def smallest_candidates(images, best_index):
'''It is unclear whether we should do best -> others, or other -> best; or even should we use the smallest or the largest'''
stitcher = Stitcher()
imageA = images[best_index]
gray = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)
grays = []
for index, imageB in enumerate(images):
if index == best_index: continue
transformed = stitcher.stitch([imageA, imageB],
showMatches=False,
reprojThresh=4.0)
grayB = cv2.cvtColor(transformed, cv2.COLOR_BGR2GRAY)
grays.append(grayB)
total_flow = np.zeros((len(grays)), dtype=np.float)
for i in range(len(grays)):
flowB = cv2.calcOpticalFlowFarneback(grays[i], gray, None, 0.5, 5, 15,
3, 5, 1.2, 0)
total = np.mean(np.linalg.norm(flowB, axis=-1))
total_flow[i] = total
best_indexes = np.argsort(total_flow)
best_indexes[best_indexes >= best_index] += 1
return np.hstack(([best_index], best_indexes))
def smallest_flow(images):
stitcher = Stitcher()
imageA = images[0]
gray = cv2.cvtColor(imageA, cv2.COLOR_BGR2GRAY)
grays = [gray]
for imageB in images[1:]:
# stitch the images together to create a panorama
transformed = stitcher.stitch([imageA, imageB],
showMatches=False,
reprojThresh=4.0)
grayB = cv2.cvtColor(transformed, cv2.COLOR_BGR2GRAY)
grays.append(grayB)
total_flow = np.zeros((len(grays), len(grays)), dtype=np.float)
for i in range(len(grays)):
for j in range(len(grays)):
flowB = cv2.calcOpticalFlowFarneback(grays[i], grays[j], None, 0.5,
2, 15, 3, 5, 1.1,
0) #0.5, 5, 15, 3, 5, 1.2, 0)
total = np.mean(np.linalg.norm(flowB, axis=-1))
total_flow[i, j] = total
total_sum = np.sum(total_flow, axis=-1)
print(total_sum)
best_index = np.argmin(total_sum)
return best_index, total_sum[best_index]
def panorama(): #メールを送信する関数
os.chdir("/home/pi/panorama-stitching/fujii_images") #画像があるディレクトリに移動する
imageA = cv2.imread("test2.jpg")
imageB = cv2.imread("test1.jpg")
angle =270 #回転する角度（反時計回り）
stitcher = Stitcher()
(preresult, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
vis = cv2.flip(vis,1)
#cv2.imshow("Keypoint Matches1", vis)
#cv2.imshow("preResult", preresult)
cv2.imwrite("pre_result.jpg", preresult)
imageC = cv2.imread("pre_result.jpg")
imageD = cv2.imread("test3.jpg")
imageC = cv2.flip(imageC,1)
imageD = cv2.flip(imageD,1)
(result, vis2) = stitcher.stitch([imageC, imageD], showMatches=True)
#cv2.imshow("Keypoint Matches2", vis2)
result = cv2.flip(result,1)
#cv2.imshow("Result", result)
cv2.imwrite("result.jpg", result)
cv2.waitKey(0)
def stitch(imageA, imageB):
imageA = cv2.imread(imageA)
imageB = cv2.imread(imageB)
imageA = imutils.resize(imageA, width=400)
imageB = imutils.resize(imageB, width=400)
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
#crop on blank space
img = result
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cnt = contours[0]
x, y, w, h = cv2.boundingRect(cnt)
crop = img[y:y + h, x:x + w]
return (imageA, imageB, crop, vis)
finally:
print "Parameters : ", args
fp = open(args, 'r')
filenames = [each.rstrip('\r\n') for each in fp.readlines()]
print filenames
#images = [cv2.resize(cv2.imread(each),(480, 320)) for each in filenames]
images = [cv2.resize(cv2.imread(each), (480, 320)) for each in filenames]
count = len(images)
#left_list, right_list, center_im = [], [],None
# 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 = imutils.resize(imageA, width=400)
#imageB = imutils.resize(imageB, width=400)
imageA = images[0]
stitcher = Stitcher()
for imageB in images[1:]:
# stitch the images together to create a panorama
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
imageA = result
# 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)
def match_and_clean(first, second, binary, threshold, verbose, percentile):
imageA = cv2.imread(first)
max_width = imageA.shape[1]
gray = enhance(imageA, binary)
stitcher = Stitcher()
diff = np.zeros(imageA.shape[:2]).astype(np.float)
warped = [gray]
for second_file in second:
imageB = cv2.imread(second_file)
imageB = imutils.resize(imageB, width=max_width)
