def stitchWithFeature():
Stitcher.featureMethod = "surf" # "sift","surf" or "orb"
Stitcher.isGPUAvailable = True
Stitcher.searchRatio = 0.75 # 0.75 is common value for matches
Stitcher.offsetCaculate = "mode" # "mode" or "ransac"
Stitcher.offsetEvaluate = 3 # 40 menas nums of matches for mode, 4.0 menas of matches for ransac
Stitcher.roiRatio = 0.2 # roi length for stitching in first direction
Stitcher.fuseMethod = "fadeInAndFadeOut"
stitcher = Stitcher()
startTime = time.time()
# method = "featureSearchIncre"; Stitcher.direction = 1; Stitcher.directIncre = 0;
# projectAddress = "images\\iron"
# outputAddress = "result\\" + method + "\\iron" + str.capitalize(Stitcher.fuseMethod) + "\\"
# stitcher.imageSetStitchWithMutiple(projectAddress, outputAddress, 50, stitcher.calculateOffsetForFeatureSearchIncre,
# startNum=1, fileExtension="jpg", outputfileExtension="jpg")
# method = "featureSearchIncre"; Stitcher.direction = 1; Stitcher.directIncre = 1;
# projectAddress = "images\\dendriticCrystal"
# outputAddress = "result\\" + method + "\\dendriticCrystal" + str.capitalize(Stitcher.fuseMethod) + "\\"
# stitcher.imageSetStitchWithMutiple(projectAddress, outputAddress, 11, stitcher.calculateOffsetForFeatureSearchIncre,
# startNum=1, fileExtension="jpg", outputfileExtension="jpg")
# Stitcher.featureMethod = "surf"; Stitcher.searchRatio = 0.95; Stitcher.offsetEvaluate = 3;
# method = "featureSearchIncre"; Stitcher.direction = 1; Stitcher.directIncre = 1;
# Stitcher.isEnhance = True; # Stitcher.isClahe = True;
# Stitcher.fuseMethod = "notFuse"
# projectAddress = "images\\superalloyTurbineblade"
# outputAddress = "result\\" + method + "\\superalloyTurbineblade" + str.capitalize(Stitcher.fuseMethod) + "\\"
# stitcher.imageSetStitchWithMutiple(projectAddress, outputAddress, 1, stitcher.calculateOffsetForFeatureSearchIncre,
# startNum=1, fileExtension="jpg", outputfileExtension="jpg")
method = "featureSearchIncre"
Stitcher.direction = 4
Stitcher.directIncre = 0
projectAddress = "images\\zirconLarge"
outputAddress = "result\\" + method + "\\zirconLarge" + str.capitalize(
Stitcher.fuseMethod) + "\\"
stitcher.imageSetStitchWithMutiple(
projectAddress,
outputAddress,
97,
stitcher.calculateOffsetForFeatureSearchIncre,
startNum=2,
fileExtension="jpg",
outputfileExtension="png")
# method = "featureSearch"; Stitcher.direction = 4; Stitcher.directIncre = 0;
# projectAddress = "images\\zirconLargeResized_4_INTER_AREA"
# outputAddress = "result\\" + method + "\\zirconLargeResized_4_INTER_AREA" + str.capitalize(Stitcher.fuseMethod) + "\\"
# stitcher.imageSetStitchWithMutiple(projectAddress, outputAddress, 97, stitcher.calculateOffsetForFeatureSearch,
# startNum=1, fileExtension="jpg", outputfileExtension="jpg")
# method = "featureSearch"; Stitcher.direction = 4; Stitcher.directIncre = 0;
# projectAddress = "images\\zirconSmall"
# outputAddress = "result\\" + method + "\\zirconSmall" + str.capitalize(Stitcher.fuseMethod) + "\\"
# stitcher.imageSetStitchWithMutiple(projectAddress, outputAddress, 194, stitcher.calculateOffsetForFeatureSearch,
# startNum=1, fileExtension="jpg", outputfileExtension="jpg")
endTime = time.time()
print("Time Consuming = " + str(endTime - startTime))
def __init__(self):
bpy.ops.wm.open_mainfile(filepath="blend_files/player2.blend")
