def main():
#Get random dog image
endpoint = "https://dog.ceo/api/breeds/image/random"
response = requests.get(endpoint)
img_url = response.json()['message']
# Get style image and process it
style_img_url = "https://upload.wikimedia.org/wikipedia/en/1/14/Picasso_The_Weeping_Woman_Tate_identifier_T05010_10.jpg"
style_image = preprocess_image(load_img(style_img_url), 256)
#Calculate style bottleneck for the preprocessed style image
style_bottleneck = run_style_predict(style_image)
#Process the content image
content_image = preprocess_image(load_img(img_url), 384)
# Stylize the content image using the style bottleneck
stylized_image = run_style_transform(style_bottleneck, content_image)
# Create a plot of the images
fig = plot(content_image, style_image, stylized_image)
tmpfile = BytesIO()
fig.savefig(tmpfile, format='png')
encoded = base64.b64encode(tmpfile.getvalue()).decode('utf8')
# Display the output in html
image_html = '<h1>A randoml dog in the style of Picasso!</h1>' + '<img src=\'data:image/png;base64,{}\'>'.format(
encoded)
return (image_html)
def calculate_loss_of_contrast(images_hdr_path, images_ldr_path,
norm_value_hdr, norm_value_ldr):
if len(images_hdr_path) != len(images_ldr_path):
raise ValueError('Sizes of HDR and LDR image lists are different.')
if len(images_hdr_path) == 0:
raise ValueError('List of HDR or LDR image paths must not be empty.')
loss_global_contrast = 0
loss_local_contrast = 0
for hdr_image_path, ldr_image_path in zip(images_hdr_path,
images_ldr_path):
print(".", end='', flush=True) # show progress
image_hdr = preprocess_image(
cv2.imread(str(hdr_image_path),
cv2.IMREAD_GRAYSCALE | cv2.IMREAD_ANYDEPTH),
norm_value_hdr)
image_ldr = preprocess_image(
cv2.imread(str(ldr_image_path), cv2.IMREAD_GRAYSCALE),
norm_value_ldr, gamma_correction)
loss_global_contrast += measure_global_contrast(image_hdr, image_ldr)
loss_local_contrast += measure_local_contrast(image_hdr, image_ldr)
loss_global_contrast /= len(images_hdr_path)
loss_local_contrast /= len(images_hdr_path)
print() # newline after progress dots
return loss_global_contrast, loss_local_contrast
def play(save_path):
''' Loads network from the location of save_path and plays a game of Pong. '''
# Initialize the Pong gym environment, set seeds
env = gym.make('Pong-v0')
replay_memory = u.ReplayMemory()
G = tf.Graph()
with G.as_default():
# Import TF graph
saver = tf.train.import_meta_graph(save_path + '.meta',
clear_devices=True)
G.device(
'/cpu:0'
) # Run graph on CPU so play can be done without taking GPU resources
# Get input/output tensors
X = G.get_tensor_by_name('X:0')
Y = G.get_tensor_by_name('Y:0')
# Initialize TF session
sess_config = tf.ConfigProto(device_count={'CPU': 1, 'GPU': 0})
with tf.Session(config=sess_config) as sess:
print('Reloading parameters...')
saver.restore(sess, save_path)
# Iterate over episodes
while True:
obs = u.preprocess_image(env.reset())
for i in range(3):
replay_memory.add_frame(
np.zeros((160 // DOWNSAMPLE, 160 // DOWNSAMPLE)))
replay_memory.add_frame(obs)
# Iterate over frames
done = False
while not done:
# Feed state into DQN
s = np.stack(
[replay_memory.frames[i] for i in range(-4, 0)],
axis=-1).reshape(1, 160 // DOWNSAMPLE,
160 // DOWNSAMPLE, 4)
y = sess.run(Y, feed_dict={X: s})
# Decide on action greedily
a = np.argmax(y) + 1
# Take action, observe environment, reward
obs, r, done, _ = env.step(a)
for i in range(STEPS_TO_SKIP):
obs, r, done_temp, _ = env.step(1)
if done_temp == True:
done = True
env.render()
# Add new frame to replay memory
replay_memory.add_frame(u.preprocess_image(obs))
q = input('Play again? ')
if q in ['', 'y', 'Y']:
pass
else:
env.render(close=True)
break
def get_trajectory(self, env, episode_max_length, render=False):
"""
Run agent-environment loop for one whole episode (trajectory)
Return dictionary of results
Note that this function returns more than the get_trajectory in the Learner class.
