本文整理汇总了Python中utils.image.transform_inverse方法的典型用法代码示例。如果您正苦于以下问题:Python image.transform_inverse方法的具体用法?Python image.transform_inverse怎么用?Python image.transform_inverse使用的例子?那么, 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utils.image
的用法示例。
在下文中一共展示了image.transform_inverse方法的7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: vis_all_detection
# 需要导入模块: from utils import image [as 别名]
# 或者: from utils.image import transform_inverse [as 别名]
def vis_all_detection(im_array, detections, class_names, scale, cfg, threshold=1e-3):
"""
visualize all detections in one image
:param im_array: [b=1 c h w] in rgb
:param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ]
:param class_names: list of names in imdb
:param scale: visualize the scaled image
:return:
"""
import matplotlib.pyplot as plt
import random
im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS)
plt.imshow(im)
for j, name in enumerate(class_names):
if name == '__background__':
continue
color = (random.random(), random.random(), random.random()) # generate a random color
dets = detections[j]
for det in dets:
bbox = det[:4] * scale
score = det[-1]
if score < threshold:
continue
rect = plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor=color, linewidth=3.5)
plt.gca().add_patch(rect)
plt.gca().text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(name, score),
bbox=dict(facecolor=color, alpha=0.5), fontsize=12, color='white')
plt.show()
示例2: draw_all_detection
# 需要导入模块: from utils import image [as 别名]
# 或者: from utils.image import transform_inverse [as 别名]
def draw_all_detection(im_array, detections, class_names, scale, cfg, threshold=1e-1):
"""
visualize all detections in one image
:param im_array: [b=1 c h w] in rgb
:param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ]
:param class_names: list of names in imdb
:param scale: visualize the scaled image
:return:
"""
import cv2
import random
color_white = (255, 255, 255)
im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS)
# change to bgr
im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
for j, name in enumerate(class_names):
if name == '__background__':
continue
color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) # generate a random color
dets = detections[j]
for det in dets:
bbox = det[:4] * scale
score = det[-1]
if score < threshold:
continue
bbox = map(int, bbox)
cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=color, thickness=2)
cv2.putText(im, '%s %.3f' % (class_names[j], score), (bbox[0], bbox[1] + 10),
color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5)
return im
示例3: vis_all_detection
# 需要导入模块: from utils import image [as 别名]
# 或者: from utils.image import transform_inverse [as 别名]
def vis_all_detection(im_array, detections, class_names, scale, cfg, threshold=0.1):
"""
visualize all detections in one image
:param im_array: [b=1 c h w] in rgb
:param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ]
:param class_names: list of names in imdb
:param scale: visualize the scaled image
:return:
"""
import matplotlib.pyplot as plt
import random
im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS)
plt.imshow(im)
for j, name in enumerate(class_names):
if name == '__background__':
continue
color = (random.random(), random.random(), random.random()) # generate a random color
dets = detections[j]
for det in dets:
bbox = det[:4] * scale
score = det[-1]
if score < threshold:
continue
rect = plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor=color, linewidth=3.5)
plt.gca().add_patch(rect)
plt.gca().text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(name, score),
bbox=dict(facecolor=color, alpha=0.5), fontsize=12, color='white')
plt.show()
示例4: draw_all_detection
# 需要导入模块: from utils import image [as 别名]
# 或者: from utils.image import transform_inverse [as 别名]
def draw_all_detection(im_array, detections, class_names, scale, cfg, threshold=0.1):
"""
visualize all detections in one image
:param im_array: [b=1 c h w] in rgb
:param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ]
:param class_names: list of names in imdb
:param scale: visualize the scaled image
:return:
"""
import cv2
import random
color_white = (255, 255, 255)
im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS)
# change to bgr
im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
for j, name in enumerate(class_names):
if name == '__background__':
continue