# has to be saved in the blender file
# check to make sure it's not set to the default
self.animation_name = bpy.data.scenes[0].name
self.stitcher = Stitcher(self.animation_name)
config = ConfigParser()
config.read('config.ini')
result = config.read('animation_config/' + self.animation_name +
".cfg")
if ''.join(result) == '':
sys.stderr.write("** could not read " + self.animation_name +
".cfg")
self.animations = {}
else:
self.animations = config._sections['animations']
self.ignored_actions = config.get('config', 'ignored_actions')
self.directions = config._sections['directions']
use_antialiasing = eval(config.get('output', 'use_antialiasing'))
self.frame_step = int(config.get('output', 'frame_step'))
self.grip = bpy.data.objects['Grip']
self.grip.rotation_mode = 'XYZ'
self.target = bpy.data.objects['Armature']
bpy.data.scenes[0].render.use_antialiasing = use_antialiasing
def imageStiching(self, img1, img2):
# 读取拼接图片
imageA = cv2.imread(img1)
imageB = cv2.imread(img2)
# cv2.imshow("Image A", imageA)
# cv2.imshow("Image B", imageB)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 把图片拼接起来
stitcher = Stitcher()
stitcher.stitch([imageA, imageB])
def stitchWithPhase():
method = "phaseCorrelate"
Stitcher.fuseMethod = "notFuse"
stitcher = Stitcher()
projectAddress = "images\\zirconSmall"
outputAddress = "result\\" + method + "\\zirconSmall" + str.capitalize(
Stitcher.fuseMethod) + "\\"
stitcher.imageSetStitch(projectAddress,
outputAddress,
51,
stitcher.calculateOffsetForPhaseCorrleate,
startNum=43,
fileExtension="jpg",
outputfileExtension="jpg")
Stitcher.phase.shutdown()
def __init__(self):
bpy.ops.wm.open_mainfile(filepath="blend_files/player2.blend")
# has to be saved in the blender file
# check to make sure it's not set to the default
self.animation_name = bpy.data.scenes[0].name
self.stitcher = Stitcher(self.animation_name)
config = ConfigParser()
config.read('config.ini')
result = config.read('animation_config/' + self.animation_name + ".cfg")
if ''.join(result) == '':
sys.stderr.write("** could not read " + self.animation_name + ".cfg")
self.animations = {}
else:
self.animations = config._sections['animations']
self.ignored_actions = config.get('config', 'ignored_actions')
self.directions = config._sections['directions']
use_antialiasing = eval(config.get('output', 'use_antialiasing'))
self.frame_step = int(config.get('output', 'frame_step'))
self.grip = bpy.data.objects['Grip']
self.grip.rotation_mode = 'XYZ'
self.target = bpy.data.objects['Armature']
bpy.data.scenes[0].render.use_antialiasing=use_antialiasing
from Stitcher import Stitcher
import cv2
# 读取拼接图片
imageA = cv2.imread("image/left_01.png")
imageB = cv2.imread("image/right_01.png")
# 把图片拼接成全景图
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB])
# 显示所有图片
cv2.imshow("Matches", vis)
cv2.imshow("Result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
class RenderScript():
def __init__(self):
bpy.ops.wm.open_mainfile(filepath="blend_files/player2.blend")
# has to be saved in the blender file
# check to make sure it's not set to the default
self.animation_name = bpy.data.scenes[0].name
self.stitcher = Stitcher(self.animation_name)
config = ConfigParser()
config.read('config.ini')
result = config.read('animation_config/' + self.animation_name + ".cfg")
if ''.join(result) == '':
sys.stderr.write("** could not read " + self.animation_name + ".cfg")