"""
state = preprocess_image(env.reset())
prev_state = state
states = []
actions = []
rewards = []
episode_probabilities = []
for _ in range(episode_max_length):
delta = state - prev_state
action, probabilities = self.act(delta)
states.append(delta)
prev_state = state
state, rew, done, _ = env.step(action)
state = preprocess_image(state)
actions.append(action)
rewards.append(rew)
episode_probabilities.append(probabilities)
if done:
break
if render:
env.render()
return {
"reward": np.array(rewards),
"state": np.array(states),
"action": np.array(actions),
"prob": np.array(episode_probabilities)
}
def load_data(labels,
img_path,
size=299,
data=None,
oversample=False,
multi_label=True):
if oversample:
new_df = pd.DataFrame()
major = labels[labels.iloc[:, 1] == np.argmax(
np.bincount(labels.iloc[:, 1]))]
minors = np.unique(labels.iloc[:, 1])[np.unique(
labels.iloc[:, 1]) != np.argmax(np.bincount(labels.iloc[:, 1]))]
for i in minors:
minor = labels[labels.iloc[:, 1] == i]
n = len(major) - len(minor)
minor_df = resample(minor,
replace=True,
n_samples=n + len(minor),
random_state=42)
new_df = new_df.append(minor_df)
new_df = new_df.reset_index(drop=True)
labels = pd.concat([major, new_df], ignore_index=True)
labels = labels.reset_index(drop=True)
else:
pass
labels = labels.reset_index(drop=True)
N = labels.shape[0]
# add a size to argument
x_train = np.empty((N, size, size, 3), dtype=np.uint8)
y_train = pd.get_dummies(labels.iloc[:, 1]).values
if multi_label:
y_train_multi = np.empty(y_train.shape, dtype=y_train.dtype)
y_train_multi[:, 4] = y_train[:, 4]
for i in range(3, -1, -1):
y_train_multi[:, i] = np.logical_or(y_train[:, i],
y_train_multi[:, i + 1])
y_train = y_train_multi
for i, image_id in enumerate(tqdm(labels.iloc[:, 0])):
if data == 'aptos':
x_train[i, :, :, :] = preprocess_image(
img_path + 'Aptos/train_images/{}.png'.format(image_id), size)
else:
try:
x_train[i, :, :, :] = preprocess_image(
img_path + 'train_resized/{}.jpeg'.format(image_id), size)
except FileNotFoundError:
continue
return x_train, y_train
def get_tensor_pair(self, patch):
patch_size = self.hr_patch_size
lr_patch_size = patch_size / 2
lr_patch = resize(patch, [lr_patch_size, lr_patch_size, 3],
mode='reflect')
hr_patch = utils.preprocess_image(patch)
lr_patch = utils.preprocess_image(lr_patch)
return hr_patch, lr_patch
def generator(samples, batch_size=32):
num_samples = len(samples)
while 1: # Loop forever so the generator never terminates
sklearn.utils.shuffle(samples)
for offset in range(0, num_samples, batch_size):
batch_samples = samples[offset:offset + batch_size]
images = []
angles = []
for batch_sample in batch_samples:
center_name = FLAGS.data_dir + '/IMG/' + batch_sample[0].split(
'/')[-1]
center_image = preprocess_image(cv2.imread(center_name))
center_angle = float(batch_sample[3])
images.append(center_image)
angles.append(center_angle)
# data augmentation
augmented_c_image, augmented_c_angle = augment_data(
center_image, center_angle)
images.append(augmented_c_image)
angles.append(augmented_c_angle)
# add in left and right cameras' info
left_name = FLAGS.data_dir + '/IMG/' + batch_sample[1].split(
'/')[-1]
left_image = preprocess_image(cv2.imread(left_name))
right_name = FLAGS.data_dir + '/IMG/' + batch_sample[2].split(
'/')[-1]
right_image = preprocess_image(cv2.imread(right_name))
# create adjusted steering measurements for the side camera images
correction = 0.3 # this is a parameter to tune
left_angle = center_angle + correction
right_angle = center_angle - correction
# add images and angles to data set
images.extend([left_image, right_image])
angles.extend([left_angle, right_angle])
# data augmentation
augmented_l_image, augmented_l_angle = augment_data(
left_image, left_angle)
augmented_r_image, augmented_r_angle = augment_data(
right_image, right_angle)
images.extend([augmented_l_image, augmented_r_image])
angles.extend([augmented_l_angle, augmented_r_angle])
# trim image to only see section with road
X = np.array(images)
y = np.array(angles)
X, y = sklearn.utils.shuffle(X, y)
yield X, y
def play(G, save_path):
# Initialize the Pong gym environment, set seeds
env = gym.make('Pong-v0')
replay_memory = u.ReplayMemory()
with G.as_default():
# Get input/output tensors
X = G.get_tensor_by_name('X:0')
Y = G.get_tensor_by_name('Y:0')
saver = tf.train.Saver(var_list=None, max_to_keep=5)
# Initialize TF session
with tf.Session() as sess:
print('Reloading parameters...')
saver.restore(sess, save_path)
# Iterate over episodes
while True:
obs = u.preprocess_image(env.reset())
for i in range(3):
replay_memory.add_frame(
np.zeros((160 // DOWNSAMPLE, 160 // DOWNSAMPLE)))
replay_memory.add_frame(obs)
# Iterate over frames
done = False
while not done:
# Feed state into DQN
s = np.stack(
[replay_memory.frames[i] for i in range(-4, 0)],
axis=-1).reshape(1, 160 // DOWNSAMPLE,
160 // DOWNSAMPLE, 4)
y = sess.run(Y, feed_dict={X: s})
# Decide on action
a = np.argmax(y) + 1
# Take action, observe environment, reward
obs, r, done, _ = env.step(a)
for i in range(STEPS_TO_SKIP):
obs, r, done_temp, _ = env.step(1)
if done_temp == True:
done = True
env.render()
# Add new state/reward to replay memory
replay_memory.add_frame(u.preprocess_image(obs))
q = input('play again?')
