def classify(request):
if request.method == 'POST' and request.FILES['predict_img']:
predict_img = request.FILES['predict_img']
fs = FileSystemStorage()
filename = fs.save(predict_img.name, predict_img)
uploaded_file_url = fs.url(filename)
# 推論処理
img = cv2.cvtColor(cv2.imread('media/{}'.format(filename)),
cv2.COLOR_BGR2RGB)
x = prepare(img)
y = model.predictor(np.array([x]))
y_proba = F.softmax(y).data
y_pre = np.argmax(y_proba, axis=1)[0]
proba = round(y_proba[0][y_pre] * 100, 2)
# AnimalInfoのDBから必要な情報の取得
animal_info = AnimalInfo.objects.filter(animal_id=y_pre)
# ZooCollectionに情報を保存
current_user = request.user
if not list(
ZooCollection.objects.filter(user_id=current_user.id,
animal_id=y_pre)):
user_info = ZooCollection(user_id=current_user.id, animal_id=y_pre)
user_info.save()
return render(
request, 'zoo_app/classify.html', {
'uploaded_file_url': uploaded_file_url,
'animal_info': animal_info,
'proba': proba
})
return render(request, 'zoo_app/classify.html', {})
def test_prepare(self):
x1 = numpy.random.uniform(0, 255, (320, 240, 3)).astype(numpy.uint8)
x2 = numpy.random.uniform(0, 255, (320, 240)).astype(numpy.uint8)
x3 = numpy.random.uniform(0, 255, (160, 120, 3)).astype(self.dtype)
x4 = numpy.random.uniform(0, 255, (1, 160, 120)).astype(self.dtype)
x5 = numpy.random.uniform(0, 255, (3, 160, 120)).astype(numpy.uint8)
y1 = googlenet.prepare(x1)
assert y1.shape == (3, 224, 224)
assert y1.dtype, self.dtype
y2 = googlenet.prepare(x2)
assert y2.shape == (3, 224, 224)
assert y2.dtype, self.dtype
y3 = googlenet.prepare(x3, size=None)
assert y3.shape == (3, 160, 120)
assert y3.dtype, self.dtype
y4 = googlenet.prepare(x4)
assert y4.shape == (3, 224, 224)
assert y4.dtype, self.dtype
y5 = googlenet.prepare(x5, size=None)
assert y5.shape == (3, 160, 120)
assert y5.dtype, self.dtype
def test_prepare(self):
x1 = numpy.random.uniform(0, 255, (320, 240, 3)).astype(numpy.uint8)
x2 = numpy.random.uniform(0, 255, (320, 240)).astype(numpy.uint8)
x3 = numpy.random.uniform(0, 255, (160, 120, 3)).astype(numpy.float32)
x4 = numpy.random.uniform(0, 255, (1, 160, 120)).astype(numpy.float32)
x5 = numpy.random.uniform(0, 255, (3, 160, 120)).astype(numpy.uint8)
y1 = googlenet.prepare(x1)
self.assertEqual(y1.shape, (3, 224, 224))
self.assertEqual(y1.dtype, numpy.float32)
y2 = googlenet.prepare(x2)
self.assertEqual(y2.shape, (3, 224, 224))
self.assertEqual(y2.dtype, numpy.float32)
y3 = googlenet.prepare(x3, size=None)
self.assertEqual(y3.shape, (3, 160, 120))
self.assertEqual(y3.dtype, numpy.float32)
y4 = googlenet.prepare(x4)
self.assertEqual(y4.shape, (3, 224, 224))
self.assertEqual(y4.dtype, numpy.float32)
y5 = googlenet.prepare(x5, size=None)
self.assertEqual(y5.shape, (3, 160, 120))
self.assertEqual(y5.dtype, numpy.float32)
def test_prepare(self):
x1 = numpy.random.uniform(0, 255, (320, 240, 3)).astype(numpy.uint8)
x2 = numpy.random.uniform(0, 255, (320, 240)).astype(numpy.uint8)
x3 = numpy.random.uniform(0, 255, (160, 120, 3)).astype(self.dtype)
x4 = numpy.random.uniform(0, 255, (1, 160, 120)).astype(self.dtype)
x5 = numpy.random.uniform(0, 255, (3, 160, 120)).astype(numpy.uint8)
y1 = googlenet.prepare(x1)
assert y1.shape == (3, 224, 224)
assert y1.dtype, self.dtype
y2 = googlenet.prepare(x2)
assert y2.shape == (3, 224, 224)
assert y2.dtype, self.dtype
y3 = googlenet.prepare(x3, size=None)
assert y3.shape == (3, 160, 120)
assert y3.dtype, self.dtype
y4 = googlenet.prepare(x4)
assert y4.shape == (3, 224, 224)
assert y4.dtype, self.dtype
y5 = googlenet.prepare(x5, size=None)
assert y5.shape == (3, 160, 120)
assert y5.dtype, self.dtype
def create_datasets(groups=500, imgpath='', savepath=''):
#def create_datasets(groups=500, imgpath='D:\BaiduNetdiskDownload\ILSVRC2012_img_val\\', savepath='E:\ccc\imgroup'):
if not imgpath:
raise ValueError
if not savepath:
raise ValueError
img_lists = [[] for _ in range(groups)]
tmp_lists = [[] for _ in range(groups)]
for index, filename in enumerate(glob.glob(imgpath + '*.JPEG')):
img_lists[index//(int(50000/groups))].append(filename)
# print(numpy.array(img_lists).shape) -> (500,100)
for index, namelists in enumerate(img_lists[0:10]):
for jpgnames in namelists:
im = Image.open(jpgnames)
tmp_lists[index].append(prepare(im, size=(256,256)).tolist())
im.close()
numpy.save(savepath + str(index), numpy.array(tmp_lists[index], dtype=numpy.float32))
tmp_lists[index] = []