self.animations = {}
else:
self.animations = config._sections['animations']
self.ignored_actions = config.get('config', 'ignored_actions')
self.directions = config._sections['directions']
use_antialiasing = eval(config.get('output', 'use_antialiasing'))
self.frame_step = int(config.get('output', 'frame_step'))
self.grip = bpy.data.objects['Grip']
self.grip.rotation_mode = 'XYZ'
self.target = bpy.data.objects['Armature']
bpy.data.scenes[0].render.use_antialiasing=use_antialiasing
def render_position(self, action, direction, rotation, grip):
grip.rotation_euler[2] = rotation
filepath = 'tmp/' + action + '_' + direction + '_'
bpy.data.scenes[0].render.filepath = filepath
bpy.ops.render.render(animation=True)
def main(self):
start_time = time.time()
for action in bpy.data.actions.keys():
self.render(action)
self.stitcher.save()
shutil.rmtree('tmp')
end = time.time()
return end - start_time
def render(self, action):
if action in self.ignored_actions:
return
start, finish = bpy.data.actions[action].frame_range
self.target.animation_data.action = bpy.data.actions[action]
bpy.data.scenes[0].frame_start = start
bpy.data.scenes[0].frame_end = finish - 1 # finish frame should be dupe of start frame
bpy.data.scenes[0].frame_step = self.frame_step
if self.animations[action]:
keys = self.animations[action].split(",")
else:
keys = self.directions.keys()
for key in keys:
self.render_position(action, key, eval(self.directions[key]), self.grip)
self.stitcher.add_to_sheet(action, key)
class RenderScript():
def __init__(self):
bpy.ops.wm.open_mainfile(filepath="blend_files/player2.blend")
# has to be saved in the blender file
# check to make sure it's not set to the default
self.animation_name = bpy.data.scenes[0].name
self.stitcher = Stitcher(self.animation_name)
config = ConfigParser()
config.read('config.ini')
result = config.read('animation_config/' + self.animation_name +
".cfg")
if ''.join(result) == '':
sys.stderr.write("** could not read " + self.animation_name +
".cfg")
self.animations = {}
else:
self.animations = config._sections['animations']
self.ignored_actions = config.get('config', 'ignored_actions')
self.directions = config._sections['directions']
use_antialiasing = eval(config.get('output', 'use_antialiasing'))
self.frame_step = int(config.get('output', 'frame_step'))
self.grip = bpy.data.objects['Grip']
self.grip.rotation_mode = 'XYZ'
self.target = bpy.data.objects['Armature']
bpy.data.scenes[0].render.use_antialiasing = use_antialiasing
def render_position(self, action, direction, rotation, grip):
grip.rotation_euler[2] = rotation
filepath = 'tmp/' + action + '_' + direction + '_'
bpy.data.scenes[0].render.filepath = filepath
bpy.ops.render.render(animation=True)
def main(self):
start_time = time.time()
for action in bpy.data.actions.keys():
self.render(action)
self.stitcher.save()
shutil.rmtree('tmp')
end = time.time()
return end - start_time
def render(self, action):
if action in self.ignored_actions:
return
start, finish = bpy.data.actions[action].frame_range
self.target.animation_data.action = bpy.data.actions[action]
bpy.data.scenes[0].frame_start = start
bpy.data.scenes[
0].frame_end = finish - 1 # finish frame should be dupe of start frame
bpy.data.scenes[0].frame_step = self.frame_step
if self.animations[action]:
