本文整理汇总了Python中matplotlib.pyplot.imshow方法的典型用法代码示例。如果您正苦于以下问题:Python pyplot.imshow方法的具体用法?Python pyplot.imshow怎么用?Python pyplot.imshow使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类matplotlib.pyplot
的用法示例。
在下文中一共展示了pyplot.imshow方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: save_frames
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def save_frames(images, filename):
num_sequences, n_steps, w, h = images.shape
fig = plt.figure()
im = plt.imshow(combine_multiple_img(images[:, 0]), cmap=plt.cm.get_cmap('Greys'), interpolation='none')
plt.axis('image')
def updatefig(*args):
im.set_array(combine_multiple_img(images[:, args[0]]))
return im,
ani = animation.FuncAnimation(fig, updatefig, interval=500, frames=n_steps)
# Either avconv or ffmpeg need to be installed in the system to produce the videos!
try:
writer = animation.writers['avconv']
except KeyError:
writer = animation.writers['ffmpeg']
writer = writer(fps=3)
ani.save(filename, writer=writer)
plt.close(fig)
示例2: show_result_pyplot
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def show_result_pyplot(model, img, result, score_thr=0.3, fig_size=(15, 10)):
"""Visualize the detection results on the image.
Args:
model (nn.Module): The loaded detector.
img (str or np.ndarray): Image filename or loaded image.
result (tuple[list] or list): The detection result, can be either
(bbox, segm) or just bbox.
score_thr (float): The threshold to visualize the bboxes and masks.
fig_size (tuple): Figure size of the pyplot figure.
"""
if hasattr(model, 'module'):
model = model.module
img = model.show_result(img, result, score_thr=score_thr, show=False)
plt.figure(figsize=fig_size)
plt.imshow(mmcv.bgr2rgb(img))
plt.show()
示例3: plot_n_image
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def plot_n_image(X, n):
""" plot first n images
n has to be a square number
"""
pic_size = int(np.sqrt(X.shape[1]))
grid_size = int(np.sqrt(n))
first_n_images = X[:n, :]
fig, ax_array = plt.subplots(nrows=grid_size, ncols=grid_size,
sharey=True, sharex=True, figsize=(8, 8))
for r in range(grid_size):
for c in range(grid_size):
ax_array[r, c].imshow(first_n_images[grid_size * r + c].reshape((pic_size, pic_size)))
plt.xticks(np.array([]))
plt.yticks(np.array([]))
示例4: visualize_sampling
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def visualize_sampling(self,permutations):
max_length = len(permutations[0])
grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0
transposed_permutations = np.transpose(permutations)
for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t
city_indices, counts = np.unique(cities_t,return_counts=True,axis=0)
for u,v in zip(city_indices, counts):
grid[t][u]+=v # update grid with counts from the batch of permutations
# plot heatmap
fig = plt.figure()
rcParams.update({'font.size': 22})
ax = fig.add_subplot(1,1,1)
ax.set_aspect('equal')
plt.imshow(grid, interpolation='nearest', cmap='gray')
plt.colorbar()
plt.title('Sampled permutations')
plt.ylabel('Time t')
plt.xlabel('City i')
plt.show()
# Heatmap of attention (x=cities; y=steps)
示例5: visualize_sampling
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def visualize_sampling(self, permutations):
max_length = len(permutations[0])
grid = np.zeros([max_length,max_length]) # initialize heatmap grid to 0
transposed_permutations = np.transpose(permutations)
for t, cities_t in enumerate(transposed_permutations): # step t, cities chosen at step t
city_indices, counts = np.unique(cities_t,return_counts=True,axis=0)
for u,v in zip(city_indices, counts):
grid[t][u]+=v # update grid with counts from the batch of permutations
# plot heatmap
fig = plt.figure()
rcParams.update({'font.size': 22})