if q in ['', 'y', 'Y']:
pass
else:
env.render(close=True)
break
def generate(self, original_image, im_label, target_class):
im_label_as_var = torch.from_numpy(np.asarray([target_class]))
criterion = nn.CrossEntropyLoss()
# process image as ImageNet dataset format
processed_image = preprocess_image(original_image)
for i in range(10):
print('Iteration: {}'.format(str(i)))
# zero previous gradient
processed_image.grad = None
out = self.model(processed_image)
pred_loss = criterion(out, im_label_as_var)
# calculate gradient
pred_loss.backward()
# create noise
# processed_image.grad.data represents gradient of first layer
adv_noise = self.alpha * torch.sign(processed_image.grad.data)
# add noise to image
processed_image.data = processed_image.data - adv_noise
# generate confirmation image
recreated_image = recreate_image(processed_image)
# process confirmation image
prep_confirmation_image = preprocess_image(recreated_image)
pred = self.model(prep_confirmation_image)
# get prediction index
_, pred_index = pred.data.max(1)
confidence = F.softmax(pred, dim=1)[0][pred_index].data.numpy()[0]
# convert tensor to int
pred_index = pred_index.numpy()[0]
if pred_index == target_class:
print('\nOriginal image class: ', im_label,
'\nTarget image class: ', target_class, '\nConfidence: ',
confidence)
# create the image for noise as: Original image - generated image
noise_image = original_image - recreated_image
cv2.imwrite(
'./generated/targeted_adv_noise_from_' + str(im_label) +
'_to_' + str(pred_index) + '.jpg', noise_image)
# write image
cv2.imwrite(
'./generated/targeted_adv_img_from_' + str(im_label) +
'_to_' + str(pred_index) + '.jpg', recreated_image)
break
return 1
def compute_features(keyframes, path_out, layer='conv5_1', max_dim=340):
"""
store all local features from conv5_1 of vgg16 at 340 mac res and
store then in path_data/[mode]/layer/max_dim
"""
# create folder if it does not exist
if not os.path.exists(path_out):
os.makedirs(path_out)
# init model
model = init_model(layer)
# message
desc_text = "Feature extraction --> Layer: {}, Max_dim: {}, total_images={}".format(
layer, max_dim, keyframes.shape[0])
# process keyframes
for k, keyframe in tqdm(enumerate(keyframes),
ascii=True,
desc=desc_text):
feats = model.predict(preprocess_image(
keyframe, max_dim=max_dim)).squeeze(axis=0)
np.save(os.path.join(path_out, "{}".format(k)), feats)
# resume computation
else:
computed = np.array(
[int(k.split('.')[0]) for k in os.listdir(path_out)])
# if features has been computed...
if computed.shape[0] == keyframes.shape[0]:
return path_out
# start from the last computed...
elif computed.shape[0] == 0:
last = 0
else:
last = np.sort(computed)[::-1][0]
# init model
model = init_model(layer)
desc_text = "Feature extraction --> Layer: {}, Max_dim: {}, total_images={}".format(
layer, max_dim, keyframes.shape[0] - last)
for k, keyframe in tqdm(enumerate(keyframes[last:]),
ascii=True,
desc=desc_text):
feats = model.predict(preprocess_image(
keyframe, max_dim=max_dim)).squeeze(axis=0)
k += last
np.save(os.path.join(path_out, "{}".format(k)), feats)
return path_out
def predict():
data = {'success': False}
if request.files.getlist("images[]"):
images = request.files.getlist("images[]")
images_preprocess = []
items = []
for image in images:
item = {}
image = image.read()
image = Image.open(io.BytesIO(image))
image = utils.preprocess_image(image)
images_preprocess.append(image / 255.)
item['image'] = utils.encode_image(image)
items.append(item)
images_preprocess = np.array(images_preprocess)
predicts = model.predict(images_preprocess, steps=len(images))
predicts = np.argmax(predicts, axis=1)
for idx, predict in enumerate(predicts):
items[idx]['info'] = class_info[str(predict)]
data['success'] = True
data['data'] = items
print(data)
return json.dumps(data, ensure_ascii=False, cls=utils.NumpyEncoder)
def predict_image(self, image, voc, img_size=IMAGE_SIZE):
if isinstance(image, str):
img = preprocess_image(image, img_size)
elif isinstance(image, np.ndarray):
img = image
else:
raise TypeError(
"Unknown handling of image input of type {}".format(
type(image)))
img_inp = np.expand_dims(img, 0)
current_context = np.array([voc.word2one_hot_dict[PLACEHOLDER]] *
(self.max_code_length))
current_context = np.expand_dims(current_context, 0)
current_context[0, 0, :] = voc.word2one_hot_dict[START_WORD]
word_predictions = []
for i in range(self.max_code_length):
probas = self.predict({
'img_data': img_inp,
'context': current_context
})
probas = probas['code'][0][i]
prediction = np.argmax(probas)
word_prediction = voc.token2word_dict[prediction]
if word_prediction == END_WORD or word_prediction == PLACEHOLDER:
break
word_predictions.append(word_prediction)
current_context[0,
i + 1, :] = voc.word2one_hot_dict[word_prediction]
return " ".join(word_predictions)
def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = data["steering_angle"]
# The current throttle of the car
throttle = data["throttle"]
# The current speed of the car
speed = data["speed"]
# The current image from the center camera of the car
imgString = data["image"]
image = Image.open(BytesIO(base64.b64decode(imgString)))
image = utils.preprocess_image(image)
image_array = np.asarray(image)
steering_angle = float(
model.predict(image_array[None, :, :, :], batch_size=1))
controller.set_desired(set_speed)
throttle = controller.update(float(speed))
print(steering_angle, throttle)
send_control(steering_angle, throttle)
# save frame
if args.image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
image.save('{}.jpg'.format(image_filename))
else:
