Added custom image evaluation scripts, Fixed PATHS

baljit92 · Jul 21, 2017 · eff0edc · eff0edc
1 parent c6cdbac
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diff --git a/README.md b/README.md
@@ -108,9 +108,14 @@ To setup TensorBox and evaluate the model, follow instructions below:
 	cd tensorbox
 	python evaluate.py --weights data/save.ckpt-18000 --test_boxes data/testing_set.json
 
-
 The evaluated image results are saved under `data/images_testing_set_18000`. 
 
+
+To evaluate a custom image by uploading it from the desktop:
+	mv web_upload_eval.py ./tensorbox
+
+Go to the browser and type in `localhost:8000/draw/modelTest` 
+
 To use a custom testing dataset, add the image files to `data/images` and replace the testing_set.json with the custom test dataset. The custom dataset should have image_path field as `/images/<image_name>` and the rects field will contain the true bounding box coordinates.
 
 Below is a solution for a potential error that might come up during TensorBox installation. The commands should be executed inside the `virtualenv`:

diff --git a/src/custom_scripts/convertToRGB.py b/src/custom_scripts/convertToRGB.py
@@ -0,0 +1,23 @@
+from PIL import Image
+import csv
+import os
+import sys, getopt
+
+
+def main(argv):
+
+	if len(argv) < 1:
+		print 'python convertToRGB.py <image_file_path>'
+		sys.exit(2)
+	else:
+		try:
+			file_path, file_name = os.path.split(argv[0])
+			im = Image.open(argv[0])
+			rgb_im = im.convert('RGB')
+			rgb_im.save(file_path+"/"+file_name.split(".")[0]+".jpeg")
+		except IOError:
+			print "Please enter a valid image"
+			sys.exit(2)
+
+if __name__ == "__main__":
+	main(sys.argv[1:])
diff --git a/src/webapp/TrainImage_Annotate/Annotate/__init__.pyc b/src/webapp/TrainImage_Annotate/Annotate/__init__.pyc
diff --git a/src/webapp/TrainImage_Annotate/Annotate/admin.pyc b/src/webapp/TrainImage_Annotate/Annotate/admin.pyc
diff --git a/src/webapp/TrainImage_Annotate/Annotate/migrations/__init__.pyc b/src/webapp/TrainImage_Annotate/Annotate/migrations/__init__.pyc
diff --git a/src/webapp/TrainImage_Annotate/Annotate/templates/download.html b/src/webapp/TrainImage_Annotate/Annotate/templates/download.html
@@ -9,17 +9,114 @@
 
 	<script src="https://code.jquery.com/jquery-2.1.1.min.js"></script>
 	<script src="/static/js/script.js"></script>
-
+	<link rel="stylesheet" href="/static/css/style.css">
+	<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css">
+	<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
+	<link href="https://fonts.googleapis.com/icon?family=Material+Icons"
+      rel="stylesheet">
 </head>
 
-<!-- initialize the canvas with the image on body load -->
-<body >
-
-<div>
-	 <p> Hello </p>
-
-</div>
+<body>
+	<div class="container">
+		<div class="row">
+			<div class="col-sm-6 col-sm-offset-4">
+				<h2> Skull Detection Tool</h2>
+				<div>
+				 	<div id="uploaderForm">
+						<form enctype="multipart/form-data" action="{% url 'evalImg' %}" method="post">
+							<input type="file" id="upload_img" name="pic" accept="image/*" capture style="display:none"/>
+
+							<div class="row">
+								<div class="col-sm-6 col-sm-offset-2">
+									<i id="upfile1" class="material-icons" style="cursor:pointer; 
+									color:rgba(128, 128, 128, 0.77); font-size: 65px">cloud_upload</i>
+								</div>
+							</div>
+						</form>
+
+						<div class="row">
+							<div class="col-sm-6 col-sm-offset-0" >
 
