def __getitem__(self,idx):
#if index % 2 == 0:
img0 = cv2.imread(self._image_paths[idx][0], 0)
img1 = cv2.imread(self._image_paths[idx][1], 0)
img0 = Image.fromarray(img0)
img1 = Image.fromarray(img1)
if self._mode == 'train':
img0 = GetTransforms(img0, type=self.cfg.use_transforms_type)
img1 = GetTransforms(img1, type=self.cfg.use_transforms_type)
img0 = np.array(img0)
img1 = np.array(img1)
img0 = transform(img0, self.cfg)
img1 = transform(img1, self.cfg)
labels = np.array(self._labels[idx]).astype(np.float32)
#print(self._image_paths[index][0],self._image_paths[index][1],labels)
img0 = torch.from_numpy(img0).float()
img1 = torch.from_numpy(img1).float()
labels = torch.from_numpy(labels).float()
if self._mode == 'train' or self._mode == 'dev':
return (img0, img1, labels)
else:
raise Exception('Unknown mode : {}'.format(self._mode))
#return img0, img1 , torch.from_numpy(np.array([int(self.training_df.iat[index,2])],dtype=np.float32))
return img0, img1 , labels
def predict_from_file(self, image_file):
"""Run prediction from image path
Args:
image_file (str): image path
Returns:
numpy array: model prediction in numpy array type
"""
image_gray = cv2.imread(image_file, 0)
image = transform(image_gray, self.cfg)
image = torch.from_numpy(image)
image = image.unsqueeze(0)
return tensor2numpy(nn.Sigmoid()(self.predict(image)))
def image_reader(self, image_file):
"""
Args:
image_file: str to an image file path
Returns:
image: (1, C, H, W) tensor of image
image_color: (H, W, C) numpy array of RGB image
"""
image_gray = cv2.imread(image_file, 0)
assert image_gray is not None, "invalid image read in: {}"\
.format(image_file)
image_color = cv2.cvtColor(image_gray, cv2.COLOR_GRAY2RGB)
image = transform(image_gray, self.cfg)
image = torch.from_numpy(image)
image = image.unsqueeze(0)
return image, image_color
def __getitem__(self, idx):
image = cv2.imread(self._image_paths[idx], 0)
image = Image.fromarray(image)
if self._mode == 'train':
image = GetTransforms(image, type=self.cfg.use_transforms_type)
image = np.array(image)
image = transform(image, self.cfg)
labels = np.array(self._labels[idx]).astype(np.float32)
path = self._image_paths[idx]
if self._mode == 'train' or self._mode == 'dev':
return (image, labels)
elif self._mode == 'test':
return (image, path)
elif self._mode == 'heatmap':
return (image, path, labels)
else:
raise Exception('Unknown mode : {}'.format(self._mode))
def __getitem__(self, idx):
image = cv2.imread(self._image_paths[idx],
0) # reading images in grayscale
image = Image.fromarray(image) # converting it to pil image
if self._mode == 'train': # No TTA used
image = GetTransforms(image, type=self.cfg.use_transforms_type)
image = np.array(image)
image = transform(image,
self.cfg) # this is happening for test time as well
labels = np.array(self._labels[idx]).astype(np.float32)
path = self._image_paths[idx]
if self._mode == 'train' or self._mode == 'dev':
return (image, labels)
elif self._mode == 'test':
return (image, path)
elif self._mode == 'heatmap':
return (image, path, labels)
else:
raise Exception('Unknown mode : {}'.format(self._mode))
def __getitem__(self, index):
#if index % 2 == 0:
#print(index)
if (index % 4 == 0):
while True:
line0 = random.choice(self.lines)
fields0 = line0.strip('\n').split(',')
if self.dict[0].get(fields0[15]) == '0':
break
while True:
line1 = random.choice(self.lines)
fields1 = line1.strip('\n').split(',')
if self.dict[0].get(fields1[15]) == '1':
break
self._labels.append(1)
elif (index % 4 == 1):
while True:
line0 = random.choice(self.lines)
fields0 = line0.strip('\n').split(',')
if self.dict[0].get(fields0[15]) == '1':
break
while True:
line1 = random.choice(self.lines)
fields1 = line1.strip('\n').split(',')
if self.dict[0].get(fields1[15]) == '0':
break
self._labels.append(1)
elif (index % 4 == 2 or index % 4 == 3):
while True:
line0 = random.choice(self.lines)
fields0 = line0.strip('\n').split(',')
if self.dict[0].get(fields0[15]) == '1':
break
while True:
line1 = random.choice(self.lines)
fields1 = line1.strip('\n').split(',')
if self.dict[0].get(fields1[15]) == '1':
break
self._labels.append(0)
'''
if should_get_same_class:
while True:
line1 = random.choice(self.lines)
fields1 = line1.strip('\n').split(',')
if self.dict[0].get(fields0[7]) == self.dict[0].get(fields1[7]):
break
else:
line1 = random.choice(self.lines)
fields1 = line1.strip('\n').split(',')
'''
image_path = fields0[0]
image_path = "/kaggle/input/chexpert/" + image_path[21:]
img0 = cv2.imread(image_path, 0)
image_path = fields1[0]
image_path = "/kaggle/input/chexpert/" + image_path[21:]
img1 = cv2.imread(image_path, 0)
img0 = Image.fromarray(img0)
img1 = Image.fromarray(img1)
if self._mode == 'train':
img0 = GetTransforms(img0, type=self.cfg.use_transforms_type)
img1 = GetTransforms(img1, type=self.cfg.use_transforms_type)
img0 = np.array(img0)
img1 = np.array(img1)
img0 = transform(img0, self.cfg)
img1 = transform(img1, self.cfg)
labels = np.array(self._labels[0]).astype(np.float32)
#print(self._image_paths[index][0],self._image_paths[index][1],labels)
img0 = torch.from_numpy(img0).float()
img1 = torch.from_numpy(img1).float()
labels = torch.from_numpy(labels).float()
if self._mode == 'train' or self._mode == 'dev':
return (img0, img1, labels)
else:
raise Exception('Unknown mode : {}'.format(self._mode))
#return img0, img1 , torch.from_numpy(np.array([int(self.training_df.iat[index,2])],dtype=np.float32))
return img0, img1, labels
