def api(request):
log = Log()
if request.method != 'POST':
log.status = 'post error'
log.save()
raise Http404
try:
formdata = request.FILES['file']
filename = formdata.name
filesize = formdata.size
except Exception as err:
log.message = err
log.status = 'formdata error'
log.save()
return server_error(request)
log.filename = filename
log.filesize = filesize
log.save()
if filesize > 10000000:
log.status = 'filesize error'
log.save()
return server_error(request)
try:
filedata = formdata.open().read()
except Exception as err:
log.message = err
log.status = 'filedata error'
log.save()
return server_error(request)
ext = imghdr.what(None, h=filedata)
if ext not in ['jpeg', 'png']:
log.message = ext
log.status = 'filetype error'
log.save()
return server_error(request)
try:
s3_key = save_image(filedata, filename)
except Exception as err:
log.message = err
log.status = 's3 error'
log.save()
return server_error(request)
log.s3_key = s3_key
log.save()
image = np.fromstring(filedata, np.uint8)
image = cv2.imdecode(image, 1)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
face_cascade = cv2.CascadeClassifier(
os.path.join(os.path.dirname(__file__),
'haarcascade_frontalface_default.xml'))
faces = face_cascade.detectMultiScale(gray,
scaleFactor=1.3,
minNeighbors=5)
if type(faces) != np.ndarray:
log.status = 'faces error'
log.save()
return server_error(request)
face_list = []
for (x, y, w, h) in faces:
face = image[y:y + h, x:x + w]
face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
face = cv2.resize(face, (IMG_ROWS, IMG_COLS))
face = np.array(face, dtype=np.float32) / 255.0
face = np.ravel(face)
face_list.append(face)
try:
percent_list = predict(face_list, dtype='int')
except Exception as err:
log.message = err
log.status = 'predict error'
log.save()
return server_error(request)
predict_list = []
for (x, y, w, h), percent in zip(faces, percent_list):
max_index = np.argmax(percent)
max_value = np.amax(percent)
if max_index == 0:
color = (177, 107, 1)
elif max_index == 1:
color = (15, 30, 236)
else:
color = (0, 0, 0)
text = '{} {}%'.format(CLASSES[max_index], max_value)
image = write_text(image, text, (x, y + h + 10), color, int(h / 10))
cv2.rectangle(image, (x, y), (x + w, y + h), color, thickness=2)
predict_list.append(text)
log.message = ','.join(predict_list)
image = cv2.imencode('.jpeg', image)[1].tostring()
response = 'data:image/jpeg;base64,' + base64.b64encode(image).decode(
'utf-8')
log.status = 'success'
log.save()
return HttpResponse(response, status=200)