+							<img id="blah" alt="your image"  style="display: inline; padding: 10px; border: 3px solid rgba(128,128,128,0.2);">
+							<br>
+							<br>
+							<div class="row">
+								<div class="col-sm-6 col-sm-offset-0" style="margin-left:105px">
+									<div id="loading"><img src="/static/img/ring-alt.gif" width="50" height="50"/></div>
+								</div>
+							</div>
 
+							<div class="row">
+								<div class="col-sm-6 col-sm-offset-4">
+									<div class="uploaderItem" id="uploaderSubmit">
+										<a href="#" onclick="addImage();">Process Image</a></div>
+									</div>
+								</div>
+							</div>
+						</div>
+					</div>
+				</div>
+			</div>
+		</div>
+	</div>
 </body>
+
+<script>
+
+var myJSVar = "{{ evalBool|safe }}";
+
+if(myJSVar == "False")
+{
+	$('#blah').css( "max-width", '300px' );
+ 	$('#blah').css( "max-height", '300px' );
+	$("#blah").hide();
+	$("#blah_title").hide();
+	$("#uploaderSubmit").hide();
+	$("#loading").hide();
+}
+else
+{
+	$("#blah").show();
+ 	$('#blah').css( "max-width", '300px' );
+ 	$('#blah').css( "max-height", '300px' );
+	$('#blah').attr('src', '/static/media_1/imageTest.jpeg');
+	$("#uploaderSubmit").hide();
+	$("#loading").hide();
+
+}
+
+function readURL(input) {
+
+	if (input.files && input.files[0]) {
+		var reader = new FileReader();
+
+		reader.onload = function (e) {
+			$('#blah').attr('src', e.target.result);
+			$("#blah").show()
+			$("#blah_title").show();
+			$("#uploaderSubmit").show();
+		}
+
+		reader.readAsDataURL(input.files[0]);
+	}
+}
+
+$("#upload_img").change(function(){
+	readURL(this);
+});
+
+function addImage()
+{   
+	$("#blah").hide();
+	$("#loading").show();
+	$("#uploaderSubmit").hide();
+	$('#uploaderForm > form').submit()
+
+}
+
+$("#upfile1").click(function () {
+    $("#upload_img").trigger('click');
+});
+
+</script>
 </html>
diff --git a/src/webapp/TrainImage_Annotate/Annotate/urls.py b/src/webapp/TrainImage_Annotate/Annotate/urls.py
@@ -10,4 +10,6 @@
 	url(r'^download/$', views.downloadImgs, name='downloadAnnotImages'),
 	url(r'^downloadNMS/$', views.downloadImgsNMS, name='downloadAnnotImages'),
 	url(r'^downloadall/$', views.downloadAll, name='downloadAllImages'),
+	url(r'^modelTest/$', views.modelTest, name='modelTest'),
+	url(r'^imageEval/$', views.imgEval, name='evalImg'),
 ]
diff --git a/src/webapp/TrainImage_Annotate/Annotate/urls.pyc b/src/webapp/TrainImage_Annotate/Annotate/urls.pyc
diff --git a/src/webapp/TrainImage_Annotate/Annotate/views.py b/src/webapp/TrainImage_Annotate/Annotate/views.py
@@ -4,13 +4,23 @@
 import re
 import csv
 import os
+import sys 
 import zipfile
 import StringIO
 from PIL import Image, ImageDraw
 import numpy as np
 import fnmatch
 import math
 
+'''
+The path where the web_upload_eval.py file is located an
+export all the functions from the .py file
+'''
+my_file = os.path.exists(os.path.abspath("../../../tensorbox/"))
+if my_file:
+	sys.path.append(os.path.abspath("../../../tensorbox/"))
+	from web_upload_eval import *
+
 def prepareArrayNMS(filename):
 
 	# prepares the array that is to be passed to the
@@ -228,6 +238,51 @@ def non_max_suppression_slow(boxes, overlapThresh, areas=None):
 	return boxes[pick].astype("int"), pick
 