ax = fig.add_subplot(1,1,1)
ax.set_aspect('equal')
plt.imshow(grid, interpolation='nearest', cmap='gray')
plt.colorbar()
plt.title('Sampled permutations')
plt.ylabel('Time t')
plt.xlabel('City i')
plt.show()
示例6: plot_some_results
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def plot_some_results(pred_fn, test_generator, n_images=10):
fig_ctr = 0
for data, seg in test_generator:
res = pred_fn(data)
for d, s, r in zip(data, seg, res):
plt.figure(figsize=(12, 6))
plt.subplot(1, 3, 1)
plt.imshow(d.transpose(1,2,0))
plt.title("input patch")
plt.subplot(1, 3, 2)
plt.imshow(s[0])
plt.title("ground truth")
plt.subplot(1, 3, 3)
plt.imshow(r)
plt.title("segmentation")
plt.savefig("road_segmentation_result_%03.0f.png"%fig_ctr)
plt.close()
fig_ctr += 1
if fig_ctr > n_images:
break
示例7: classify
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def classify(self, features, show=False):
recs, _ = features.shape
result_shape = (features.shape[0], len(self.root))
scores = np.zeros(result_shape)
print scores.shape
R = Record(np.arange(recs, dtype=int), features)
for i, T in enumerate(self.root):
for idxs, result in classify(T, R):
for idx in idxs.indexes():
scores[idx, i] = float(result[0]) / sum(result.values())
if show:
plt.cla()
plt.clf()
plt.close()
plt.imshow(scores, cmap=plt.cm.gray)
plt.title('Scores matrix')
plt.savefig(r"../scratch/tree_scores.png", bbox_inches='tight')
return scores
示例8: preprocess_image
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def preprocess_image(img_path):
img = io.imread(img_path)
if np.max(img.shape[:2]) != config.img_size:
print('Resizing so the max image size is %d..' % config.img_size)
scale = (float(config.img_size) / np.max(img.shape[:2]))
else:
scale = 1.0#scaling_factor
center = np.round(np.array(img.shape[:2]) / 2).astype(int)
# image center in (x,y)
center = center[::-1]
crop, proc_param = img_util.scale_and_crop(img, scale, center,
config.img_size)
# import ipdb; ipdb.set_trace()
# Normalize image to [-1, 1]
# plt.imshow(crop/255.0)
# plt.show()
crop = 2 * ((crop / 255.) - 0.5)
return crop, proc_param, img
示例9: plot_attention
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def plot_attention(sentences, attentions, labels, **kwargs):
fig, ax = plt.subplots(**kwargs)
im = ax.imshow(attentions, interpolation='nearest',
vmin=attentions.min(), vmax=attentions.max())
plt.colorbar(im, shrink=0.5, ticks=[0, 1])
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
ax.set_yticks(range(len(labels)))
ax.set_yticklabels(labels, fontproperties=getChineseFont())
# Loop over data dimensions and create text annotations.
for i in range(attentions.shape[0]):
for j in range(attentions.shape[1]):
text = ax.text(j, i, sentences[i][j],
ha="center", va="center", color="b", size=10,
fontproperties=getChineseFont())
ax.set_title("Attention Visual")
fig.tight_layout()
plt.show()
示例10: show_landmarks
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def show_landmarks(image, heatmap, gt_landmarks, gt_heatmap):
"""Show image with pred_landmarks"""
pred_landmarks = []
pred_landmarks, _ = get_preds_fromhm(torch.from_numpy(heatmap).unsqueeze(0))
pred_landmarks = pred_landmarks.squeeze()*4
# pred_landmarks2 = get_preds_fromhm2(heatmap)
heatmap = np.max(gt_heatmap, axis=0)
heatmap = heatmap / np.max(heatmap)
# image = ski_transform.resize(image, (64, 64))*255
image = image.astype(np.uint8)
heatmap = np.max(gt_heatmap, axis=0)
heatmap = ski_transform.resize(heatmap, (image.shape[0], image.shape[1]))
heatmap *= 255
heatmap = heatmap.astype(np.uint8)
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
plt.imshow(image)
plt.scatter(gt_landmarks[:, 0], gt_landmarks[:, 1], s=0.5, marker='.', c='g')
