New Hope. Adding photos and search by number

bibrecognition/bibrecognition/settings.py

@@ -120,3 +120,5 @@ USE_TZ = True
 
 STATIC_URL = '/static/'
 
+MEDIA_ROOT = os.path.join(os.path.dirname(BASE_DIR), "bibrecognition/images")
+MEDIA_URL = '/images/'

bibrecognition/imguploader/forms.py

@@ -7,3 +7,9 @@ class PhotoForm(forms.Form):
         queryset=Competitions.objects.all(),  to_field_name="comp_slug")
     file_field = forms.FileField(
         widget=forms.ClearableFileInput(attrs={'multiple': True}))
+
+
+class SearchForm(forms.Form):
+    zawody = forms.ModelChoiceField(
+        queryset=Competitions.objects.all(),  to_field_name="comp_slug")
+    numer = forms.DecimalField(decimal_places=0)

bibrecognition/imguploader/functions.py

@@ -37,7 +37,7 @@ def decode_predictions(scores, geometry):
 		for x in range(0, numCols):
 			# if our score does not have sufficient probability,
 			# ignore it
-			if scoresData[x] < args["min_confidence"]:
+			if scoresData[x] < 0.5:
 				continue
 
 			# compute the offset factor as our resulting feature
@@ -71,123 +71,65 @@ def decode_predictions(scores, geometry):
 	return (rects, confidences)
 
 
-def findNumber():
+def findNumber(url):
+	image = cv2.imread(url)
+	orig = image.copy()
+	(origH, origW) = image.shape[:2]
+	(newW, newH) = (320,320)
+	rW = origW / float(newW)
+	rH = origH / float(newH)
+	image = cv2.resize(image, (newW, newH))
+	(H, W) = image.shape[:2]
+	layerNames = [
+		"feature_fusion/Conv_7/Sigmoid",
+		"feature_fusion/concat_3"]
+	net = cv2.dnn.readNet("../EAST/frozen_east_text_detection.pb")
+	blob = cv2.dnn.blobFromImage(image, 1.0, (W, H),
+								(123.68, 116.78, 103.94), swapRB=True, crop=False)
+	net.setInput(blob)
+	(scores, geometry) = net.forward(layerNames)
+	(rects, confidences) = decode_predictions(scores, geometry)
+	boxes = non_max_suppression(np.array(rects), probs=confidences)
+	results = []
+	for (startX, startY, endX, endY) in boxes:
+		
+		startX = int(startX * rW)
+		startY = int(startY * rH)
+		endX = int(endX * rW)
+		endY = int(endY * rH)
+		
+		dX = int((endX - startX) * 0.0)
+		dY = int((endY - startY) * 0.0)
+		
+		startX = max(0, startX - dX)
+		startY = max(0, startY - dY)
+		endX = min(origW, endX + (dX * 2))
+		endY = min(origH, endY + (dY * 2))
+		
+		roi = orig[startY:endY, startX:endX]
+		
+		config = ("-l eng --oem 1 --psm 7")
+		text = pytesseract.image_to_string(roi, config=config)
+		
+		results.append(((startX, startY, endX, endY), text))
+		
+	results = sorted(results, key=lambda r: r[0][1])
+	wyniki = []
+	for ((startX, startY, endX, endY), text) in results:
+		if( text.isdigit() ):
+			wyniki.append(text)
+		# print("OCR TEXT")
+		# print("========")
+		# print("{}\n".format(text))
+		
+		# text = "".join([c if ord(c) < 128 else "" for c in text]).strip()
+		# output = orig.copy()
+		# cv2.rectangle(output, (startX, startY), (endX, endY),
+				# (0, 0, 255), 2)
+		# cv2.putText(output, text, (startX, startY - 20),
+				# cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 3)
+				
+		# cv2.imshow("Text Detection", output)
+		# cv2.waitKey(0)
 