keys = self.animations[action].split(",")
else:
keys = self.directions.keys()
for key in keys:
self.render_position(action, key, eval(self.directions[key]),
self.grip)
self.stitcher.add_to_sheet(action, key)
def main():
stitcher = Stitcher()
if config_scale:
background = cv2.imread('images/background_scaled.jpg')
else:
background = cv2.imread('images/background.jpg')
transformer = Transformer(config_scale)
cap_left = cv2.VideoCapture(videos_path + videos[0])
cap_mid = cv2.VideoCapture(videos_path + videos[1])
cap_right = cv2.VideoCapture(videos_path + videos[2])
frame_width = int(cap_mid.get(cv.CV_CAP_PROP_FRAME_WIDTH))
frame_height = int(cap_mid.get(cv.CV_CAP_PROP_FRAME_HEIGHT))
frame_count = int(cap_mid.get(cv.CV_CAP_PROP_FRAME_COUNT))
init_points = {'C0': (71, 1153), \
'R0': (80, 761), 'R1': (80, 1033), 'R2': (95, 1127), 'R3': (54, 1156), 'R4': (65, 1185),
'R5': (61, 1204), 'R6': (56, 1217), 'R7': (69, 1213), 'R8': (67, 1253), 'R9': (75, 1281),
'R10': (92, 1347), \
'B0': (71, 1409), 'B1': (72, 1016), 'B2': (47, 1051), 'B3': (58, 1117), 'B4': (74, 1139),
'B5': (123, 1156), 'B6': (61, 1177), 'B7': (48, 1198), 'B8': (102, 1353)}
points = init_points.values()
tracker = Tracker(background, config_scale, init_points.values())
# cap_left.set(cv.CV_CAP_PROP_POS_FRAMES, 1400)
# cap_mid.set(cv.CV_CAP_PROP_POS_FRAMES, 1400)
# cap_right.set(cv.CV_CAP_PROP_POS_FRAMES, 1400)
for fr in range(frame_count):
print(fr)
status_left, frame_left = cap_left.read()
status_mid, frame_mid = cap_mid.read()
status_right, frame_right = cap_right.read()
scaled_size = (frame_width / image_down_scale_factor,
frame_height / image_down_scale_factor)
frame_left = cv2.resize(frame_left, scaled_size)
frame_mid = cv2.resize(frame_mid, scaled_size)
frame_right = cv2.resize(frame_right, scaled_size)
# Adjust the brightness difference.
frame_mid = cv2.convertScaleAbs(frame_mid, alpha=0.92)
if status_left and status_mid and status_right:
warped_left_mid = stitcher.stitch(frame_mid, frame_left,
H_left_mid)
warped_left_mid_right = stitcher.stitch(warped_left_mid,
frame_right, H_mid_right)
warped_left_mid_right_cropped = crop_img(warped_left_mid_right)
# plt.imshow(warped_left_mid_right_cropped)
# plt.show()
# cv2.waitKey(0)
points = tracker.tracking(warped_left_mid_right_cropped)
for i in range(len(points)):
cv2.circle(warped_left_mid_right_cropped,
(points[i][1], points[i][0]), 3, (0, 0, 255), -1)
height, width = warped_left_mid_right_cropped.shape[:2]
warped_left_mid_right_cropped = cv2.resize(
warped_left_mid_right_cropped, (width / 2, height / 2))
cv2.imshow('Objects', warped_left_mid_right_cropped)
cv2.waitKey(1)
# background = transformer.transform(points)
# plt.imshow(warped_left_mid_right_cropped)
# plt.show()
# cv2.imshow('Objects', background)
# cv2.waitKey(30)
cv2.waitKey(0)
cv2.destroyAllWindows()
cap_left.release()
cap_mid.release()
cap_right.release()
print('start')
device1_cap = cv2.VideoCapture(device1_url)#读取视频流
device2_cap = cv2.VideoCapture(device2_url)#读取视频流
while(device1_cap.isOpened() or device2_cap.isOpened()):
print('success')
if(device1_cap.isOpened()):
device1_ret, device1_frame = device1_cap.read()
cv2.imshow('frame1',device1_frame)
if(device2_cap.isOpened()):
device2_ret, device2_frame = device2_cap.read()
cv2.imshow('frame2',device2_frame)
if(device1_cap.isOpened() and device2_cap.isOpened()):