# NOTE: DON'T EDIT THIS.
sio.emit('manual', data={}, skip_sid=True)
def slide(self, image):
"""
Extracts sliding windows over the preprocessed image
@param image: A 2d ndarray representing an image
@return: A length two tuple of a list of dictionaries, each corresponding to a window, and a list of images,
each corresponding to a down sampled image. The down sampled images have been preprocessed by
utils.preprocess_image
"""
# Each 'patch' is a list of contributions to bins, like the output from utils.preprocess_image
patches_dicts = []
images = []
for image_index, (scale, patch_extractor) in enumerate(izip(self.scales, self.patch_extractors)):
if scale != 1:
downsampled_image = ndimage.interpolation.zoom(image, scale)
else:
downsampled_image = image
preprocessed_image = utils.preprocess_image(downsampled_image)
images.append(preprocessed_image)
positions = patch_extractor.patch_positions(preprocessed_image)
rf_size = [patch_extractor.rf_size[0] / scale, patch_extractor.rf_size[1] / scale]
#patches = patch_extractor.extract_all(preprocessed_image)
#for i, patch in enumerate(patches):
for i in xrange(positions.shape[1]):
patch_position = positions[:, i].tolist()
orig_position = (positions[:, i] / scale).tolist()
patches_dicts.append({
'image_index': image_index,
'patch_position': patch_position,
'patch_size': patch_extractor.rf_size,
'orig_position': orig_position,
'orig_size': rf_size,
})
return patches_dicts, images
def run_images():
parser = argparse.ArgumentParser()
parser.add_argument('--dir_path', default="./test_images", type=str)
parser.add_argument('--out_path', default='./out_images', type=str)
parser.add_argument('--model_file',
default="./model/yolov2-tiny-voc.h5",
type=str)
args = parser.parse_args()
paths = utils.get_image_path(args.dir_path)
images = []
print('reading image from %s' % args.dir_path)
for path in paths:
image = cv2.imread(path)
resized = cv2.resize(image, (416, 416))
images.append(resized)
image_processed = []
for image in images:
image_processed.append(utils.preprocess_image(image))
print('loading model from %s' % args.model_file)
model = load_model(args.model_file)
predictions = model.predict(np.array(image_processed))
if not os.path.exists(args.out_path):
os.mkdir(args.out_path)
print('writing image to %s' % args.out_path)
for i in range(predictions.shape[0]):
boxes = utils.process_predictions(predictions[i],
probs_threshold=0.3,
iou_threshold=0.2)
out_image = utils.draw_boxes(images[i], boxes)
cv2.imwrite('%s/out%s.jpg' % (args.out_path, i), out_image)
def main():
parser = argparse.ArgumentParser(description="Deep Dream tutorial")
parser.add_argument("--src_img", default="sky.jpg", required=True, type=str, help="Source image to perform deep dram on")
parser.add_argument("--directory", default="../dream_dir", type=str, help="Result directory to save intermediate images")
parser.add_argument("--iterations", default="1000", type=int, help="How long to dream.")
parser.add_argument("--save_every", default="1", type=int, help="Saving image after every _ iterations")
parser.add_argument("--downscale_factor", default="3", type=int, help="Downscale factor for reducing image scale")
parser.add_argument("--overwrite_save_dir", default=False, type=bool, help="Delete all files in selected directory")
args = parser.parse_args()
proc = preprocess_image(args.src_img)
print(proc.shape)
model = get_feature_extractor(layer_settings)
print("model loaded\nDreaming")
if not os.path.isdir(args.directory):
try:
os.mkdir(args.directory)
print(f"created directory \"{args.directory}\"")
except:
print("couldn't create directory")
if len(os.listdir(args.directory))>0 and args.overwrite_save_dir==True:
for f in os.listdir(args.directory):
os.remove(os.path.join(args.directory, f))
print("Directory cleaned")
dream_on(proc, model, args.directory, iterations=args.iterations, save_every=args.save_every, downscale_factor=args.downscale_factor)
def test_all_images():
parser = argparse.ArgumentParser()
arg = parser.add_argument
# model-related variables
arg('--model-path', type=str, help='Model path')
arg('--dataset', type=str, help='roof: roof segmentation / income: income determination')
# image-related variables
arg('--masks-dir', type=str, default='./data/dataset/labels', help='numPy masks directory')
arg('--npy-dir', type=str, default='./data/dataset/split_npy', help='numPy preprocessed patches directory')
args = parser.parse_args()
roof_path = "./data/dataset/split_npy"
if args.dataset == "roof":
model = load_model(args.model_path, UNet11)