 
+@csrf_exempt
+def modelTest(request):
+	return render(request, 'download.html', {'evalBool':False})
+
+@csrf_exempt
+def imgEval(request):
+
+	'''
+
+	check if the form request made is a POST request; if it is POST
+	request, get the uploaded image data and the image file name and 
+	upload it for evaluation with the ML model
+	'''
+	if request.method == 'POST':
+		handle_uploaded_file(request.FILES['pic'], str(request.FILES['pic']))
+		check_main()
+		return render(request, 'download.html', {'evalBool':True})
+	return render(request, 'download.html', {'evalBool':False})
+
+def handle_uploaded_file(file, filename):
+
+	'''
+	The image that is to be evaluated using the model is saved 
+	with the name of 'image_test.jpeg' for easy finding and processing
+	of file by the evaluation file
+	'''
+	# create media_1 folder if it does not exist
+	if not os.path.exists('Annotate/static/media_1/'):
+		os.mkdir('Annotate/static/media_1/')
+ 	# save te uploaded image to media_1 flder
+	with open('Annotate/static/media_1/image_test.jpeg', 'wb+') as destination:
+		for chunk in file.chunks():
+			destination.write(chunk)
+
+	# the path from where the model will take the input image
+	if not os.path.exists('uploadFils/'):
+		os.mkdir('uploadFils/')
+
+	with open('uploadFils/image_test.jpeg', 'wb+') as destination:
+		for chunk in file.chunks():
+			destination.write(chunk)
+
+
+
+
 
 def viewAnnotImgs(request):
 # lists all the training images without Non-Maximum Suppression	