# 把图像拼接成全景图
stitcher = Stitcher()
(result, vis) = stitcher.stitch([device1_frame, device2_frame], showMatches=True)
print("panomanic mode")
cv2.imshow("Result", result)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print('end')
device1_cap.release()
device2_cap.release()
cv2.destroyAllWindows()
def main():
stitcher = Stitcher()
if config_scale:
background = cv2.imread('images/background_scaled.jpg')
else:
background = cv2.imread('images/background.jpg')
transformer = Transformer(config_scale)
cap_left = cv2.VideoCapture(videos_path + videos[0])
cap_mid = cv2.VideoCapture(videos_path + videos[1])
cap_right = cv2.VideoCapture(videos_path + videos[2])
frame_width = int(cap_mid.get(cv.CV_CAP_PROP_FRAME_WIDTH))
frame_height = int(cap_mid.get(cv.CV_CAP_PROP_FRAME_HEIGHT))
frame_count = int(cap_mid.get(cv.CV_CAP_PROP_FRAME_COUNT))
init_points = {'C0': (71, 1153), \
'R0': (80, 761), 'R1': (80, 1033), 'R2': (95, 1127), 'R3': (54, 1156), 'R4': (65, 1185),
'R5': (61, 1204), 'R6': (56, 1217), 'R7': (69, 1213), 'R8': (67, 1253), 'R9': (75, 1281),
'R10': (92, 1347), \
'B0': (71, 1409), 'B1': (72, 1016), 'B2': (47, 1051), 'B3': (58, 1117), 'B4': (74, 1139),
'B5': (123, 1156), 'B6': (61, 1177), 'B7': (48, 1198), 'B8': (102, 1353)}
points = init_points.values()
tracker = Tracker(background, config_scale, init_points.values())
# cap_left.set(cv.CV_CAP_PROP_POS_FRAMES, 1400)
# cap_mid.set(cv.CV_CAP_PROP_POS_FRAMES, 1400)
# cap_right.set(cv.CV_CAP_PROP_POS_FRAMES, 1400)
for fr in range(frame_count):
print(fr)
status_left, frame_left = cap_left.read()
status_mid, frame_mid = cap_mid.read()
status_right, frame_right = cap_right.read()
scaled_size = (frame_width / image_down_scale_factor, frame_height / image_down_scale_factor)
frame_left = cv2.resize(frame_left, scaled_size)
frame_mid = cv2.resize(frame_mid, scaled_size)
frame_right = cv2.resize(frame_right, scaled_size)
# Adjust the brightness difference.
frame_mid = cv2.convertScaleAbs(frame_mid, alpha=0.92)
if status_left and status_mid and status_right:
warped_left_mid = stitcher.stitch(frame_mid, frame_left, H_left_mid)
warped_left_mid_right = stitcher.stitch(warped_left_mid, frame_right, H_mid_right)
warped_left_mid_right_cropped = crop_img(warped_left_mid_right)
# plt.imshow(warped_left_mid_right_cropped)
# plt.show()
# cv2.waitKey(0)
points = tracker.tracking(warped_left_mid_right_cropped)
for i in range(len(points)):
cv2.circle(warped_left_mid_right_cropped, (points[i][1], points[i][0]), 3, (0, 0, 255), -1)
height, width = warped_left_mid_right_cropped.shape[:2]
warped_left_mid_right_cropped = cv2.resize(warped_left_mid_right_cropped, (width / 2, height / 2))
cv2.imshow('Objects', warped_left_mid_right_cropped)
cv2.waitKey(1)
# background = transformer.transform(points)
# plt.imshow(warped_left_mid_right_cropped)
# plt.show()
# cv2.imshow('Objects', background)
# cv2.waitKey(30)
cv2.waitKey(0)
cv2.destroyAllWindows()
cap_left.release()
cap_mid.release()
cap_right.release()
def stitchWithFeature():
Stitcher.featureMethod = "surf" # "sift","surf" or "orb"
Stitcher.isGPUAvailable = True
Stitcher.searchRatio = 0.75 # 0.75 is common value for matches
Stitcher.offsetCaculate = "mode" # "mode" or "ransac"
Stitcher.offsetEvaluate = 3 # 3 menas nums of matches for mode, 3.0 menas of matches for ransac