-	return 0
-
-# construct the argument parser and parse the arguments
-ap = argparse.ArgumentParser()
-ap.add_argument("-i", "--image", type=str,
-                help="path to input image")
-ap.add_argument("-east", "--east", type=str, default="./EAST/frozen_east_text_detection.pb",
-                help="path to input EAST text detector")
-ap.add_argument("-c", "--min-confidence", type=float, default=0.5,
-                help="minimum probability required to inspect a region")
-ap.add_argument("-w", "--width", type=int, default=320,
-                help="nearest multiple of 32 for resized width")
-ap.add_argument("-e", "--height", type=int, default=320,
-                help="nearest multiple of 32 for resized height")
-ap.add_argument("-p", "--padding", type=float, default=0.0,
-                help="amount of padding to add to each border of ROI")
-args = vars(ap.parse_args())
-
-# load the input image and grab the image dimensions
-image = cv2.imread(args["image"])
-orig = image.copy()
-(origH, origW) = image.shape[:2]
-
-# set the new width and height and then determine the ratio in change
-# for both the width and height
-(newW, newH) = (args["width"], args["height"])
-rW = origW / float(newW)
-rH = origH / float(newH)
-
-# resize the image and grab the new image dimensions
-image = cv2.resize(image, (newW, newH))
-(H, W) = image.shape[:2]
-
-# define the two output layer names for the EAST detector model that
-# we are interested -- the first is the output probabilities and the
-# second can be used to derive the bounding box coordinates of text
-layerNames = [
-	"feature_fusion/Conv_7/Sigmoid",
-	"feature_fusion/concat_3"]
-
-# load the pre-trained EAST text detector
-print("[INFO] loading EAST text detector...")
-net = cv2.dnn.readNet(args["east"])
-
-# construct a blob from the image and then perform a forward pass of
-# the model to obtain the two output layer sets
-blob = cv2.dnn.blobFromImage(image, 1.0, (W, H),
-                             (123.68, 116.78, 103.94), swapRB=True, crop=False)
-net.setInput(blob)
-(scores, geometry) = net.forward(layerNames)
-
-# decode the predictions, then  apply non-maxima suppression to
-# suppress weak, overlapping bounding boxes
-(rects, confidences) = decode_predictions(scores, geometry)
-boxes = non_max_suppression(np.array(rects), probs=confidences)
-
-# initialize the list of results
-results = []
-
-# loop over the bounding boxes
-for (startX, startY, endX, endY) in boxes:
-	# scale the bounding box coordinates based on the respective
-	# ratios
-	startX = int(startX * rW)
-	startY = int(startY * rH)
-	endX = int(endX * rW)
-	endY = int(endY * rH)
-
-	# in order to obtain a better OCR of the text we can potentially
-	# apply a bit of padding surrounding the bounding box -- here we
-	# are computing the deltas in both the x and y directions
-	dX = int((endX - startX) * args["padding"])
-	dY = int((endY - startY) * args["padding"])
-
-	# apply padding to each side of the bounding box, respectively
-	startX = max(0, startX - dX)
-	startY = max(0, startY - dY)
-	endX = min(origW, endX + (dX * 2))
-	endY = min(origH, endY + (dY * 2))
-
-	# extract the actual padded ROI
-	roi = orig[startY:endY, startX:endX]
-
-	# in order to apply Tesseract v4 to OCR text we must supply
-	# (1) a language, (2) an OEM flag of 4, indicating that the we
-	# wish to use the LSTM neural net model for OCR, and finally
-	# (3) an OEM value, in this case, 7 which implies that we are
-	# treating the ROI as a single line of text
-	config = ("-l eng --oem 1 --psm 7")
-	text = pytesseract.image_to_string(roi, config=config)
-
-	# add the bounding box coordinates and OCR'd text to the list
-	# of results
-	results.append(((startX, startY, endX, endY), text))
-
-# sort the results bounding box coordinates from top to bottom
-results = sorted(results, key=lambda r: r[0][1])
-
-# loop over the results
-for ((startX, startY, endX, endY), text) in results:
-	# display the text OCR'd by Tesseract
-	print("OCR TEXT")
-	print("========")
-	print("{}\n".format(text))
-
-	# strip out non-ASCII text so we can draw the text on the image
-	# using OpenCV, then draw the text and a bounding box surrounding
-	# the text region of the input image
-	text = "".join([c if ord(c) < 128 else "" for c in text]).strip()
-	output = orig.copy()
-	cv2.rectangle(output, (startX, startY), (endX, endY),
-               (0, 0, 255), 2)
-	cv2.putText(output, text, (startX, startY - 20),
-             cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 3)
-
-	# show the output image
-	cv2.imshow("Text Detection", output)
-	cv2.waitKey(0)
+	return wyniki

