def new_image():
form = ImageForm()
if form.validate_on_submit():
# save original images
rand_name = urandom(4).hex() + form.image_file.data.filename
print("is htis hte name /?",rand_name)
real_name = ntpath.basename(image_uploadset.save(form.image_file.data, folder='originals', name=rand_name))
original_path = './images/originals/'+real_name
image_cv = cv2.imread(original_path)
# Face detection
## need to tell the location of the classifier manually!!
#face_cascade = cv2.CascadeClassifier('C:/Users/mihir/PycharmProjects/A1_ECE1779/venv/Lib/site-packages/cv2/data/haarcascade_frontalface_default.xml')
#face_cascade = cv2.CascadeClassifier('/Users/ragnoletto/Documents/School/UofT/ECE1779/assignments/A1_ECE1779/venv/lib/python3.7/site-packages/cv2/data/haarcascade_frontalface_default.xml')
#face_cascade = cv2.CascadeClassifier('/Users/bibinsebastian/Dropbox/UofT/ECE1779/A2_ECE1779/venv/lib/python3.6/site-packages/cv2/data/haarcascade_frontalface_default.xml')
face_cascade = cv2.CascadeClassifier('/home/ubuntu/Desktop/ece1779/A2_ECE1779/venv/lib/python3.7/site-packages/cv2/data/haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(image_cv, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
face_cv = image_cv.copy() #this is neeeded since it will throw error if there is no image in the picture
for (x,y,w,h) in faces:
face_cv = cv2.rectangle(image_cv, (x,y) , (x + w,y + h), (0,0,255),8)
roi_gray = gray[y:y+h, x:x+w]
roi_color = face_cv[y:y+h, x:x+w]
picture_path = './images/faces/'+real_name
cv2.imwrite(picture_path, face_cv)
cv2.waitKey(0)
try:
image = Image(user_id=current_user.id, file_name=real_name, num_faces = len(faces))
db.session.add(image)
db.session.commit()
flash('File Uploaded!')
s3.upload_file(original_path,webapp.config["S3_BUCKET"],real_name)
s3.upload_file(picture_path,webapp.config["S3_BUCKET"],'f_'+real_name)
os.remove(original_path)
os.remove(picture_path)
except Exception as error:
db.session.rollback()
abort(500, error)
return render_template("images/new.html", title="Upload an Image", form=form)
def imagem():
form = ImageForm()
if form.validate_on_submit():
file = form.image.data
user_image = UserImage(user=current_user, image=file)
db.session.add(user_image)
db.session.commit()
flash('Imagem base salva!')
return redirect(url_for('imagem'))
images = UserImage.query.filter_by(user=current_user, parent=None)
return render_template('imagem.html',
title='imagem',
form=form,
user=current_user,
images=images)
def file_form(request):
if request.method == 'GET':
context = {
'form': ImageForm
}
return render(request, 'file_form.html', context)
else:
form = ImageForm(request.POST, request.FILES)
if form.is_valid():
image = form.save()
image.save()
return redirect('home')
context = {
'form': form
}
return render(request, 'file_form.html', context)
def image_veiw(request):
form = ImageForm()
if request.method == 'POST':
form = ImageForm(request.POST)
if form.is_valid():
form.save()
return HttpResponse('success')
return render(request, 'test.html', {'form': form})
def image_veiw(request):
form = ImageForm()
if request.method=='POST':
# form = ImageForm(request.POST)
# if form.is_valid():
# form.save()
name=request.POST.get('Iname')
name=request.POST.get('Ilocation')
name=request.POST.get('Iname')
return HttpResponse('success')
def add_image(board_slug):
print 'add_image: ', board_slug
board = Board.query.filter_by(slug=board_slug).first()
if not board:
abort(404)
form = ImageForm()
if form.validate_on_submit():
image = Image(filename=form.filename.data)
db.session.add(image)
board.images.append(image)
db.session.commit()
return json_success(
{'image': {
'filename': image.filename,
'id': image.id
}})
return json_error_message('Failed to create image',
error_data=form.errors)
def add_image(board_slug):
print 'add_image: ', board_slug
board = Board.query.filter_by(slug=board_slug).first()
if not board:
abort(404)
form = ImageForm()
if form.validate_on_submit():
image = Image(filename=form.filename.data)
db.session.add(image)
board.images.append(image)
db.session.commit()
return json_success({
'image': {
'filename': image.filename,
'id': image.id
}
})
return json_error_message('Failed to create image',
error_data=form.errors)
def index():
form = ImageForm()
if form.validate_on_submit():
w = form.w.data
h = form.h.data
file = form.img.data
if allowed_file(file.filename):
file_extension = os.path.splitext(file.filename)[1]
filename = str(uuid.uuid4()) + file_extension
file.save(os.path.join(UPLOAD_FOLDER, filename))
flash('File successfully uploaded')
new_record = ImageRequest(w=w, h=h, img_path=filename)
db.session.add(new_record)
db.session.commit()
change_img_size.apply_async(args=[filename, w, h, new_record.id])
flash('Your task id is ' + str(new_record.id))
return render_template('index.html', form=form), 202
else:
flash('Allowed file types are png, jpg')
return redirect(request.url), 301
return render_template('index.html', form=form), 200
def upload_image():
pid,pnames = [t.pid for t in Patients.query.filter_by(therapist_id=current_user.id).all()], [str(t.first_name) + " " + str(t.last_name) for t in Patients.query.filter_by(therapist_id=current_user.id).all()]
imtypes_id,imtype_names = [t.id for t in ImageTypes.query.all()], [t.name for t in ImageTypes.query.all()]
form = ImageForm()
form.patient_id.choices = list(zip(pid, pnames))
form.im_type.choices = list(zip(imtypes_id, imtype_names))
if form.validate_on_submit():
filename = photos.save(form.photo.data, name=f"{form.patient_id.data}_{form.datetime.data}_{current_user.id}.")