Stitcher.roiRatio = 0.2 # roi length for stitching in first direction
Stitcher.fuseMethod = "fadeInAndFadeOut" # "notFuse","average","maximum","minimum","fadeInAndFadeOut","trigonometric", "multiBandBlending"
stitcher = Stitcher()
Stitcher.direction = 1
Stitcher.directIncre = 0
projectAddress = "demoImages\\iron"
outputAddress = "result\\iron" + str.capitalize(Stitcher.fuseMethod) + "\\"
stitcher.imageSetStitchWithMutiple(
projectAddress,
outputAddress,
1,
stitcher.calculateOffsetForFeatureSearchIncre,
startNum=1,
fileExtension="jpg",
outputfileExtension="jpg")
Stitcher.direction = 1
Stitcher.directIncre = 1
projectAddress = "demoImages\\dendriticCrystal"
outputAddress = "result\\dendriticCrystal" + str.capitalize(
Stitcher.fuseMethod) + "\\"
stitcher.imageSetStitchWithMutiple(
projectAddress,
outputAddress,
1,
stitcher.calculateOffsetForFeatureSearchIncre,
startNum=1,
fileExtension="jpg",
outputfileExtension="jpg")
Stitcher.direction = 4
Stitcher.directIncre = 0
projectAddress = "demoImages\\zirconBSE"
outputAddress = "result\\zirconBSE" + str.capitalize(
Stitcher.fuseMethod) + "\\"
stitcher.imageSetStitchWithMutiple(
projectAddress,
outputAddress,
1,
stitcher.calculateOffsetForFeatureSearch,
startNum=1,
fileExtension="jpg",
outputfileExtension="jpg")
Stitcher.direction = 4
Stitcher.directIncre = 0
projectAddress = "demoImages\\zirconCL"
outputAddress = "result\\zirconCL" + str.capitalize(
Stitcher.fuseMethod) + "\\"
stitcher.imageSetStitchWithMutiple(
projectAddress,
outputAddress,
1,
stitcher.calculateOffsetForFeatureSearch,
startNum=1,
fileExtension="jpg",
outputfileExtension="jpg")
Stitcher.direction = 4
Stitcher.directIncre = 0
projectAddress = "demoImages\\zirconREM"
outputAddress = "result\\zirconREM" + str.capitalize(
Stitcher.fuseMethod) + "\\"
stitcher.imageSetStitchWithMutiple(
projectAddress,
outputAddress,
1,
stitcher.calculateOffsetForFeatureSearch,
startNum=1,
fileExtension="jpg",
outputfileExtension="jpg")
Stitcher.direction = 4
Stitcher.directIncre = 0
projectAddress = "demoImages\\zirconTEM"
outputAddress = "result\\zirconTEM" + str.capitalize(
Stitcher.fuseMethod) + "\\"
stitcher.imageSetStitchWithMutiple(
projectAddress,
outputAddress,
1,
stitcher.calculateOffsetForFeatureSearch,
startNum=1,
fileExtension="jpg",
outputfileExtension="jpg")
from Stitcher import Stitcher
import cv2
from os import listdir
from os.path import isfile, join
mypath = 'images'
# f = []
files = [join(mypath, f) for f in listdir(mypath) if isfile(join(mypath, f))]
files = sorted(files)
# print(files)
stitcher = Stitcher()
j = files[-1]
im1 = cv2.imread(j)
# cv2.imshow('i',im1)
for i in reversed(xrange(len(files) - 1)):
print i
im2 = cv2.imread(files[i])
# cv2.imshow('n',im2)
result = stitcher.stitch(im1, im2)
im1 = result
# im1= result[~np.all(result == 0, axis=2)]
# result = stitcher.stitch(im1,im2)
# im1= result[~np.all(result == 0, axis=2)]
# result = stitcher.stitch(im1,im2)
# initialize video streams
no_of_streams = 2
vss = [VideoStream(calibrator, src=1), VideoStream(calibrator, src=0)]
calibrator.calculate_optimal_camera_matrix(vss[0].read().shape[1],vss[0].read().shape[0])
# initialize homographies
homographies = []
for i in range(no_of_streams - 1):
homographies.append(matcher.match(vss[i+1].frame, vss[i].frame))
vss_frames_list = []
for i in range(no_of_streams):