else:
model = load_model(args.model_path, UNet11, input_channels=5, num_classes=4)
# roof_model = load_model("./trained_models/model_10_percent_roof_Unet11_200epochs.pth", UNet11)
if not os.path.exists("./data/test_all"):
os.mkdir("./data/test_all")
# Select sample pictures
images_filenames = np.array(sorted(glob.glob(args.npy_dir + "/*.npy")))
for filename in tqdm(images_filenames):
fig = plt.figure(figsize=(10, 10))
image = pickle.load(open(filename, "rb"))
image = preprocess_image(image, args.dataset)
pred = run_model(image, model, args.dataset)
# if args.dataset == "income":
# roof_image = pickle.load(open(os.path.join(roof_path, filename[filename.rfind("/") + 1:]), "rb"))
# roof_image = preprocess_image(roof_image, "roof")
# pred_roof = run_model(roof_image, roof_model, "roof")
# pred[0][0] = pred[0][0] * pred_roof[0][0]
# pred[0][1] = pred[0][1] * pred_roof[0][0]
mask_path = os.path.join(args.masks_dir, args.dataset, filename[filename.rfind("/") + 1:])
y = pickle.load(open(mask_path, "rb"))
fig.add_subplot(1, 3, 1)
plt.imshow(reverse_transform(image.cpu().numpy()[0], args.dataset))
fig.add_subplot(1, 3, 2)
plt.imshow(masks_to_colorimg(y, args.dataset))
fig.add_subplot(1, 3, 3)
plt.imshow(pred_to_colorimg(pred.cpu().numpy(), args.dataset))
plt.savefig(os.path.join("./data/test_all",
filename[filename.rfind("/") + 1:filename.rfind(".")] + ".png"))
plt.clf()
plt.close(fig)
def predict(sess, image_file):
"""
Runs the graph stored in "sess" to predict boxes for "image_file". Prints and plots the preditions.
Arguments:
sess -- your tensorflow/Keras session containing the YOLO graph
image_file -- name of an image stored in the "images" folder.
Returns:
out_scores -- tensor of shape (None, ), scores of the predicted boxes
out_boxes -- tensor of shape (None, 4), coordinates of the predicted boxes
out_classes -- tensor of shape (None, ), class index of the predicted boxes
Note: "None" actually represents the number of predicted boxes, it varies between 0 and max_boxes.
"""
image, image_data = preprocess_image("images/" + image_file, model_image_size = (608, 608))
out_scores, out_boxes, out_classes = sess.run(yolo_eval(yolo_outputs, image_shape),
feed_dict={yolo_model.input:image_data,
K.learning_phase():0})
print('Found {} boxes for {}'.format(len(out_boxes), image_file))
colors = generate_colors(class_names)
draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors)
image.save(os.path.join("out", image_file), quality=90)
output_image = scipy.misc.imread(os.path.join("out", image_file))
imshow(output_image)
return out_scores, out_boxes, out_classes
def deblur(image_path):
data = {
'A_paths': [image_path],
'A': np.array([preprocess_image(load_image(image_path))])
}
x_test = data['A']
g = generator_model()
g.load_weights('generator.h5')
generated_images = g.predict(x=x_test)
generated = np.array([deprocess_image(img) for img in generated_images])
#kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], np.float32) #锐化
print(generated.shape)
#ima = Image.fromarray(generated)
#dst = cv.filter2D(ima, -1, kernel=kernel)
#dst.save("/content/drive/My Drive/5405_digitalMedia/result/e.png")
#image_arr = np.array(dst)
x_test = deprocess_image(x_test)
'''
img = generated[0, :, :, :]
im = Image.fromarray(img.astype(np.uint8))
im.save("/content/drive/My Drive/5405_digitalMedia/result/f.png")
src = cv.imread("/content/drive/My Drive/5405_digitalMedia/result/f.png")
kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], dtype=np.float32)
sharpen_image = cv.filter2D(src, cv.CV_32F, kernel=kernel)
sharpen_image = cv.convertScaleAbs(sharpen_image)
cv.imwrite("/content/drive/My Drive/5405_digitalMedia/result/g.png",sharpen_image)
'''
for i in range(generated_images.shape[0]):
x = x_test[i, :, :, :]
img = generated[i, :, :, :]
output = np.concatenate((x, img), axis=1)
im = Image.fromarray(output.astype(np.uint8))
im.save("result.jpg") #('deblur'+image_path)
def predict_and_draw(sess, image, scores, boxes, classes, yolo_model):
"""
Runs the graph stored in "sess" to predict boxes and draw bounding boxes
Arguments:
sess -- your tensorflow/Keras session containing the YOLO graph
image - Original Image
scores -- tensor of shape (None, ), predicted score for each box
boxes -- tensor of shape (None, 4), predicted box coordinates
classes -- tensor of shape (None,), predicted class for each box
yolo_model - pre-trained YOLO Model
Returns:
image -- Image with bounding box drawn
"""
# Preprocess your image
image_data = preprocess_image(image, model_image_size=(608, 608))