file_url = photos.url(filename)
img = Images(patient_id=form.patient_id.data, datetime=form.datetime.data, im_type=form.im_type.data, image=filename)
db.session.add(img)
db.session.commit()
flash("New image successfully added")
if form.analyze.data:
#send POST request to ml module
res = requests.post(ML_URL, json={'impath':filename})
if res.status_code == 200:
result = json.loads(res.text)
diagnosis = 1 if result["tumor_detected"]==False else 2
img_anal = ImageAnalysis(image_id=img.image_id, segment=result["segmentation_img"], tumor=result["classification"], diagnosis=diagnosis, recommendations=None, confidence=result["confidence"], dt=datetime.now(), verified=False)
db.session.add(img_anal)
db.session.commit()
#flash(img_anal)
flash("Successfully analyzed new image")
else:
flash("Error when analyzing")
else:
file_url = None
return render_template("upload_image.html", form=form, file_url=file_url)
def patient_dashboard():
if "email" not in session:
flash("You must log in first")
return redirect(url_for("login"))
form = ImageForm()
if form.validate_on_submit():
f_name = save_picture(form.picture.data)
covid_prediction = model_predict(
os.path.join(app.root_path, 'static\\assets\\img\\xray', f_name), )
if covid_prediction is True:
email = session["email"]
cursor = mysql.connection.cursor(MySQLdb.cursors.DictCursor)
cursor.execute(
'UPDATE DETAILS SET xraystatus = "Threatened", status = "Threatened", disease = "Covid-19" WHERE email = %s',
(email, ))
mysql.connection.commit()
flash('You have high chances of Covid-19, please see a doctor.')
else:
flash('Congratulations! You have low chances of Covid-19.')
return render_template('patient-dashboard.html',
form=form,
title="Patient Dashboard")
def index():
form = ImageForm()
if form.validate_on_submit():
# Remove cached image files
file_path = os.path.join('uploads', 'uploaded_image.jpg')
if os.path.exists(file_path):
os.remove(file_path)
filename = photos.save(form.upload.data, name="uploaded_image.jpg")
file_url = photos.url(filename) + f"?{time.time()}"
caption = get_caption_and_attention_plot('uploads/' + filename)
if os.path.exists(os.path.join("uploads", "attention_plot.jpg")):
attention_path = photos.url(
"attention_plot.jpg") + f"?{time.time()}"
else:
attention_path = None
else:
file_url = None
caption = None
attention_path = None
return render_template('index.html',
form=form,
file_url=file_url,
caption=caption,
attention=attention_path)
def upload_view(request):
if request.method == 'POST':
form = ImageForm(request.POST or None, request.FILES or None)
if form.is_valid():
if form.cleaned_data['link']:
image = ImageModel.objects.create()
link_path = urlparse(form.cleaned_data['link'])
name = os.path.basename(link_path.path)
temp_file = tempfile.NamedTemporaryFile()
temp_file.write(
requests.get(form.cleaned_data['link'],
stream=True).content)
image.file.save(name, temp_file)
elif form.cleaned_data['file']:
form.save()
return redirect('index')
elif request.method == 'GET':
form = ImageForm()
return render(request, 'upload.html', {'form': form})
def new_image():
form = ImageForm()
if form.validate_on_submit():
# save original images
rand_name = urandom(4).hex() + form.image_file.data.filename
real_name = ntpath.basename(
image_uploadset.save(form.image_file.data, name=rand_name))
original_path = '/tmp/' + real_name
watermark_name = '/tmp/' + 'w_' + real_name
#Adding watermark . need to store the watermark image in s3 and read from there.
