from matplotlib.patches import Circle
def vis_two(im_array, dets1, dets2, thresh=0.9):
"""Visualize detection results before and after calibration
Parameters:
----------
im_array: numpy.ndarray, shape(1, c, h, w)
test image in rgb
dets1: numpy.ndarray([[x1 y1 x2 y2 score]])
detection results before calibration
dets2: numpy.ndarray([[x1 y1 x2 y2 score]])
detection results after calibration
thresh: float
boxes with scores > thresh will be drawn in red otherwise yellow
Returns:
-------
"""
import matplotlib.pyplot as plt
import random
figure = plt.figure()
plt.subplot(121)
plt.imshow(im_array)
color = 'yellow'
for i in range(dets1.shape[0]):
bbox = dets1[i, :4]
landmarks = dets1[i, 5:]
score = dets1[i, 4]
if score > thresh:
rect = plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=0.7)
plt.gca().add_patch(rect)
landmarks = landmarks.reshape((5,2))
for j in range(5):
plt.scatter(landmarks[j,0],landmarks[j,1],c='yellow',linewidths=0.1, marker='x', s=5)
# plt.gca().text(bbox[0], bbox[1] - 2,
# '{:.3f}'.format(score),
# bbox=dict(facecolor='blue', alpha=0.5), fontsize=12, color='white')
# else:
# rect = plt.Rectangle((bbox[0], bbox[1]),
# bbox[2] - bbox[0],
# bbox[3] - bbox[1], fill=False,
# edgecolor=color, linewidth=0.5)
# plt.gca().add_patch(rect)
plt.subplot(122)
plt.imshow(im_array)
color = 'yellow'
for i in range(dets2.shape[0]):
bbox = dets2[i, :4]
landmarks = dets1[i, 5:]
score = dets2[i, 4]
if score > thresh:
rect = plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=0.7)
plt.gca().add_patch(rect)
landmarks = landmarks.reshape((5, 2))
for j in range(5):
plt.scatter(landmarks[j, 0], landmarks[j, 1], c='yellow',linewidths=0.1, marker='x', s=5)
# plt.gca().text(bbox[0], bbox[1] - 2,
# '{:.3f}'.format(score),
# bbox=dict(facecolor='blue', alpha=0.5), fontsize=12, color='white')
# else:
# rect = plt.Rectangle((bbox[0], bbox[1]),
# bbox[2] - bbox[0],
# bbox[3] - bbox[1], fill=False,
# edgecolor=color, linewidth=0.5)
# plt.gca().add_patch(rect)
plt.show()
def vis_face(im_array, dets, landmarks=None):
"""Visualize detection results before and after calibration
Parameters:
----------
im_array: numpy.ndarray, shape(1, c, h, w)
test image in rgb
dets1: numpy.ndarray([[x1 y1 x2 y2 score]])
detection results before calibration
dets2: numpy.ndarray([[x1 y1 x2 y2 score]])
detection results after calibration
thresh: float
boxes with scores > thresh will be drawn in red otherwise yellow
Returns:
-------
"""
import matplotlib.pyplot as plt
import random
import pylab
figure = pylab.figure()
# plt.subplot(121)
pylab.imshow(im_array)
figure.suptitle('DFace Detector', fontsize=20)
for i in range(dets.shape[0]):
bbox = dets[i, :4]
rect = pylab.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='yellow', linewidth=0.9)
pylab.gca().add_patch(rect)
if landmarks is not None:
for i in range(landmarks.shape[0]):
landmarks_one = landmarks[i, :]
landmarks_one = landmarks_one.reshape((5, 2))
for j in range(5):
# pylab.scatter(landmarks_one[j, 0], landmarks_one[j, 1], c='yellow', linewidths=0.1, marker='x', s=5)
cir1 = Circle(xy=(landmarks_one[j, 0], landmarks_one[j, 1]), radius=2, alpha=0.4, color="red")
pylab.gca().add_patch(cir1)
# plt.gca().text(bbox[0], bbox[1] - 2,
# '{:.3f}'.format(score),
# bbox=dict(facecolor='blue', alpha=0.5), fontsize=12, color='white')
# else:
# rect = plt.Rectangle((bbox[0], bbox[1]),
# bbox[2] - bbox[0],
# bbox[3] - bbox[1], fill=False,
# edgecolor=color, linewidth=0.5)
# plt.gca().add_patch(rect)
pylab.show()