color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) # generate a random color
dets = detections[j]
for det in dets:
bbox = det[:4] * scale
score = det[-1]
if score < threshold:
continue
bbox = map(int, bbox)
cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=color, thickness=2)
cv2.putText(im, '%s %.3f' % (class_names[j], score), (bbox[0], bbox[1] + 10),
color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5)
return im
示例5: vis_all_detection
# 需要导入模块: from utils import image [as 别名]
# 或者: from utils.image import transform_inverse [as 别名]
def vis_all_detection(im_array, detections, class_names, scale, cfg, threshold=1e-4):
"""
visualize all detections in one image
:param im_array: [b=1 c h w] in rgb
:param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ]
:param class_names: list of names in imdb
:param scale: visualize the scaled image
:return:
"""
import matplotlib.pyplot as plt
import random
im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS)
plt.imshow(im)
for j, name in enumerate(class_names):
if name == '__background__':
continue
color = (random.random(), random.random(), random.random()) # generate a random color
dets = detections[j]
for det in dets:
bbox = det[:4] * scale
score = det[-1]
if score < threshold:
continue
rect = plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor=color, linewidth=3.5)
plt.gca().add_patch(rect)
plt.gca().text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(name, score),
bbox=dict(facecolor=color, alpha=0.5), fontsize=12, color='white')
plt.show()
示例6: draw_all_poly_detection
# 需要导入模块: from utils import image [as 别名]
# 或者: from utils.image import transform_inverse [as 别名]
def draw_all_poly_detection(im_array, detections, class_names, scale, cfg, threshold=0.2):
"""
visualize all detections in one image
:param im_array: [b=1 c h w] in rgb
:param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ]
:param class_names: list of names in imdb
:param scale: visualize the scaled image
:return:
"""
import cv2
import random
color_white = (255, 255, 255)
im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS)
# change to bgr
im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
if DEBUG:
class_names = ['__background__', 'fg']
for j, name in enumerate(class_names):
if name == '__background__':
continue
color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) # generate a random color
dets = detections[j]
for det in dets:
bbox = det[:8] * scale
score = det[-1]
if score < threshold:
continue
bbox = map(int, bbox)
# draw first point
cv2.circle(im, (bbox[0], bbox[1]), 3, (0, 0, 255), -1)
for i in range(3):
cv2.line(im, (bbox[i * 2], bbox[i * 2 + 1]), (bbox[(i+1) * 2], bbox[(i+1) * 2 + 1]), color=color, thickness=2)
cv2.line(im, (bbox[6], bbox[7]), (bbox[0], bbox[1]), color=color, thickness=2)
cv2.putText(im, '%s %.3f' % (class_names[j], score), (bbox[0], bbox[1] + 10),
color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5)
return im
示例7: draw_all_poly_detection
# 需要导入模块: from utils import image [as 别名]
# 或者: from utils.image import transform_inverse [as 别名]
def draw_all_poly_detection(im_array, detections, class_names, scale, cfg, threshold=0.2):
"""
visualize all detections in one image
:param im_array: [b=1 c h w] in rgb
:param detections: [ numpy.ndarray([[x1 y1 x2 y2 score]]) for j in classes ]
:param class_names: list of names in imdb
:param scale: visualize the scaled image
:return:
"""
# pdb.set_trace()
import cv2
import random
color_white = (255, 255, 255)
im = image.transform_inverse(im_array, cfg.network.PIXEL_MEANS)
# change to bgr
im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)
for j, name in enumerate(class_names):
if name == '__background__':
continue
color = (random.randint(0, 256), random.randint(0, 256), random.randint(0, 256)) # generate a random color
try:
dets = detections[j]
except:
pdb.set_trace()
for det in dets:
bbox = det[:8] * scale
score = det[-1]
if score < threshold:
continue
bbox = map(int, bbox)
# draw first point
cv2.circle(im, (bbox[0], bbox[1]), 3, (0, 0, 255), -1)
for i in range(3):
cv2.line(im, (bbox[i * 2], bbox[i * 2 + 1]), (bbox[(i+1) * 2], bbox[(i+1) * 2 + 1]), color=color, thickness=2)
cv2.line(im, (bbox[6], bbox[7]), (bbox[0], bbox[1]), color=color, thickness=2)
cv2.putText(im, '%s %.3f' % (class_names[j], score), (bbox[0], bbox[1] + 10),
color=color_white, fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5)
return im