with Image(filename=original_path) as background:
with Image(filename='cpics.png') as watermark:
background.watermark(image=watermark,
transparency=0.50,
left=randint(1, 100),
top=randint(1, 100))
background.save(filename=watermark_name)
try:
s3.upload_file(original_path, webapp.config["S3_BUCKET"],
real_name)
s3.upload_file(watermark_name, webapp.config["S3_BUCKET_WATER"],
'w_' + real_name)
os.remove(original_path)
os.remove(watermark_name)
table_user = dynamodb.Table(webapp.config["DDB_USER_TBL_NAME"])
table_attrib = dynamodb.Table(webapp.config["DDB_ATTRIB_TBL_NAME"])
# rekognition on images
rekognition = boto3.client('rekognition')
response_label = rekognition.detect_labels(Image={
"S3Object": {
"Bucket": webapp.config["S3_BUCKET"],
"Name": real_name
}
},
MaxLabels=10,
MinConfidence=70)
response_faces = rekognition.recognize_celebrities(
Image={
"S3Object": {
"Bucket": webapp.config["S3_BUCKET"],
"Name": real_name
}
})
#print("rekognition response", response)
# labels = [{'Confidence': Decimal(str(round(label_prediction['Confidence'], 1))),
# 'Name': label_prediction['Name']} for label_prediction in response['Labels']]
labels = [{
'Confidence': int(label_prediction['Confidence']),
'Name': label_prediction['Name'].lower()
} for label_prediction in response_label['Labels']]
if response_faces['CelebrityFaces'] != []:
labels.extend([{
'Confidence':
int(face_prediction['MatchConfidence']),
'Name':
face_prediction['Name'].lower()
} for face_prediction in response_faces['CelebrityFaces']])
#print("labels:::::", labels)
# update table
response_user = table_user.get_item(Key={
'username': current_user.id,
})
#print("response 1 ",response1)
response_attrib = table_attrib.scan()
records = []
for i in response_attrib['Items']:
records.append(i)
while 'LastEvaluatedKey' in response_attrib:
response_attrib = table.scan(
IndexName=indexName,
ExclusiveStartKey=response['LastEvaluatedKey'])
for i in response_attrib['Items']:
records.append(i)
#print("response_attrib",response_attrib)
attrib_list = {}
for item in records:
attrib_list[(item['attribute'])] = int(item['attrib_counter'])
for label in labels:
if label['Name'] in attrib_list.keys():
attrib_list[label['Name']] += int(1)
table_attrib.update_item(
Key={
'attribute': label['Name']
#,
# 'attrib_counter': attrib_list[label['Name']]
},
UpdateExpression=
"SET attrib_counter = :val, im_path = list_append(im_path, :val2)",
ExpressionAttributeValues={
':val':
attrib_list[label['Name']],
# ':val2': [real_name] },
':val2': [{
'username': current_user.id,
'email': response_user['Item']['email'],
'path': real_name,
'Confidence': label['Confidence'],
}]
},
ReturnValues="UPDATED_NEW")
else:
putItem_Attrib(label['Name'], int(1),
[{
'username': current_user.id,
'email': response_user['Item']['email'],
'path': real_name,
'Confidence': label['Confidence']
}])
# for label in labels:
# if label['Name'] in attrib_list:
# attrib_list[label['Name']] += int(1)
# else:
# attrib_list[label['Name']] = int(1)
# print("attrib list",attrib_list)
table_user.update_item(
Key={
'username': current_user.id,
},
UpdateExpression="SET images = list_append(images, :val)",
ExpressionAttributeValues={
':val': [{
'path': real_name,
'labels': labels,
}],
},
ReturnValues="UPDATED_NEW")
# table.update_item(
# Key={
# 'username': current_user.id,
# },
# UpdateExpression="SET images = list_append(images, :val), attrib = :val2",
# ExpressionAttributeValues={
# ':val': [{
# 'path': real_name,
# 'labels': labels,
# }],
# ':val2': attrib_list,
# },
# ReturnValues="UPDATED_NEW"
# )
#print("labels",labels)
flash('File Uploaded!')
except Exception as error:
# db.session.rollback()
abort(500, error)
return render_template("images/new.html",
title="Upload an Image",
form=form)