# Run the session with the correct tensors and choose the correct placeholders in the feed_dict.
# You'll need to use feed_dict={yolo_model.input: ... , K.learning_phase(): 0})
out_scores, out_boxes, out_classes = sess.run([scores, boxes, classes],
feed_dict={yolo_model.input: image_data, K.learning_phase(): 0})
# Draw bounding boxes on the image file
box = Box(image, out_scores, out_boxes, out_classes, class_names)
box.draw_boxes()
# return out_scores, out_boxes, out_classes
return image
def predict():
input_size = (416, 416)
tf.app.flags.DEFINE_string('image_file', './images/person.jpg', 'image_path')
FLAGS = tf.app.flags.FLAGS
image_file = FLAGS.image_file
image = cv2.imread(image_file)
image_shape = image.shape[:2]
image_cp = preprocess_image(image, input_size)
images = tf.placeholder(tf.float32, [1, input_size[0], input_size[1], 3])
detection_feat = darknet(images)
feat_sizes = input_size[0] // 32, input_size[1] // 32
detection_results = decode(detection_feat, feat_sizes, len(class_names), anchors)
checkpoint_path = "./checkpoint_dir/yolo2_coco.ckpt"
#checkpoint_path = "/Users/xiang/Downloads/DeepLearning_tutorials-master/ObjectDetections/yolo2/checkpoint_dir/yolo2_coco.ckpt"
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, checkpoint_path)
bboxes, obj_probs, class_probs = sess.run(detection_results, feed_dict={images: image_cp})
bboxes, scores, class_inds = postprocess(bboxes, obj_probs, class_probs,
image_shape=image_shape)
img_detection = draw_detection(image, bboxes, scores, class_inds, class_names)
cv2.imwrite("./res/detection.jpg", img_detection)
cv2.imshow("detection results", img_detection)
print('*****Click the window,and press any key to close!*****')
cv2.waitKey(0)
cv2.destroyAllWindows()
def main():
input_size = (416,416)
image_file = './yolo2_data/car.jpg'
image = cv2.imread(image_file)
image_shape = image.shape[:2] #只取wh，channel=3不取
# copy、resize416*416、归一化、在第0维增加存放batchsize维度
image_cp = preprocess_image(image,input_size)
# 【1】输入图片进入darknet19网络得到特征图，并进行解码得到：xmin xmax表示的边界框、置信度、类别概率
tf_image = tf.placeholder(tf.float32,[1,input_size[0],input_size[1],3])
model_output = darknet_slim(tf_image) # darknet19网络输出的特征图
output_sizes = input_size[0]//32, input_size[1]//32 # 特征图尺寸是图片下采样32倍
output_decoded = decode(model_output=model_output,output_sizes=output_sizes,
num_class=len(class_names),anchors=anchors) # 解码
model_path = "models/model.ckpt"
init_fn = slim.assign_from_checkpoint_fn(model_path,slim.get_variables())
with tf.Session() as sess:
init_fn(sess)
bboxes,obj_probs,class_probs = sess.run(output_decoded,feed_dict={tf_image:image_cp})
# 【2】筛选解码后的回归边界框——NMS(post process后期处理)
bboxes,scores,class_max_index = postprocess(bboxes,obj_probs,class_probs,image_shape=image_shape)
# 【3】绘制筛选后的边界框
img_detection = draw_detection(image, bboxes, scores, class_max_index, class_names)
cv2.imwrite("./yolo2_data/detection.jpg", img_detection)
print('YOLO_v2 detection has done!')
cv2.imshow("detection_results", img_detection)
cv2.waitKey(0)
def infer(image: str,
phi: int = 0,
saved_model: str = './savedmodel',
classes: dict = None,
score_threshold: float = 0.3,
nms_threshold: float = 0.5,
device: str = 'gpu'):
if device != 'gpu':
os.environ['CUDA_VISIBLE_DEVICES'] = '-1' #Using CPU
else:
os.environ['CUDA_VISIBLE_DEVICES'] = '0' #Using GPU
#For COCO dataset
if classes == None:
classes = {
value['id'] - 1: value['name']
for value in json.load(open('coco_90.json', 'r')).values()
}
#select resolution according to architecture
image_sizes = (512, 640, 768, 896, 1024, 1280, 1408)
image_size = image_sizes[phi]
#To get different color for each class
num_classes = len(classes.values())
colors = [
np.random.randint(0, 256, 3).tolist() for _ in range(num_classes)
]
#load the model
model = load_model(saved_model)
#load and preprocess image
img = cv2.imread(image)
src_image = img.copy()
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
h, w = img.shape[:2]
img, scale = preprocess_image(img, image_size=image_size)
#detect and post process
start = time.time()
boxes, scores, labels = model.predict_on_batch(
[np.expand_dims(img, axis=0)])
boxes, scores, labels = np.squeeze(boxes), np.squeeze(scores), np.squeeze(
labels)
end = time.time()
boxes = postprocess_boxes(boxes=boxes, scale=scale, height=h, width=w)
# print(f'infer time: {end-start}, fps: {1/(end-start)}')
# select indices which have a score above the threshold
indices = np.where(scores[:] > score_threshold)[0]
# indices = tf.image.non_max_suppression(boxes,scores,max_output_size=[100],iou_threshold = nms_threshold,score_threshold = score_threshold)
boxes = boxes[indices]
labels = labels[indices]
#draw boxes on the original image
draw_boxes(src_image, boxes, scores, labels, colors, classes)
return src_image
def plot_image_heatmap(self, img, top=1):
img_tensor = preprocess_image(img, self.model.mean,
self.model.std) # (1, 3, 299, 299)
self._forward(img_tensor)
cols = top + 1
plt.figure(figsize=(4 * cols, 4))
for i in range(cols):
if i == 0:
plt.subplot(1, cols, i + 1)
plt.imshow(img, alpha=1.0)
plt.title('Original image')
plt.axis('off')
else:
class_idx = self._get_class_idx(i)
label = self.idx2label[class_idx]
proba = self.probas[class_idx]
heatmap = self._generate_heatmap(class_idx)
plt.subplot(1, cols, i + 1)
plt.imshow(img, alpha=1.0)
plt.imshow(heatmap, cmap='jet', alpha=0.5)
plt.title('{} ({:.3f})'.format(label, proba))
plt.axis('off')
def main():
input_size = (416,416)
image_file = './005767.jpg'
image = cv2.imread(image_file)
image_shape = image.shape[:2]
tf.reset_default_graph()
# copy、resize416*416、
image_cp = preprocess_image(image,input_size)
#image_cp=np.ones([1,416,416,3]).astype(np.float32)
image_cp=np.load("005767.npy")#("/home/huawei/chems/bioavailability_model/atlas_data/005767.npy")
image_cp=np.transpose(image_cp,[0,2,3,1])
np.save("atoms.npy",image_cp)
#
with tf.name_scope('input'):
tf_image = tf.placeholder(tf.float32,[1,input_size[0],input_size[1],3],name='input_data')
#meta_variable=tf.placeholder(tf.float32,[1,1,len(class_names)*1024,1],name='meta_weigiht')
model_output = Darknet(tf_image)
#meta=np.ones([1,1,len(class_names)*1024,1], dtype=np.float32)
model_path = "./yolov2_model/yolov2_coco.ckpt"
saver = tf.train.Saver()
with tf.Session() as sess:
#sess.run(model_output,feed_dict={tf_image:image_cp,meta_variable:meta})
sess.run(tf.global_variables_initializer())
a=sess.run(model_output.rtn(),feed_dict={tf_image:image_cp})
a=np.transpose(a,[0,3,1,2])
#a=np.transpose(a,[0,3,2,1])
a=np.reshape(a,[-1])[:90]
#print(a)
for i in range(90):
# print("=============================")
print(a[i],i)
saver.save(sess,model_path)
def get_data(self, image):
with tf.name_scope('data'):
jpeg = tf.read_file(image)
image = tf.image.decode_jpeg(jpeg, channels=3)
image = utils.preprocess_image(image, is_training=False)
image = tf.expand_dims(image, 0)
self.img = image
def r_mac_descriptor(self, file):
"""
Get the descriptor from the path of the img
:param file: the path of the image
:return: the descriptor
"""
# Load sample image
# file = utils.DATA_DIR + 'sample.jpg'
K.clear_session()
img = image.load_img(file)
# Resize
scale = utils.IMG_SIZE / max(img.size)
new_size = (int(np.ceil(scale * img.size[0])),
int(np.ceil(scale * img.size[1]))
) # (utils.IMG_SIZE, utils.IMG_SIZE)
#print('Original size: %s, Resized image: %s' % (str(img.size), str(new_size)))
img = img.resize(new_size)
# Mean substraction
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_image(x)
# Load RMAC model
Wmap, Hmap = self.get_size_vgg_feat_map(x.shape[3], x.shape[2])
regions = self.rmac_regions(Wmap, Hmap, 7)
#print('Loading RMAC model...')
model = self.rmac((x.shape[1], x.shape[2], x.shape[3]), len(regions))
# Compute RMAC vector
#print('Extracting RMAC from image...')
return model.predict([x, np.expand_dims(regions, axis=0)])
def read_and_decode(self, filename):
filename_queue = tf.train.string_input_producer([filename])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'image_raw':
tf.FixedLenFeature([],
tf.string),
'label_raw':
tf.FixedLenFeature([],
tf.string),
'height':
tf.FixedLenFeature([],
tf.int64),
'width':
tf.FixedLenFeature([], tf.int64)
})
height = tf.cast(features['height'], tf.int32)
width = tf.cast(features['width'], tf.int32)
image = tf.decode_raw(features['image_raw'], tf.uint8)
image = tf.reshape(image, [height, width, 3])
label = tf.decode_raw(features['label_raw'], tf.uint8)
label = tf.reshape(label, [height, width, 1])
image, label = preprocess_image(image,
label,
is_training=self.config.is_training)
return image, label
def execute(file, curr, rank):
# Load sample image
# file = utils.DATA_DIR + 'sample.jpg'
img = image.load_img(file)
# Resize
scale = utils.IMG_SIZE / max(img.size)
new_size = (int(np.ceil(scale * img.size[0])),
int(np.ceil(scale * img.size[1])))
print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())),
str(curr), "is processing...")