bibrecognition/imguploader/migrations/0004_remove_photo_url.py

@@ -0,0 +1,17 @@
+# Generated by Django 3.0.3 on 2020-06-19 23:49
+
+from django.db import migrations
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('imguploader', '0003_competitions_status'),
+    ]
+
+    operations = [
+        migrations.RemoveField(
+            model_name='photo',
+            name='url',
+        ),
+    ]

bibrecognition/imguploader/migrations/0005_photometa_comp_id.py

@@ -0,0 +1,19 @@
+# Generated by Django 3.0.3 on 2020-06-20 00:27
+
+from django.db import migrations, models
+import django.db.models.deletion
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('imguploader', '0004_remove_photo_url'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='photometa',
+            name='comp_id',
+            field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='imguploader.Competitions'),
+        ),
+    ]

bibrecognition/imguploader/models.py

@@ -1,8 +1,8 @@
 from django.db import models
 
 class PhotoManager(models.Manager):
-    def create_photo(self, comp_id, name, image, url):
-        photo = self.create(comp_id = comp_id, name = name, image = image, url = url)
+    def create_photo(self, comp_id, name, image):
+        photo = self.create(comp_id = comp_id, name = name, image = image)
 
         return photo
 # Create your models here.
@@ -18,10 +18,15 @@ class Photo(models.Model):
     comp_id = models.ForeignKey(Competitions, on_delete=models.CASCADE)
     name = models.CharField(max_length=100, default='Zdjecie')
     image = models.ImageField(upload_to='images/', default='placeholder.jpg')
-    url = models.CharField(max_length=50)
+    # url = models.CharField(max_length=50)
     objects = PhotoManager()
 
+    def __str__(self):
+        return self.name
+
+
 class PhotoMeta(models.Model):
+    comp_id = models.ForeignKey(Competitions, on_delete=models.CASCADE, null=True)
     photo_id = models.ForeignKey(Photo, on_delete=models.CASCADE)
     meta_key = models.CharField(max_length=50)
     meta_value = models.CharField(max_length=50)

bibrecognition/imguploader/templates/index.html

@@ -7,9 +7,12 @@
 </head>
 <body>
     {% if user.is_authenticated %}
-        Zalogowany 😎
+        Zalogowany 😎 <br />
+        <a href="{% url 'upload' %}">Załaduj zdjęcia</a><br />
+        <a href="{% url 'search' %}">Przeszukaj bazę</a>
     {% else %}
-        Gość 🏃‍♀️
+        Gość 🏃‍♀️<br />
+        <a href="{% url 'search' %}">Przeszukaj bazę</a>
     {% endif %}
 </body>
 </html>

bibrecognition/imguploader/templates/search.html

@@ -0,0 +1,27 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Search Photos</title>
+</head>
+
+<body>
+    {% if form %}
+    <form action="/search" method="post" >
+        {% csrf_token %}
+        {{ form }}
+        <input type="submit" value="Submit">
+    </form>
+    {% endif %}
+    {% if foto %}
+    <ul>
+    {% for n in foto %}
+    <li><img src="{{ n }}" /></li>
+    {% endfor %}
+    </ul>
+    {% endif %}
+</body>
+
+</html>

bibrecognition/imguploader/urls.py

@@ -1,8 +1,16 @@
 from django.urls import path
+from django.conf.urls.static import static
+from django.conf import settings
 
