def plot_dwell_heatmap(sess, xml_dir_extention=None):
# make sure there is nothing still in the figure
plt.cla()
# get the scrolling data
data, correct_doc = get_dwell_area(sess,
xml_dir_extention=xml_dir_extention)
if len(data) == 0:
return
# load an image of the file
dst_img_path = 'parts_for_viz/resized-images/womens_suffrage_'
dst_img_path += '1_B.png' if correct_doc.find('1') != -1 else '2_A.png'
dst_img = Image.open(dst_img_path)
width, height = dst_img.size
# visualize image
plt.imshow(dst_img)
# plot data
for x_left, x_right, y_top, y_bottom in data:
# Create a Rectangle patch
rect = patches.Rectangle((x_left, y_top),
x_right - x_left,
y_bottom - y_top,
facecolor='b',
alpha=1.0 / len(data))
plt.add_patch(rect)
# scale the plot to fit over the image
plt.xlim([0, width])
plt.ylim([height, 0])
# get rid of y ticks
plt.yticks([], [])
plt.xticks([], [])
plt.savefig(sess.dir_name + os.sep + 'dwell_heatmap.png', dpi=800)
def draw_bot(self, plt, pose, color, alpha, size=0.03):
arrow = mpatches.Arrow(pose[0],
pose[1],
size * np.cos(pose[2]),
size * np.sin(pose[2]),
color=color,
width=size / 2,
alpha=alpha)
plt.add_patch(arrow)
def plot(self, mz=None, color='k', alpha=0.5, ax=None):
from matplotlib.path import Path
from matplotlib.patches import PathPatch
#TODO: allow plotting to a 2D plot?
# confine matplotlib imports to here, so this module works
# even if matplotlib is not installed (e.g. on a server)
path = self.as_poly(mz)
ply = PathPatch(Path(path), facecolor=desaturate(color, 0.5), \
alpha=alpha, lw=0)
if ax is None:
import matplotlib.pyplot as plt
plt.add_patch(ply)
else:
ax.add_patch(ply)
def plot(self, mz=None, color='k', alpha=0.5, ax=None):
from matplotlib.path import Path
from matplotlib.patches import PathPatch
# TODO: allow plotting to a 2D plot?
# confine matplotlib imports to here, so this module works
# even if matplotlib is not installed (e.g. on a server)
path = self.as_poly(mz)
ply = PathPatch(Path(path),
facecolor=desaturate(color, 0.5),
alpha=alpha,
lw=0)
if ax is None:
import matplotlib.pyplot as plt
plt.add_patch(ply)
else:
ax.add_patch(ply)
def main():
m = -1
b = 3
lower = -20
upper = 20
num_points = 25
x1 = [random.randrange(start=1, stop=9) for i in range(num_points)]
x2 = [random.randrange(start=1, stop=9) for i in range(num_points)]
#y1 = [random.randrange(start=lower,stop=np.sqrt(1-x**2)) for x in x1]
#y2 = [random.randrange(start=np.sqrt(1-x**2) ,stop=upper) for x in x2]
c = plt.Circle((0, 0), radius=5)
plt.gca()
plt.add_patch(c)
plt.axis('scaled')
plt.show()
#lt.scatter(x1,y1,c='blue')
#plt.scatter(x2,y2,c='red')
plt.show()
def plot_img_bbox(img, target, sub):
# plot the image and bboxes
# Bounding boxes are defined as follows: x-min y-min width height
plt.subplot(sub)
plt.axis('off')
plt.imshow(img)
for box in (target['boxes']):
x, y, width, height = box[0], box[1], box[2] - box[0], box[3] - box[1]
print(x, y, width, height)
rect = patches.Rectangle((x, y),
width,
height,
linewidth=2,
edgecolor='r',
facecolor='none')
# Draw the bounding box on top of the image
plt.add_patch(rect)
return sub + 1
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Rectangle
x1 = np.array([0, 0, 1, 1])
y1 = np.array([0, 1, 0, 1])
xy = (0, 0)
plt.figure()
plt.show()
plt.add_patch(
Rectangle([0, 0], 0, 0, facecolor='None', edgecolor='black',
linewidth=1.0))
def main():
anno_classes = ['alb', 'bet', 'dol', 'lag', 'other', 'shark', 'yft']
path = os.getcwd()
download_annotations(path)
# //get a list of all images and coordinates from
bb_json = {}
for c in anno_classes:
if c == 'other': continue # no annotation file for "other" class
f = '{}/annos/{}_labels.json'.format(path, c)
j = json.load(open(f, 'r'))
for l in j:
if 'annotations' in l.keys() and len(l['annotations']) > 0:
bb_json[l['filename'].split('/')[-1]] = sorted(
l['annotations'],
key=lambda x: x['height'] * x['width'])[-1]
print(bb_json['img_04908.jpg'])
files = []
files_and_sizes = []
import settings
data_dir = os.path.join(path, settings.DATA)
files_and_sizes.extend(
get_files_and_sizes(data_dir, settings.TRAIN_FOLDER_NAME))
files_and_sizes.extend(
get_files_and_sizes(data_dir, settings.VALIDATE_FOLDER_NAME))
files_and_sizes.extend(
get_files_and_sizes(data_dir, settings.TEST_FOLDER_NAME))
# raw_filenames = [f[0].split('/')[-1] for f in dirs_and_files]
empty_bbox = {'height': 0., 'width': 0., 'x': 0., 'y': 0.}
# if we have a file that is not annotated then annotate it with an empty box
for entry in files_and_sizes:
if not entry[0] in bb_json.keys():
bb_json[entry[0]] = empty_bbox
# now lets scale all images in data folders to 224x224
for entry in files_and_sizes:
scale_boundingbox(bb_json, entry[0], entry[1])
bb = bb_json[entry[0]]
#display a few images and bounding boxes
for i in range(10):
fn = files_and_sizes[i][0]
fqn = files_and_sizes[i][2]
bb = bb_json[fn]
img = mpimg.imread(fqn)
plt.imshow(img)
# Create a Rectangle patch
rect = patches.Rectangle((bb[0], bb[1]),
bb[2],
bb[3],
linewidth=1,
edgecolor='r',
facecolor='none')
plt.add_patch(rect)