img = img.resize(new_size)
# Mean substraction
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_image(x)
# Load RMAC model
Wmap, Hmap = get_size_vgg_feat_map(x.shape[3], x.shape[2])
regions = rmac_regions(Wmap, Hmap, 3)
# print('Loading RMAC model...')
model = rmac((x.shape[1], x.shape[2], x.shape[3]), len(regions), rank)
# Compute RMAC vector
# print('Extracting RMAC from image...')
RMAC = model.predict([x, np.expand_dims(regions, axis=0)])
return RMAC
def compute_features(images, args):
"""
Compute feature values for images
:param images: List of images to compute features for
:param args: Run-time arguments
:return: Feature layer values for images
"""
# Verify that pre-trained feature model exist
if not os.path.exists(args.feature_model):
logging.critical('Checkpoint %s does not exist' % args.feature_model)
sys.exit(1)
with tf.Graph().as_default():
# Pre-process images
input_images = [
preprocess_image('%s/%s.jpg' % (path, image_id), args)
for path, _, image_id in images
]
# Run pre-processed images through network
return run_network(feature_network(args),
args.feature_model,
args,
train=False,
value=input_images)
def main():
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
phi = 0
weighted_bifpn = False
model_path = 'checkpoints/flir_59_0.1409_0.6001.h5'
image_sizes = (512, 640, 768, 896, 1024, 1280, 1408)
image_size = image_sizes[phi]
# coco classes
classes = {
value['id'] - 1: value['name']
for value in json.load(open('coco_90.json', 'r')).values()
}
num_classes = 3
score_threshold = 0.01
colors = [
np.random.randint(0, 256, 3).tolist() for _ in range(num_classes)
]
_, model = efficientdet(phi=phi,
weighted_bifpn=weighted_bifpn,
num_classes=num_classes,
score_threshold=score_threshold)
model.load_weights(model_path, by_name=True)
video_path = r'G:\datasets\iray\cap\20200515_100016.avi'
video_path = r'G:\datasets\iray\cap\20200515_100016.avi'
video_path = r'G:\projects\20200513_102922.avi'
cap = cv2.VideoCapture(video_path)
while True:
ret, image = cap.read()
if not ret:
break
src_image = image.copy()
# BGR -> RGB
image = image[:, :, ::-1]
h, w = image.shape[:2]
image, scale = preprocess_image(image, image_size=image_size)
# run network
start = time.time()
boxes, scores, labels = model.predict_on_batch(
[np.expand_dims(image, axis=0)])
boxes, scores, labels = np.squeeze(boxes), np.squeeze(
scores), np.squeeze(labels)
print(time.time() - start)
boxes = postprocess_boxes(boxes=boxes, scale=scale, height=h, width=w)
# select indices which have a score above the threshold
indices = np.where(scores[:] > score_threshold)[0]
# select those detections
boxes = boxes[indices]
labels = labels[indices]
draw_boxes(src_image, boxes, scores, labels, colors, classes)
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.imshow('image', src_image)
cv2.waitKey(0)
import numpy as np import tensorflow as tf import cv2 from PIL import Image from model import darknet from detect_ops import decode from utils import preprocess_image, postprocess, draw_detection from config import anchors, class_names input_size = (416, 416) image_file = "./images/car.jpg" image = cv2.imread(image_file) image_shape = image.shape[:2] image_cp = preprocess_image(image, input_size) """ image = Image.open(image_file) image_cp = image.resize(input_size, Image.BICUBIC) image_cp = np.array(image_cp, dtype=np.float32)/255.0 image_cp = np.expand_dims(image_cp, 0) #print(image_cp) """ images = tf.placeholder(tf.float32, [1, input_size[0], input_size[1], 3]) detection_feat = darknet(images) feat_sizes = input_size[0] // 32, input_size[1] // 32 detection_results = decode(detection_feat, feat_sizes, len(class_names), anchors) checkpoint_path = "./checkpoint_dir/yolo2_coco.ckpt"
POOLING_FUNCTION = 'MAX'
# Load images
content_image = utils.read_image(CONTENT_PATH)
style_image = utils.read_image(STYLE_PATH)
g = tf.Graph()
with g.device(DEVICE), g.as_default(), tf.Session() as sess:
# 1. Compute content representation
print("1. Computing content representation...")
content_shape = (1,) + content_image.shape # add batch size dimension
x = tf.placeholder(tf.float32, content_shape)
net, activations, img_mean = vgg.net(VGG_19_PATH, x, pooling_function=POOLING_FUNCTION)
# Pre-process image
content_image_pp = utils.preprocess_image(content_image, img_mean)
content_representation = activations[CONTENT_LAYER].eval(feed_dict={x: np.array([content_image_pp])})
# 2. Compute style Gram matrices
print("2. Computing style Gram matrices...")
style_shape = (1,) + style_image.shape # add batch size dimension
x = tf.placeholder(tf.float32, style_shape)
net, activations, _ = vgg.net(VGG_19_PATH, x, pooling_function=POOLING_FUNCTION)
# Pre-process image
style_image_pp = utils.preprocess_image(style_image, img_mean)
style_layer_shapes = {}
gram_matrices = {}
for style_layer, _ in STYLE_LAYERS.items():
import cv2
import numpy as np
import tensorflow as tf
import matplotlib.image as mpimg
from ssd_300_vgg import SSD
from utils import preprocess_image, process_bboxes
from visualization import plt_bboxes
ssd_net = SSD()
classes, scores, bboxes = ssd_net.detections()
images = ssd_net.images()
sess = tf.Session()
# Restore SSD model.
ckpt_filename = './ssd_checkpoints/ssd_vgg_300_weights.ckpt'
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, ckpt_filename)
img = cv2.imread('./demo/dog.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_prepocessed = preprocess_image(img)
rclasses, rscores, rbboxes = sess.run([classes, scores, bboxes],
feed_dict={images: img_prepocessed})
rclasses, rscores, rbboxes = process_bboxes(rclasses, rscores, rbboxes)
plt_bboxes(img, rclasses, rscores, rbboxes)