vss_frames_list.append(vss[i].read())
stitcher = Stitcher(vss_frames_list, homographies)
vss[0].start()
time.sleep(1)
vss[1].start()
time.sleep(1)
while True:
frame1 = vss[0].frame
# print frame1
frame2 = vss[1].frame
stitcher.set_images([frame1, frame2])
stitcher.leftshift()
cv2.imshow('Result', stitcher.result)
import cv2
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--first", required=True, help="path to the first image")
ap.add_argument("-s",
"--second",
required=True,
help="path to the second image")
args = vars(ap.parse_args())
# load the two images and resize them to have a width of 400 pixels
# (for faster processing)
imageA = cv2.imread(args["first"])
imageB = cv2.imread(args["second"])
imageA = cv2.imread("IMG_0090.jpg")
imageB = cv2.imread("IMG_0091.jpg")
imageA = imutils.resize(imageA, width=400)
imageB = imutils.resize(imageB, width=400)
# stitch the images together to create a panorama
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
# show the images
cv2.imshow("Image A", imageA)
cv2.imshow("Image B", imageB)
cv2.imshow("Keypoint Matches", vis)
cv2.imshow("Result", result)
cv2.waitKey(0)
import cv2
import imutils
from Stitcher import Stitcher
import copy
import numpy as np
if __name__ == "__main__":
imageA = cv2.imread("images/random_forest2.jpg")
imageB = cv2.imread("images/random_forest1_lowexposure.jpg")
# imageA = cv2.imread("images/boat1.jpg")
# imageB = cv2.imread("images/boat2.jpg")
imageA = imutils.resize(imageA, width=2000)
imageB = imutils.resize(imageB, width=2000)
# stitch the images together to create a panorama
stitcher = Stitcher()
(result, H, vis) = stitcher.stitch(imageB, imageA, showMatches=True)
# get the wrapped image that we use for blending
wrapped_image = cv2.warpPerspective(
imageB, H, (imageA.shape[1] + imageB.shape[1], imageB.shape[0]))
src = copy.deepcopy(wrapped_image)
# convert it to grayscale image
im_bw = cv2.cvtColor(wrapped_image, cv2.COLOR_RGB2GRAY)
# threshold the image
ret, thresh_im = cv2.threshold(im_bw, 0, 255, 0)
# calculate the contours from the black and white image
_, contours, hierarchy = cv2.findContours(im_bw, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
# approximate the contour by polygon
# this polygon is used as mask for the blending
epsilon = 0.1 * cv2.arcLength(contours[0], True)
from Stitcher import Stitcher
# .pos file specifying the names of the tile images
position_list_filename = '/home/sihao/SchultzBox/Sihao/Imaging/050618Grid_stack/050618aaPositionList.pos'
# Create Stitcher object
s = Stitcher(position_list_filename)
# Stitch and save
s.stitch()
s.save('stitched.png')
# initialize video streams
no_of_streams = 2
vss = [VideoStream(calibrator, src=1), VideoStream(calibrator, src=0)]
calibrator.calculate_optimal_camera_matrix(vss[0].read().shape[1],
vss[0].read().shape[0])
# initialize homographies
homographies = []
for i in range(no_of_streams - 1):
homographies.append(matcher.match(vss[i + 1].frame, vss[i].frame))
vss_frames_list = []
for i in range(no_of_streams):
vss_frames_list.append(vss[i].read())
stitcher = Stitcher(vss_frames_list, homographies)
vss[0].start()
time.sleep(1)
vss[1].start()
time.sleep(1)
while True:
frame1 = vss[0].frame
# print frame1
frame2 = vss[1].frame
stitcher.set_images([frame1, frame2])
stitcher.leftshift()
cv2.imshow('Result', stitcher.result)
