我们从Python开源项目中，提取了以下20个代码示例，用于说明如何使用scipy.misc.imrotate()。
def run_pretrained(input_state,model,action_states,gameState): print '\n\nLoading pretrained weights onto model...' model.load_weights(p.PRETRAINED_PATH) epsilon=1 while True: print 'Running pretrained model (no exploration) with weights at ', p.PRETRAINED_PATH nn_out = model.predict(input_state,batch_size=1,verbose=0) nn_action = [[0,0]] nn_action[0][np.argmax(nn_out)] =1 action,rand_flag = select_action(nn_action+action_states,prob=[epsilon,(1-epsilon)*1/7,(1-epsilon)*6/7]) rgbDisplay, reward, tState = gameState.frame_step(action) grayDisplay = (np.dot(np.fliplr(imrotate(imresize(rgbDisplay, (80,80), interp='bilinear'), -90))[:,:,:3], [0.299, 0.587, 0.114])).reshape((1,1,80,80)) output_state = np.append(grayDisplay,input_state[:,:p.HISTORY-1,:,:], axis=1) #############################################################################################################################################################################
def read_images_from_disk(input_queue): """Consumes a single filename and label as a ' '-delimited string. Args: filename_and_label_tensor: A scalar string tensor. Returns: Two tensors: the decoded image, and the string label. """ label = input_queue[1] file_contents = tf.read_file(input_queue[0]) example = tf.image.decode_png(file_contents, channels=3) return example, label # def random_rotate_image(image): # angle = np.random.uniform(low=-10.0, high=10.0) # return misc.imrotate(image, angle, 'bicubic')
def crawl_directory(directory, augment_with_rotations=False, first_label=0): """Crawls data directory and returns stuff.""" label_idx = first_label images = [] labels = [] info = [] # traverse root directory for root, _, files in os.walk(directory): logging.info('Reading files from %s', root) for file_name in files: full_file_name = os.path.join(root, file_name) img = imread(full_file_name, flatten=True) for idx, angle in enumerate([0, 90, 180, 270]): if not augment_with_rotations and idx > 0: break images.append(imrotate(img, angle)) labels.append(label_idx + idx) info.append(full_file_name) if len(files) == 20: label_idx += 4 if augment_with_rotations else 1 return images, labels, info
def _rotate_batch(batch, angle): return np.vstack([rotate(x_i.reshape(28, 28), angle).reshape([-1, 28*28]) for x_i in batch])
def random_rotate_image(image): angle = np.random.uniform(low=-10.0, high=10.0) return misc.imrotate(image, angle, 'bicubic')
def random_rotate_image(image): # rotate image for data augmentation angle = np.random.uniform(low=-20.0, high=20.0) return misc.imrotate(image, angle, 'bicubic')
def plotCluster( _x, labels, core_samples_mask, n_clusters_, f): unique_labels = set(labels) colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels))) for k, col in zip(unique_labels, colors): if k == -1: # Black used for noise. col = 'k' class_member_mask = (labels == k) xy = _x[class_member_mask & ~core_samples_mask] ax = plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=6) xy = _x[class_member_mask & core_samples_mask] ax = plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14) #plt.title('Estimated number of clusters: %d' % n_clusters_) #plt.axis('off') #misc.imrotate(ax, 270) index = f print f plt.axis('off') plt.savefig(f) image_rotate(f) plt.close() #plt.show() return
def plotCluster(_x, labels, core_samples_mask, n_clusters_, f): unique_labels = set(labels) colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels))) for k, col in zip(unique_labels, colors): if k == -1: # Black used for noise. col = 'k' class_member_mask = (labels == k) xy = _x[class_member_mask & ~core_samples_mask] ax = plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=6) xy = _x[class_member_mask & core_samples_mask] ax = plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14) #plt.title('Estimated number of clusters: %d' % n_clusters_) #plt.axis('off') #misc.imrotate(ax, 270) index = f print f plt.axis('off') plt.savefig(f) image_rotate(f) plt.close() #plt.show() return ############################################################################################## # Rotating the image, as output was comming tilted
def rotate_images(images, angle, image_size): images_list = [None] * images.shape[0] for i in range(images.shape[0]): images_list[i] = misc.imrotate(images[i,:,:,:], angle) images_rot = np.stack(images_list,axis=0) sz1 = images_rot.shape[1]/2 sz2 = image_size/2 images_crop = images_rot[:,(sz1-sz2):(sz1+sz2),(sz1-sz2):(sz1+sz2),:] return images_crop
def run_pretrained(input_state,model,action_states,gameState): print '\n\nLoading pretrained weights onto model...' model.load_weights(p.PRETRAINED_PATH) epsilon=1 while True: print 'Running pretrained model (no exploration) with weights at ', p.PRETRAINED_PATH nn_out = model.predict(input_state,batch_size=1,verbose=0) nn_action = [[0,1]] if np.argmax(nn_out) else [[1,0]] action,rand_flag = select_action(nn_action+action_states,prob=[epsilon,(1-epsilon)/2,(1-epsilon)/2]) rgbDisplay, reward, tState = gameState.frame_step(action) #grayDisplay = (np.dot(imresize(rgbDisplay, (80,80), interp='bilinear')[:,:,:3], [0.299, 0.587, 0.114])).reshape((1,1,80,80)) grayDisplay = (np.dot(np.fliplr(imrotate(imresize(rgbDisplay, (80,80), interp='bilinear'), -90))[:,:,:3], [0.299, 0.587, 0.114])).reshape((1,1,80,80)) output_state = np.append(input_state[:,1:,:,:], grayDisplay,axis=1) #############################################################################################################################################################################
def rotate_frame(frame, angle): # Perform the image rotation and update the fits header #frame[np.isnan(frame)] = 0.0 new_frame = ndimage.interpolation.rotate(frame, angle, reshape=False, order=1, mode='constant', cval=float('nan')) #new_frame = misc.imrotate(frame, angle, interp="bilinear") # Return the rotated frame return new_frame
def _rotate_batch(batch, angle): return np.vstack([rotate(x_i.reshape(28, 28), angle).reshape([-1, 28*28]) for x_i in batch]) / 255.
