本文整理汇总了Python中cv2.vconcat方法的典型用法代码示例。如果您正苦于以下问题：Python cv2.vconcat方法的具体用法？Python cv2.vconcat怎么用？Python cv2.vconcat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cv2的用法示例。
在下文中一共展示了cv2.vconcat方法的2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: define_new_pose_configuration
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import vconcat [as 别名]
def define_new_pose_configuration(configName, noAnimals, noBps, Imagepath, BpNameList, animalNumber):
global ix, iy
global centerCordStatus
def draw_circle(event,x,y,flags,param):
global ix,iy
global centerCordStatus
if (event == cv2.EVENT_LBUTTONDBLCLK):
if centerCordStatus == False:
cv2.circle(overlay,(x,y-sideImageHeight),10,colorList[-i],-1)
cv2.putText(overlay,str(bpNumber+1), (x+4,y-sideImageHeight), cv2.FONT_HERSHEY_SIMPLEX, 0.7, colorList[i], 2)
cv2.imshow('Define pose', overlay)
centerCordStatus = True
im = cv2.imread(Imagepath)
imHeight, imWidth = im.shape[0], im.shape[1]
if imWidth < 300:
im = imutils.resize(im, width=800)
imHeight, imWidth = im.shape[0], im.shape[1]
im = np.uint8(im)
fontScale = max(imWidth, imHeight) / (max(imWidth, imHeight) * 1.2)
cv2.namedWindow('Define pose', cv2.WINDOW_NORMAL)
overlay = im.copy()
colorList = []
for color in range(len(BpNameList)):
r, g, b = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
colorTuple = (r, g, b)
colorList.append(colorTuple)
for i in range(len(BpNameList)):
cv2.namedWindow('Define pose', cv2.WINDOW_NORMAL)
centerCordStatus = False
bpNumber = i
sideImage = np.zeros((100, imWidth, 3), np.uint8)
sideImageHeight, sideImageWidth = sideImage.shape[0], sideImage.shape[1]
cv2.putText(sideImage, 'Double left click ' + BpNameList[i] + '. Press ESC to continue.', (10, 50), cv2.FONT_HERSHEY_SIMPLEX, fontScale, colorList[i], 2)
ix, iy = -1, -1
while (1):
cv2.setMouseCallback('Define pose', draw_circle)
imageConcat = cv2.vconcat([sideImage, overlay])
cv2.imshow('Define pose', imageConcat)
k = cv2.waitKey(20) & 0xFF
if k == 27:
cv2.destroyWindow('Define pose')
break
overlay = cv2.resize(overlay, (250,300))
imagePath = os.path.join(os.getcwd(), 'pose_configurations', 'schematics')
namePath = os.path.join(os.getcwd(), 'pose_configurations', 'configuration_names', 'pose_config_names.csv')
bpPath = os.path.join(os.getcwd(), 'pose_configurations', 'bp_names', 'bp_names.csv')
noAnimalsPath = os.path.join(os.getcwd(), 'pose_configurations', 'no_animals', 'no_animals.csv')
imageNos = len(glob.glob(imagePath + '/*.png'))
newImageName = 'Picture' + str(imageNos+1) + '.png'
imageOutPath = os.path.join(imagePath, newImageName)
BpNameList = ','.join(BpNameList)
with open(namePath, 'a') as fd:
fd.write(configName + '\n')
with open(bpPath, 'a') as fd:
fd.write(BpNameList + '\n')
with open(noAnimalsPath, 'a') as fd:
fd.write(animalNumber + '\n')
cv2.imwrite(imageOutPath, overlay) 示例2: generate_training_output
# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import vconcat [as 别名]
def generate_training_output(colors_1, scaled_depth_maps_1, boundaries, intrinsic_matrices, is_hsv, epoch,
results_root):
color_inputs_cpu = colors_1.data.cpu().numpy()
pred_depths_cpu = scaled_depth_maps_1.data.cpu().numpy()
boundaries_cpu = boundaries.data.cpu().numpy()
intrinsics_cpu = intrinsic_matrices.data.cpu().numpy()
color_imgs = []
pred_depth_imgs = []
for j in range(colors_1.shape[0]):
color_img = color_inputs_cpu[j]
pred_depth_img = pred_depths_cpu[j]
color_img = np.moveaxis(color_img, source=[0, 1, 2], destination=[2, 0, 1])
color_img = color_img * 0.5 + 0.5
color_img[color_img < 0.0] = 0.0
color_img[color_img > 1.0] = 1.0
color_img = np.uint8(255 * color_img)
if is_hsv:
color_img = cv2.cvtColor(color_img, cv2.COLOR_HSV2BGR_FULL)
pred_depth_img = np.moveaxis(pred_depth_img, source=[0, 1, 2], destination=[2, 0, 1])
if j == 0:
# Write point cloud
boundary = boundaries_cpu[j]
intrinsic = intrinsics_cpu[j]
boundary = np.moveaxis(boundary, source=[0, 1, 2], destination=[2, 0, 1])
point_cloud = point_cloud_from_depth(pred_depth_img, color_img, boundary,
intrinsic,
point_cloud_downsampling=1)
write_point_cloud(
str(results_root / "point_cloud_epoch_{epoch}_index_{index}.ply".format(epoch=epoch,
index=j)),
point_cloud)
color_img = cv2.resize(color_img, dsize=(300, 300))
pred_depth_img = cv2.resize(pred_depth_img, dsize=(300, 300))
color_imgs.append(color_img)
if j == 0:
histr = cv2.calcHist([pred_depth_img], [0], None, histSize=[100], ranges=[0, 1000])
plt.plot(histr, color='b')
plt.xlim([0, 40])
plt.savefig(
str(results_root / 'generated_depth_hist_{epoch}.jpg'.format(epoch=epoch)))
plt.clf()
display_depth_img = display_depth_map(pred_depth_img)
pred_depth_imgs.append(display_depth_img)
final_color = color_imgs[0]
final_pred_depth = pred_depth_imgs[0]
for j in range(colors_1.shape[0] - 1):
final_color = cv2.hconcat((final_color, color_imgs[j + 1]))
final_pred_depth = cv2.hconcat((final_pred_depth, pred_depth_imgs[j + 1]))
final = cv2.vconcat((final_color, final_pred_depth))
cv2.imwrite(str(results_root / 'generated_mask_{epoch}.jpg'.format(epoch=epoch)),
final)