plt.scatter(pred_landmarks[:, 0], pred_landmarks[:, 1], s=0.5, marker='.', c='r')
plt.pause(0.001) # pause a bit so that plots are updated
示例11: test
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def test(self):
list_ = os.listdir("./maps/val/")
nums_file = list_.__len__()
saver = tf.train.Saver(tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, "generator"))
saver.restore(self.sess, "./save_para/model.ckpt")
rand_select = np.random.randint(0, nums_file)
INPUTS_CONDITION = np.zeros([1, self.img_h, self.img_w, 3])
INPUTS = np.zeros([1, self.img_h, self.img_w, 3])
img = np.array(Image.open(self.path + list_[rand_select]))
img_h, img_w = img.shape[0], img.shape[1]
INPUTS_CONDITION[0] = misc.imresize(img[:, img_w//2:], [self.img_h, self.img_w]) / 127.5 - 1.0
INPUTS[0] = misc.imresize(img[:, :img_w//2], [self.img_h, self.img_w]) / 127.5 - 1.0
[fake_img] = self.sess.run([self.inputs_fake], feed_dict={self.inputs_condition: INPUTS_CONDITION})
out_img = np.concatenate((INPUTS_CONDITION[0], fake_img[0], INPUTS[0]), axis=1)
Image.fromarray(np.uint8((out_img + 1.0)*127.5)).save("./results/1.jpg")
plt.imshow(np.uint8((out_img + 1.0)*127.5))
plt.grid("off")
plt.axis("off")
plt.show()
示例12: generate_png_chess_dp_vertex
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def generate_png_chess_dp_vertex(self):
"""Produces pictures of the dominant product vertex a chessboard convention"""
import matplotlib.pylab as plt
plt.ioff()
dab2v = self.get_dp_vertex_doubly_sparse()
for i, ab in enumerate(dab2v):
fname = "chess-v-{:06d}.png".format(i)
print('Matrix No.#{}, Size: {}, Type: {}'.format(i+1, ab.shape, type(ab)), fname)
if type(ab) != 'numpy.ndarray': ab = ab.toarray()
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.set_aspect('equal')
plt.imshow(ab, interpolation='nearest', cmap=plt.cm.ocean)
plt.colorbar()
plt.savefig(fname)
plt.close(fig)
示例13: save_movie_to_frame
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def save_movie_to_frame(images, filename, idx=0, cmap='Blues'):
# Collect to single image
image = movie_to_frame(images[idx])
# Flip it
# image = np.fliplr(image)
# image = np.flipud(image)
f = plt.figure(figsize=[12, 12])
plt.imshow(image, cmap=plt.cm.get_cmap(cmap), interpolation='none', vmin=0, vmax=1)
plt.axis('image')
plt.xticks([])
plt.yticks([])
plt.savefig(filename, format='png', bbox_inches='tight', dpi=80)
plt.close(f)
示例14: save_movies_to_frame
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def save_movies_to_frame(images, filename, cmap='Blues'):
# Binarize images
# images[images > 0] = 1.
# Grid images
images = np.swapaxes(images, 1, 0)
images = np.array([combine_multiple_img(image) for image in images])
# Collect to single image
image = movie_to_frame(images)
f = plt.figure(figsize=[12, 12])
plt.imshow(image, cmap=plt.cm.get_cmap(cmap), interpolation='none', vmin=0, vmax=1)
plt.axis('image')
plt.savefig(filename, format='png', bbox_inches='tight', dpi=80)
plt.close(f)
示例15: test_interpolate_grid_const_nn
# 需要导入模块: from matplotlib import pyplot [as 别名]
# 或者: from matplotlib.pyplot import imshow [as 别名]
def test_interpolate_grid_const_nn(self, sphere3_msh):
data = sphere3_msh.elm.tag1
f = mesh_io.ElementData(data, mesh=sphere3_msh)
n = (200, 10, 1)
affine = np.array([[1, 0, 0, -100.5],
[0, 1, 0, -5],
[0, 0, 1, 0],
[0, 0, 0, 1]], dtype=float)
interp = f.interpolate_to_grid(n, affine, method='assign')
'''
import matplotlib.pyplot as plt
plt.imshow(np.squeeze(interp))
plt.colorbar()
plt.show()
assert False
'''
assert np.isclose(interp[100, 5, 0], 3)
assert np.isclose(interp[187, 5, 0], 4)
assert np.isclose(interp[193, 5, 0], 5)
assert np.isclose(interp[198, 5, 0], 0)