme.MD) +### 9). Visual Recognition [updated directions are in the Chapter09 Readme.pdf](./Chapter09/readme.MD) - demo: [https://z2c-c09.mybluemix.net/](https://z2c-c09.mybluemix.net/) -###10). Watson Conversations +### 10). Watson Conversations - demo: [https://z2c-c10.mybluemix.net/](https://z2c-c10.mybluemix.net/) -###11). [Finding Answers with Watson Discovery](./Chapter11/README.pdf) +### 11). [Finding Answers with Watson Discovery](./Chapter11/README.pdf) - demo: [https://z2c-c11.mybluemix.net/](https://z2c-c11.mybluemix.net/) -###12). How do I get started on my client app? Design Thinking, Stories, Architecture, Keeping it simple
 +### 12). How do I get started on my client app? Design Thinking, Stories, Architecture, Keeping it simple
 If you are using IBM Public Bluemix for this tutorial series, IBM is introducing a significant change on January 2nd, moving from DEA (Droplet Execution Environment) to Diego. The tutorial will run without change in this new environment. There are two things you need to understand with this change: