class Test:
def __init__(self):
self.net_dict = NNState(mode='eval')
# Data Augmentation operations
img_transforms = transforms.Compose([
transforms.RandomRotation((-30, 30)),
transforms.RandomResizedCrop((64, 64), scale=(0.7, 1.0)),
transforms.ColorJitter(brightness=0.4,
contrast=0.3,
saturation=0.3,
hue=0.3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.eval_data = datasets.ImageFolder('./dataset_segmented/test',
transform=img_transforms)
def eval(self):
print('Evaluating...')
self.net_dict.net = self.net_dict.net.eval()
eval_loader = DataLoader(dataset=self.eval_data,
batch_size=self.net_dict.batch_size,
shuffle=False,
num_workers=0,
drop_last=False)
n_batch = len(eval_loader)
with torch.no_grad():
eval_loss_stack = self.net_dict.to_device(torch.Tensor())
correct = 0
total = 0
for i, batch in enumerate(eval_loader):
# forward propagation
inputs, labels = batch[0], batch[1]
inputs = self.net_dict.to_device(inputs)
labels = self.net_dict.to_device(labels)
# Forward
labels_hat = self.net_dict.net.forward(inputs)
_, predicted = torch.max(labels_hat.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
loss_batch = self.net_dict.criterion(labels_hat, labels)
eval_loss_stack = torch.cat(
(eval_loss_stack, loss_batch.unsqueeze(0)), 0)
print('Batch [%d/%d], Eval Loss: %.4f' %
(i + 1, n_batch, loss_batch))
eval_loss = torch.mean(eval_loss_stack)
print('*********************************')
print('=> Mean Evaluation Loss: %.3f' % eval_loss)
print('=> Accuracy of the network: %d %%' %
(100 * correct / total))
print('*********************************')
return eval_loss
def __init__(self):
self.net_dict = NNState(mode='eval')
# Data Augmentation operations
img_transforms = transforms.Compose([
transforms.RandomResizedCrop((64, 64), scale=(0.7, 1.0)),
transforms.ColorJitter(brightness=0.4,
contrast=0.3,
saturation=0.3,
hue=0.3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.eval_data = datasets.ImageFolder('./dataset_segmented/test',
transform=img_transforms)
def __init__(self):
self.net_dict = NNState('train')
# Data Augmentation operations
img_transforms = transforms.Compose([
transforms.RandomRotation((-30, 30)),
transforms.RandomResizedCrop((64, 64), scale=(0.7, 1.0)),
transforms.ColorJitter(brightness=0.4,
contrast=0.3,
saturation=0.3,
hue=0.3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.train_data = datasets.ImageFolder('./nn_dataset/train',
transform=img_transforms)
print(self.train_data.class_to_idx)
self.eval_data = datasets.ImageFolder('./nn_dataset/eval',
transform=img_transforms)
def __init__(self):
self.nn_state = NNState(mode='eval')
class Evaluate:
def __init__(self):
self.nn_state = NNState(mode='eval')
def sliding_window(self, img):
"""
This function converts the classifier has been trained to a detector
You Can modify this function to improve the detection accuracy
:param img: Input image in the format of PIL. Use Image.open(image_path)
to read the image.
:return: a single-channel heat map. with labels (1, 2, 3, ...)
"""
img = np.array(img)
w, h, _ = img.shape
start_time = time.time()
# the step size of moving the sliding window, measured in pixels
stride = 8
# Generate a grid of centers for the window
u_mesh, v_mesh = np.meshgrid(np.arange(h, step=stride),
np.arange(w, step=stride))
# the height, and width of the output heat map
h_out = len(np.arange(h, step=stride))
w_out = len(np.arange(w, step=stride))
u_mesh, v_mesh = u_mesh.reshape(-1), v_mesh.reshape(-1)
print('\n Generating Anchors ...')
all_anchors = list()
# number of crops at each designated window waitpoint
anchor_h2ws = list([0.6, 1, 1.5]) # different height to width ratio
anchor_heights = list([32, 64]) # the height of the sliding window
num_patches = 6
num_anchors = len(anchor_h2ws) * len(anchor_heights)
for i in tqdm(range(len(u_mesh))):
uv = [u_mesh[i], v_mesh[i]]
anchors_temp = self.get_multi_scal_anchors(uv, img, anchor_h2ws,
anchor_heights)
all_anchors += anchors_temp
anchor_imdb = AnchorIMDB(all_anchors)
anchor_loader = DataLoader(anchor_imdb,
batch_size=num_patches * num_anchors,
shuffle=False,
num_workers=4,
drop_last=False)
heat_map = list()
print('\n Inferring ...')
with torch.no_grad():
sigmoid = nn.Sigmoid()
for batch in tqdm(anchor_loader):
batch = self.nn_state.to_device(batch)
x = self.nn_state.net(batch)
# HERE DEFINES THE LOGIC OF CHOOSING THE FINAL LABEL OUT OF
# N ANCHORS
x = sigmoid(x).reshape((num_patches, num_anchors, -1))
val, _ = torch.max(x, 1)
# val = val.reshape((num_patches, -1))
score, pred = torch.max(val, 1)
# score, pred = score.float(), pred.float()
# pred = torch.where(score > 0.99, pred, torch.zeros(pred.size()))
heat_map += pred.data.reshape(num_patches).tolist()
print("--- %.3fs seconds ---" % (time.time() - start_time))
heat_map = np.asarray(heat_map).reshape(w_out, h_out)
return heat_map
def visualise_heatmap(self, heat_map, img, overlay=True):
"""
This function visualises the heat_map
:param heat_map:
:param img:
:param overlay: True to display the mask on top of the image. False to
display separately.
"""
h, w = heat_map.shape
out = np.ones((h, w, 3))
elephant = np.array([66, 135, 245]) / 255.0
llama = np.array([245, 114, 66]) / 255.0
snake = np.array([16, 207, 6]) / 255.0
bg = np.array([80, 80, 80]) / 255.0
for i in range(h):
for j in range(w):
if heat_map[i, j] == 0:
out[i, j, :] *= bg
elif heat_map[i, j] == 1:
out[i, j, :] = elephant
elif heat_map[i, j] == 2:
out[i, j, :] = llama
elif heat_map[i, j] == 3:
out[i, j, :] = snake
bg_label = label_box.Patch(color=bg, label='bg[0]')
elephant_label = label_box.Patch(color=elephant, label='elephant[1]')
llama_label = label_box.Patch(color=llama, label='llama[2]')
snake_label = label_box.Patch(color=snake, label='snake[3]')
if overlay:
out = Image.fromarray((out * 255).astype('uint8'))
out = out.resize(img.size)
out = out.convert("RGBA")
img = img.convert("RGBA")
out = Image.blend(img, out, alpha=.6)
plt.legend(
handles=[bg_label, elephant_label, llama_label, snake_label])
plt.imshow(out)
else:
fig, ax = plt.subplots(1, 2)
ax[1].legend(
handles=[bg_label, elephant_label, llama_label, snake_label])
ax[0].imshow(img)
ax[1].imshow(out)
plt.show()
def get_multi_scal_anchors(self, uv, np_img, anchor_h2ws, anchor_heights):
"""
Crops the image into sizes of the anchor boxes
:param uv: the window centre location
:param np_img: the original PIL image
:param anchor_h2ws: the height to width ratio of anchor boxes
:param anchor_heights: the height of the anchor bo
:return:
"""
h_max, w_max, _ = np_img.shape
u, v = uv[0], uv[1]
img_batch = list()
for h in anchor_heights:
for h2w in anchor_h2ws:
win_size = np.array([h, int(h / h2w)])
half_win = (win_size / 2.0).astype(int)
v_min = max(0, v - half_win[1])
v_max = min(h_max, v + half_win[1])
u_min = max(0, u - half_win[0])
u_max = min(w_max, u + half_win[0])
anchor_temp = np_img[v_min:v_max, u_min:u_max, :]
anchor_temp = Image.fromarray(anchor_temp)
img_batch.append(anchor_temp)
return img_batch
class Train:
def __init__(self):
self.net_dict = NNState('train')
# Data Augmentation operations
img_transforms = transforms.Compose([
transforms.RandomRotation((-30, 30)),
transforms.RandomResizedCrop((64, 64), scale=(0.7, 1.0)),
transforms.ColorJitter(brightness=0.4,
contrast=0.3,
saturation=0.3,
hue=0.3),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
self.train_data = datasets.ImageFolder('./dataset_segmented/train',
transform=img_transforms)
print(self.train_data.class_to_idx)
self.eval_data = datasets.ImageFolder('./dataset_segmented/eval',
transform=img_transforms)
def train(self):
train_loader = DataLoader(dataset=self.train_data,
batch_size=self.net_dict.batch_size,
shuffle=True,
num_workers=4,
drop_last=True)
n_batch = len(train_loader)
for epoch_idx in range(self.net_dict.last_epoch + 1,
self.net_dict.n_epochs):
train_loss_buff = torch.Tensor()
train_loss_buff = self.net_dict.to_device(train_loss_buff)
print('\nEpoch [%d/%d]:' % (epoch_idx, self.net_dict.n_epochs))
t_start = time.time()
# update the network
for i, batch in enumerate(train_loader):
self.net_dict.optimiser.zero_grad()
inputs, labels = batch[0], batch[1]
inputs = self.net_dict.to_device(inputs)
labels = self.net_dict.to_device(labels)
# Forward
labels_hat = self.net_dict.net.forward(inputs)
loss = self.net_dict.criterion(labels_hat, labels)
# Backward
loss.backward()
# Optimise
self.net_dict.optimiser.step()
train_loss_buff = torch.cat(
(train_loss_buff, loss.reshape(1, 1)), 0)
if (i + 1) % 10 == 0:
print('[%d/%d], Itr [%d/%d], Loss: %.4f' %
(epoch_idx, self.net_dict.n_epochs, i, n_batch,
loss.item()))
# current_lr = self.optimiser.param_groups[0]['lr']
self.net_dict.lr_scheduler.step()
avg_train_loss = torch.mean(train_loss_buff)
print('=> Average training loss: %.4f' % avg_train_loss)
print('Training Duration: %.3fs' % (time.time() - t_start))
if (epoch_idx + 1) % 1 == 0:
eval_loss_mean = self.eval()
# Save model, and best model if qualified
delta_acc = self.net_dict.best_acc - eval_loss_mean
if delta_acc > 0:
self.net_dict.best_acc = eval_loss_mean
self.net_dict.save_ckpt(epoch_idx, delta_acc)
def eval(self):
print('Evaluating...')
self.net_dict.net = self.net_dict.net.eval()
eval_loader = DataLoader(dataset=self.eval_data,
batch_size=self.net_dict.batch_size,
shuffle=False,
num_workers=0,
drop_last=False)
n_batch = len(eval_loader)
with torch.no_grad():
eval_loss_stack = self.net_dict.to_device(torch.Tensor())
correct = 0
total = 0
for i, batch in enumerate(eval_loader):
# forward propagation
inputs, labels = batch[0], batch[1]
inputs = self.net_dict.to_device(inputs)
labels = self.net_dict.to_device(labels)
# Forward
labels_hat = self.net_dict.net.forward(inputs)
_, predicted = torch.max(labels_hat.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
loss_batch = self.net_dict.criterion(labels_hat, labels)
eval_loss_stack = torch.cat(
(eval_loss_stack, loss_batch.unsqueeze(0)), 0)
print('Batch [%d/%d], Eval Loss: %.4f' %
(i + 1, n_batch, loss_batch))
eval_loss = torch.mean(eval_loss_stack)
print('*********************************')
print('=> Mean Evaluation Loss: %.3f' % eval_loss)
print('=> Accuracy of the network: %d %%' %
(100 * correct / total))
print('*********************************')
return eval_loss
class Evaluate:
def __init__(self):
self.nn_state = NNState(mode='eval')
def sliding_window(self, img):
"""
This function converts the classifier has been trained to a detector
You Can modify this function to improve the detection accuracy
:param img: Input image in the format of PIL. Use Image.open(image_path)
to read the image.
:return: a single-channel heat map. with labels (1, 2, 3, ...)
"""
img2 = img
img = np.array(img)
w, h, _ = img.shape
start_time = time.time()
# the step size of moving the sliding window, measured in pixels
stride = 8
# Generate a grid of centers for the window
u_mesh, v_mesh = np.meshgrid(np.arange(h, step=stride),
np.arange(w, step=stride))
# the height, and width of the output heat map
h_out = len(np.arange(h, step=stride))
w_out = len(np.arange(w, step=stride))
u_mesh, v_mesh = u_mesh.reshape(-1), v_mesh.reshape(-1)
print('\n Generating Anchors ...')
all_anchors = list()
# number of crops at each designated window waitpoint
anchor_h2ws = list([0.6, 1, 1.5]) # different height to width ratio
anchor_heights = list([32, 64]) # the height of the sliding window
num_patches = 6
num_anchors = len(anchor_h2ws) * len(anchor_heights)
for i in tqdm(range(len(u_mesh))):
uv = [u_mesh[i], v_mesh[i]]
anchors_temp = self.get_multi_scal_anchors(uv, img, anchor_h2ws,
anchor_heights)
all_anchors += anchors_temp
anchor_imdb = AnchorIMDB(all_anchors)
anchor_loader = DataLoader(anchor_imdb,
batch_size=num_patches * num_anchors,
shuffle=False,
num_workers=4,
drop_last=False)
heat_map = list()
print('\n Inferring ...')
with torch.no_grad():
sigmoid = nn.Sigmoid()
# sigmoid.cuda()
for batch in tqdm(anchor_loader):
# batch.cuda()
batch = self.nn_state.to_device(batch)
x = self.nn_state.net(batch)
# HERE DEFINES THE LOGIC OF CHOOSING THE FINAL LABEL OUT OF
# N ANCHORS
x = sigmoid(x).reshape((num_patches, num_anchors, -1))
val, _ = torch.max(x, 1)
# val = val.reshape((num_patches, -1))
score, pred = torch.max(val, 1)
# score, pred = score.float(), pred.float()
# pred = torch.where(score > 0.99, pred, torch.zeros(pred.size()))
heat_map += pred.data.reshape(num_patches).tolist()
print("--- %.3fs seconds ---" % (time.time() - start_time))
heat_map = np.asarray(heat_map, dtype=np.uint8).reshape(w_out, h_out)
#
#
# #-------------------
# # Setup SimpleBlobDetector parameters.
# params = cv2.SimpleBlobDetector_Params()
#
# # Change thresholds
# params.minThreshold = 0
# params.maxThreshold = 255
# params.thresholdStep = 25
#
# # Filter by Area.
# # params.filterByArea = True
# print(params)
#
# # Create a detector with the parameters
# ver = (cv2.__version__).split('.')
# print(f"using opencv version {ver}")
# if int(ver[0]) < 3 :
# detector = cv2.SimpleBlobDetector(params)
# else :
# detector = cv2.SimpleBlobDetector_create(params)
#
# # Detect blobs.
# print(f"heat map is a {type(heat_map)}, {heat_map.dtype} and img is a {type(img)}, {img.dtype}")
# heat_map = self.convertHeatmapToImage(heat_map, img)
# print(f"heat map is a {type(heat_map)}, {heat_map.dtype} and img is a {type(img)}, {img.dtype}")
# # keypoints = detector.detect(cv2.7(heat_map, cv2.COLOR_BGR2GRAY))
#
# # im = cv2.imread("blob.jpg", cv2.IMREAD_GRAYSCALE)
# # print(f"blob is a {type(im)}")
# # print(f"dtype {im.dtype}")
# # keypoints = detector.detect(im)
# # print(f"Blob Keypoints: {keypoints}")
#
# # plt.imshow(heat_map)
# # plt.imshow(im)
# # plt.show()
# print(heat_map.shape)
# # exit(0)
#
#
# # keypoints = detector.detect(heat_map)
# print(keypoints)
# # cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob
# im_with_keypoints = cv2.drawKeypoints(img2, keypoints, np.array([]), (0,0,255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
#
# # Show keypoints
# cv2.imshow("Keypoints", im_with_keypoints)
# #-------------------
return heat_map
def convertHeatmapToImage(self, heat_map, img):
h, w = heat_map.shape
out = np.zeros((h, w), dtype=np.uint8)
bg = 1
elephant = 2
llama = 3
snake = 5
crocodile = 7
out[heat_map == 0] = bg
out[heat_map == 1] = elephant
out[heat_map == 2] = llama
out[heat_map == 3] = snake
out[heat_map == 4] = crocodile
# for i in range(h):
# for j in range(w):
# if heat_map[i, j] == 0:
# out[i, j] == bg
# elif heat_map[i, j] == 1:
# out[i, j] = elephant
# elif heat_map[i, j] == 2:
# out[i, j] = llama
# elif heat_map[i, j] == 3:
# out[i, j] = snake
# elif heat_map[i, j] == 4:
# out[i, j] = crocodile
# out = Image.fromarray((out*255).astype('uint8'))
# out = out.resize(img.size)
# out = out.convert("RGBA")
# open_cv_image = np.array(out)
# Convert RGB to BGR
# print(type(open_cv_image) , ' size: ', open_cv_image.shape)
# open_cv_image = open_cv_image[:, :, ::-1]
# plt.imshow(out)
# plt.show()
# print(type(open_cv_image) , ' size: ', open_cv_image.shape)
return out
def visualise_heatmap(self, heat_map, img, overlay=True):
"""
This function visualises the heat_map
:param heat_map:
:param img:
:param overlay: True to display the mask on top of the image. False to
display separately.
"""
h, w = heat_map.shape
out = np.ones((h, w, 3))
elephant = np.array([66, 135, 245]) / 255.0
llama = np.array([245, 114, 66]) / 255.0
snake = np.array([16, 207, 6]) / 255.0
bg = np.array([80, 80, 80]) / 255.0
for i in range(h):
for j in range(w):
if heat_map[i, j] == 0:
out[i, j, :] *= bg
elif heat_map[i, j] == 1:
out[i, j, :] = elephant
elif heat_map[i, j] == 2:
out[i, j, :] = llama
elif heat_map[i, j] == 3:
out[i, j, :] = snake
bg_label = label_box.Patch(color=bg, label='bg[0]')
elephant_label = label_box.Patch(color=elephant, label='elephant[1]')
llama_label = label_box.Patch(color=llama, label='llama[2]')
snake_label = label_box.Patch(color=snake, label='snake[3]')
if overlay:
out = Image.fromarray((out * 255).astype('uint8'))
out = out.resize(img.size)
out = out.convert("RGBA")
img = img.convert("RGBA")
out = Image.blend(img, out, alpha=.6)
plt.legend(
handles=[bg_label, elephant_label, llama_label, snake_label])
plt.imshow(out)
else:
fig, ax = plt.subplots(1, 2)
ax[1].legend(
handles=[bg_label, elephant_label, llama_label, snake_label])
ax[0].imshow(img)
ax[1].imshow(out)
plt.show()
def get_multi_scal_anchors(self, uv, np_img, anchor_h2ws, anchor_heights):
"""
Crops the image into sizes of the anchor boxes
:param uv: the window centre location
:param np_img: the original PIL image
:param anchor_h2ws: the height to width ratio of anchor boxes
:param anchor_heights: the height of the anchor bo
:return:
"""
h_max, w_max, _ = np_img.shape
u, v = uv[0], uv[1]
img_batch = list()
for h in anchor_heights:
for h2w in anchor_h2ws:
win_size = np.array([h, int(h / h2w)])
half_win = (win_size / 2.0).astype(int)
v_min = max(0, v - half_win[1])
v_max = min(h_max, v + half_win[1])
u_min = max(0, u - half_win[0])
u_max = min(w_max, u + half_win[0])
anchor_temp = np_img[v_min:v_max, u_min:u_max, :]
anchor_temp = Image.fromarray(anchor_temp)
img_batch.append(anchor_temp)
return img_batch