 from . import views
 
 urlpatterns = [
     path('', views.index, name="index"),
     path('upload', views.uploadPhotos, name="upload"),
+    path('search', views.searchPhotos, name="search"),
+    
+
+
 ]
+
+urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

bibrecognition/imguploader/views.py

@@ -1,13 +1,15 @@
 from django.shortcuts import render
 from django.http import HttpResponse
 from .forms import PhotoForm
+from .forms import SearchForm
 from django.http import HttpResponseRedirect
 
 from .models import PhotoManager
 from .models import Photo
 from .models import Competitions
+from .models import PhotoMeta
 
-# from .functions import test
+from .functions import findNumber
 
 
 # Create your views here.
@@ -25,15 +27,61 @@ def uploadPhotos(request):
             for f in files:
                 zawody = Competitions.objects.get(comp_slug=comp)
                 
-                photo = Photo.objects.create_photo(zawody,f,f,'test')
+                # photo = Photo.objects.create_photo(zawody,comp+"_"+f,f)
+                file_name = comp+"_"+f.name
+                photo = Photo(comp_id=zawody, name=file_name, image=f)
+                photo.save(force_insert=True)
+                # print("URL of photo: "+photo.image.url)
+                numbers = findNumber(photo.image.url)
+                
+                for nr in numbers:
+                    pm = PhotoMeta(comp_id=zawody, photo_id=photo, meta_key="detect_number", meta_value=nr)
+                    pm.save(force_insert=True)
             # return self.form_valid(form)
-            return HttpResponseRedirect('/success/url/')
+            return HttpResponseRedirect('/success/')
         else:
             # return self.form_invalid(form)
             # form.save()
             # return render(request, print(request.FILES['file_field']))
-            return HttpResponseRedirect('/faild/url/')
+            return HttpResponseRedirect('/failed/')
     else:
         form = PhotoForm()
     return render(request, 'upload.html', {'form': form})
     # return HttpResponse("Hello, world. This is imageUploader")
+
+
+def searchPhotos(request):
+    if request.method == 'POST':
+        form = SearchForm(request.POST)
+        comp = request.POST['zawody']
+        numer = request.POST['numer']
+        print(request)
+        
+        if form.is_valid():
+            allFotos = []
+            imgUrls = []
+            zawody = Competitions.objects.get(comp_slug=comp)
+            try:
+                zdjecia = PhotoMeta.objects.filter(comp_id=zawody, meta_value=numer)
+            except PhotoMeta.DoesNotExist:
+                zdjecia = None
+            if( zdjecia ):
+                for zdjecie in zdjecia:
+                    # allFotos.append(Photo.objects.get(id=zdjecie.photo_id))
+                    imgUrls.append(zdjecie.photo_id.image.name)
+
+                # for fotos in allFotos:
+                #     imgUrls.append(fotos.image.url)
+
+                return render(request, 'search.html', {'foto': imgUrls})
+            else:
+                print('no ni ma')
+                
+            return HttpResponseRedirect('/success/')
+        else:
+            
+            return HttpResponseRedirect('/failed/')
+    else:
+        form = SearchForm()
+    return render(request, 'search.html', {'form': form})
+    # return HttpResponse("Hello, world. This is imageUploader")

main.py

@@ -169,12 +169,14 @@ for (startX, startY, endX, endY) in boxes:
 results = sorted(results, key=lambda r: r[0][1])
 
 # loop over the results
+
 for ((startX, startY, endX, endY), text) in results:
 	# display the text OCR'd by Tesseract
 	print("OCR TEXT")
 	print("========")
 	print("{}\n".format(text))
 
+
 	# strip out non-ASCII text so we can draw the text on the image
 	# using OpenCV, then draw the text and a bounding box surrounding
 	# the text region of the input image
@@ -188,3 +190,4 @@ for ((startX, startY, endX, endY), text) in results:
 	# show the output image
 	cv2.imshow("Text Detection", output)
 	cv2.waitKey(0)
+