diff --git a/src/webapp/TrainImage_Annotate/Annotate/views.pyc b/src/webapp/TrainImage_Annotate/Annotate/views.pyc
diff --git a/src/webapp/TrainImage_Annotate/TrainImage_Annotate/__init__.pyc b/src/webapp/TrainImage_Annotate/TrainImage_Annotate/__init__.pyc
diff --git a/src/webapp/TrainImage_Annotate/TrainImage_Annotate/settings.pyc b/src/webapp/TrainImage_Annotate/TrainImage_Annotate/settings.pyc
diff --git a/src/webapp/TrainImage_Annotate/TrainImage_Annotate/urls.pyc b/src/webapp/TrainImage_Annotate/TrainImage_Annotate/urls.pyc
diff --git a/src/webapp/TrainImage_Annotate/TrainImage_Annotate/wsgi.pyc b/src/webapp/TrainImage_Annotate/TrainImage_Annotate/wsgi.pyc
diff --git a/web_upload_eval.py b/web_upload_eval.py
@@ -0,0 +1,98 @@
+import tensorflow as tf
+import os
+import json
+import subprocess
+from scipy.misc import imread, imresize
+from scipy import misc
+from train import build_forward
+from utils.annolist import AnnotationLib as al
+from utils.train_utils import add_rectangles, rescale_boxes
+
+import cv2
+import argparse
+
+abs_path_model = os.path.abspath('../../../tensorbox/data')
+
+def get_image_dir():
+    weights_iteration = int(os.path.abspath(abs_path_model+abs_path_model+'/save.ckpt-18000').split('-')[-1])
+    expname = '_'
+    image_dir = '%s/images_%s_%d%s' % (os.path.dirname(abs_path_model+'/save.ckpt-18000'), os.path.basename(abs_path_model+'/testing_set_present.json')[:-5], weights_iteration, expname)
+    return image_dir
+
+def get_results(H):
+    tf.reset_default_graph()
+    x_in = tf.placeholder(tf.float32, name='x_in', shape=[H['image_height'], H['image_width'], 3])
+    if H['use_rezoom']:
+        pred_boxes, pred_logits, pred_confidences, pred_confs_deltas, pred_boxes_deltas = build_forward(H, tf.expand_dims(x_in, 0), 'test', reuse=None)
+        grid_area = H['grid_height'] * H['grid_width']
+        pred_confidences = tf.reshape(tf.nn.softmax(tf.reshape(pred_confs_deltas, [grid_area * H['rnn_len'], 2])), [grid_area, H['rnn_len'], 2])
+        if H['reregress']:
+            pred_boxes = pred_boxes + pred_boxes_deltas
+    else:
+        pred_boxes, pred_logits, pred_confidences = build_forward(H, tf.expand_dims(x_in, 0), 'test', reuse=None)
+    saver = tf.train.Saver()
+    with tf.Session() as sess:
+        sess.run(tf.global_variables_initializer())
+        saver.restore(sess, abs_path_model+'/save.ckpt-18000')
+
+        pred_annolist = al.AnnoList()
+
+        true_annolist = al.parse(abs_path_model+'/testing_set_present.json')
+        data_dir = os.path.dirname(abs_path_model+'/testing_set_present.json')
+        image_dir = get_image_dir()
+        subprocess.call('mkdir -p %s' % image_dir, shell=True)
+        for i in range(len(true_annolist)):
+            true_anno = true_annolist[i]
+            orig_img = imread('../../../src/webapp/TrainImage_Annotate/uploadFils/image_test.jpeg')
+            img = imresize(orig_img, (H["image_height"], H["image_width"]), interp='cubic')
+            feed = {x_in: img}
+            (np_pred_boxes, np_pred_confidences) = sess.run([pred_boxes, pred_confidences], feed_dict=feed)
+            pred_anno = al.Annotation()
+            pred_anno.imageName = true_anno.imageName
+            new_img, rects = add_rectangles(H, [img], np_pred_confidences, np_pred_boxes,
+                                            use_stitching=True, rnn_len=H['rnn_len'], min_conf=0.2, tau=0.25, show_suppressed=False)
+
+            pred_anno.rects = rects
+            pred_anno.imagePath = os.path.abspath(data_dir)
+            pred_anno = rescale_boxes((H["image_height"], H["image_width"]), pred_anno, orig_img.shape[0], orig_img.shape[1])
+            pred_annolist.append(pred_anno)
+
+            imname = '../../../src/webapp/TrainImage_Annotate/Annotate/static/media_1/imageTest.jpeg'
+            misc.imsave(imname, new_img)
+            if i % 25 == 0:
+                print(i)
+    return pred_annolist, true_annolist
+
+def check_main():
+
+
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(0)
+    hypes_file = abs_path_model+'/hypes.json'
+
+    with open(hypes_file, 'r') as f:
+        H = json.load(f)
+    expname = '_'
+    pred_boxes = '%s.%s%s' % (abs_path_model+'/save.ckpt-18000', expname, os.path.basename(abs_path_model+'/testing_set_present.json'))
+    true_boxes = '%s.gt_%s%s' % (abs_path_model+'/save.ckpt-18000', expname, os.path.basename(abs_path_model+'/testing_set_present.json'))
+
+
+    pred_annolist, true_annolist = get_results(H)
+    pred_annolist.save(pred_boxes)
+    true_annolist.save(true_boxes)
+
+    try:
+        rpc_cmd = './utils/annolist/doRPC.py --minOverlap %f %s %s' % (0.5, true_boxes, pred_boxes)
+        print('$ %s' % rpc_cmd)
+        rpc_output = subprocess.check_output(rpc_cmd, shell=True)
+        print(rpc_output)
+        txt_file = [line for line in rpc_output.split('\n') if line.strip()][-1]
+        output_png = '%s/results.png' % get_image_dir()
+        plot_cmd = './utils/annolist/plotSimple.py %s --output %s' % (txt_file, output_png)
+        print('$ %s' % plot_cmd)
+        plot_output = subprocess.check_output(plot_cmd, shell=True)
+        print('output results at: %s' % plot_output)
+    except Exception as e:
+        print(e)
+
+if __name__ == '__main__':
+   check_main()