# stitch the images together to create a panorama
transformed = stitcher.stitch([imageA, imageB],
showMatches=False,
reprojThresh=4.0)
grayB = enhance(transformed, binary)
flow = cv2.calcOpticalFlowFarneback(gray, grayB, None, 0.5, 2, 15, 3,
5, 1.1, 0)
flowB = revert_flow(transformed, flow)
warped_gray = enhance(flowB, binary)
warped.append(warped_gray)
diff += diff_image(gray, warped_gray)
diff /= len(second)
# print(np.histogram(diff, 100))
others = np.median(np.array(warped), axis=0).astype(np.uint8)
#others = ((np.max(np.array(warped), axis=0) + np.mean(np.array(warped), axis=0)) / 2).astype(np.uint8)
others_percentile = np.percentile(np.array(warped), percentile,
axis=0).astype(np.uint8)
black, white = estimate_black_white(gray)
print('black', black, 'white', white)
# compute a threshold
if threshold == 0:
threshold = threshold_for_most_dark(diff)
mask = diff > threshold
red_mask = get_red_mask(imageA)
total_mask = np.logical_or(mask, red_mask)
black_mask = np.logical_and(total_mask, others < (int(black) + white) / 2)
white_mask = np.logical_and(total_mask, others > (int(black) + white) / 2)
print('total to remove black pixels', np.sum(black_mask), 'white pixels',
np.sum(white_mask))
corrected = np.copy(gray)
corrected[black_mask] = others[black_mask]
corrected[white_mask] = ndimage.median_filter(gray, size=20)[white_mask]
#corrected[total_mask] = ndimage.median_filter(gray, size=20)[total_mask]
#cv2.imshow('flow', draw_flow(grayB, flow))
# show the images
if verbose:
cv2.imshow("Image A", imutils.resize(imageA, width=1280))
cv2.imshow("corrected", imutils.resize(corrected, width=1280))
cv2.imshow('diff', imutils.resize(diff.astype(np.uint8), width=1280))
cv2.imshow('mask',
imutils.resize(mask.astype(np.uint8) * 255, width=1280))
cv2.imshow(
'blackmask',
imutils.resize(black_mask.astype(np.uint8) * 255, width=1280))
cv2.imshow(
'whitemask',
imutils.resize(white_mask.astype(np.uint8) * 255, width=1280))
cv2.imshow("others", imutils.resize(others, width=1280))
cv2.waitKey(0)
return corrected, others_percentile, diff
def panorama():
stitcher = Stitcher()
br = CvBridge()
rospy.init_node('panorama', anonymous=True)
# Load parameters
input_image_topic = "camera/compressed"
if rospy.has_param('~input_image_topic'):
input_image_topic = rospy.get_param('~input_image_topic')
input_image_topic = '%s/compressed' % input_image_topic
else:
rospy.logwarn(
"input image topic not provided on param 'input_image_topic'; using %s"
% input_image_topic)
output_image_topic = "panorama/compressed"
if rospy.has_param('~output_image_topic'):
output_image_topic = rospy.get_param('~output_image_topic')
output_image_topic = '%s/compressed' % output_image_topic
else:
rospy.logwarn(
"output image topic not provided on param 'output_image_topic'; using %s"
% output_image_topic)
num_segments = 2
if rospy.has_param('~num_segments'):
num_segments = rospy.get_param('~num_segments')
else:
rospy.logwarn(
"panorama number of segments not provided on param 'num_segments'; using %s"
% num_segments)
# Create publisher
panorama_publisher = rospy.Publisher(output_image_topic,
CompressedImage,
queue_size=1)
# Create panorama
rospy.loginfo("Waiting for image on topic %s" % input_image_topic)
imageA = rospy.wait_for_message(input_image_topic, CompressedImage)
imageA = br.compressed_imgmsg_to_cv2(imageA)
for n in range(1, num_segments):
# move
rospy.loginfo("Waiting for image on topic %s" % input_image_topic)
imageB = rospy.wait_for_message(input_image_topic, CompressedImage)
imageB = br.compressed_imgmsg_to_cv2(imageB)
rospy.loginfo("Stitching...")
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
imageA = result
# 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)
# Output panorama to file
cv2.imwrite("output.jpg", result)
# Publish panorama to ROS
compressed_img_message = br.cv2_to_compressed_imgmsg(result)
rospy.loginfo("Publishing panorama to topic %s" % output_image_topic)
panorama_publisher.publish(compressed_img_message)
