mirror of
https://github.com/opencv/opencv.git
synced 2024-11-28 13:10:12 +08:00
work on feature_homography.py: multiple targets
This commit is contained in:
Alexander Mordvintesv
parent
989631c5cc
commit
ffa8c32348
@ -6,6 +6,12 @@ import itertools as it
|
||||
|
||||
image_extensions = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.pbm', '.pgm', '.ppm']
|
||||
|
||||
class Bunch(object):
|
||||
def __init__(self, **kw):
|
||||
self.__dict__.update(kw)
|
||||
def __str__(self):
|
||||
return str(self.__dict__)
|
||||
|
||||
def splitfn(fn):
|
||||
path, fn = os.path.split(fn)
|
||||
name, ext = os.path.splitext(fn)
|
||||
|
||||
@ -20,7 +20,7 @@ import cv2
|
||||
import video
|
||||
import common
|
||||
from collections import namedtuple
|
||||
from common import getsize
|
||||
from common import getsize, Bunch
|
||||
|
||||
|
||||
FLANN_INDEX_KDTREE = 1
|
||||
@ -35,11 +35,11 @@ MIN_MATCH_COUNT = 10
|
||||
|
||||
ar_verts = np.float32([[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0],
|
||||
[0, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0, 1],
|
||||
[0.5, 0.5, 2]])
|
||||
[0, 0.5, 2], [1, 0.5, 2]])
|
||||
ar_edges = [(0, 1), (1, 2), (2, 3), (3, 0),
|
||||
(4, 5), (5, 6), (6, 7), (7, 4),
|
||||
(0, 4), (1, 5), (2, 6), (3, 7),
|
||||
(4, 8), (5, 8), (6, 8), (7, 8)]
|
||||
(4, 8), (5, 8), (6, 9), (7, 9), (8, 9)]
|
||||
|
||||
|
||||
|
||||
@ -53,7 +53,7 @@ class App:
|
||||
self.cap = video.create_capture(src)
|
||||
self.frame = None
|
||||
self.paused = False
|
||||
self.ref_frame = None
|
||||
self.ref_frames = []
|
||||
|
||||
self.detector = cv2.ORB( nfeatures = 1000 )
|
||||
self.matcher = cv2.FlannBasedMatcher(flann_params, {}) # bug : need to pass empty dict (#1329)
|
||||
@ -66,13 +66,19 @@ class App:
|
||||
if len(self.frame_desc) < MIN_MATCH_COUNT or len(self.frame_desc) < MIN_MATCH_COUNT:
|
||||
return
|
||||
|
||||
raw_matches = self.matcher.knnMatch(self.frame_desc, k = 2)
|
||||
p0, p1 = [], []
|
||||
for m in raw_matches:
|
||||
if len(m) == 2 and m[0].distance < m[1].distance * 0.75:
|
||||
m = m[0]
|
||||
p0.append( self.ref_points[m.trainIdx].pt ) # queryIdx
|
||||
p1.append( self.frame_points[m.queryIdx].pt )
|
||||
matches = self.matcher.knnMatch(self.frame_desc, k = 2)
|
||||
matches = [m[0] for m in matches if len(m) == 2 and m[0].distance < m[1].distance * 0.75]
|
||||
if len(matches) < MIN_MATCH_COUNT:
|
||||
return
|
||||
img_ids = [m.imgIdx for m in matches]
|
||||
match_counts = np.bincount(img_ids, minlength=len(self.ref_frames))
|
||||
bast_id = match_counts.argmax()
|
||||
if match_counts[bast_id] < MIN_MATCH_COUNT:
|
||||
return
|
||||
ref_frame = self.ref_frames[bast_id]
|
||||
matches = [m for m in matches if m.imgIdx == bast_id]
|
||||
p0 = [ref_frame.points[m.trainIdx].pt for m in matches]
|
||||
p1 = [self.frame_points[m.queryIdx].pt for m in matches]
|
||||
p0, p1 = np.float32((p0, p1))
|
||||
if len(p0) < MIN_MATCH_COUNT:
|
||||
return
|
||||
@ -82,22 +88,28 @@ class App:
|
||||
if status.sum() < MIN_MATCH_COUNT:
|
||||
return
|
||||
p0, p1 = p0[status], p1[status]
|
||||
return p0, p1, H
|
||||
return ref_frame, p0, p1, H
|
||||
|
||||
|
||||
def on_frame(self, vis):
|
||||
match = self.match_frames()
|
||||
if match is None:
|
||||
return
|
||||
|
||||
w, h = getsize(self.frame)
|
||||
p0, p1, H = match
|
||||
for (x0, y0), (x1, y1) in zip(np.int32(p0), np.int32(p1)):
|
||||
cv2.line(vis, (x0+w, y0), (x1, y1), (0, 255, 0))
|
||||
x0, y0, x1, y1 = self.ref_rect
|
||||
ref_frame, p0, p1, H = match
|
||||
vis[:h,w:] = ref_frame.frame
|
||||
draw_keypoints(vis[:,w:], ref_frame.points)
|
||||
x0, y0, x1, y1 = ref_frame.rect
|
||||
cv2.rectangle(vis, (x0+w, y0), (x1+w, y1), (0, 255, 0), 2)
|
||||
corners0 = np.float32([[x0, y0], [x1, y0], [x1, y1], [x0, y1]])
|
||||
img_corners = cv2.perspectiveTransform(corners0.reshape(1, -1, 2), H)
|
||||
cv2.polylines(vis, [np.int32(img_corners)], True, (255, 255, 255), 2)
|
||||
|
||||
for (x0, y0), (x1, y1) in zip(np.int32(p0), np.int32(p1)):
|
||||
cv2.line(vis, (x0+w, y0), (x1, y1), (0, 255, 0))
|
||||
|
||||
'''
|
||||
corners3d = np.hstack([corners0, np.zeros((4, 1), np.float32)])
|
||||
fx = 0.9
|
||||
K = np.float64([[fx*w, 0, 0.5*(w-1)],
|
||||
@ -110,21 +122,19 @@ class App:
|
||||
for i, j in ar_edges:
|
||||
(x0, y0), (x1, y1) = verts[i], verts[j]
|
||||
cv2.line(vis, (int(x0), int(y0)), (int(x1), int(y1)), (255, 255, 0), 2)
|
||||
|
||||
'''
|
||||
def on_rect(self, rect):
|
||||
x0, y0, x1, y1 = rect
|
||||
self.ref_frame = self.frame.copy()
|
||||
self.ref_rect = rect
|
||||
points, descs = [], []
|
||||
for kp, desc in zip(self.frame_points, self.frame_desc):
|
||||
x, y = kp.pt
|
||||
if x0 <= x <= x1 and y0 <= y <= y1:
|
||||
points.append(kp)
|
||||
descs.append(desc)
|
||||
self.ref_points, self.ref_descs = points, np.uint8(descs)
|
||||
|
||||
self.matcher.clear()
|
||||
self.matcher.add([self.ref_descs])
|
||||
descs = np.uint8(descs)
|
||||
frame_data = Bunch(frame = self.frame, rect=rect, points = points, descs=descs)
|
||||
self.ref_frames.append(frame_data)
|
||||
self.matcher.add([descs])
|
||||
|
||||
def run(self):
|
||||
while True:
|
||||
@ -141,14 +151,9 @@ class App:
|
||||
w, h = getsize(self.frame)
|
||||
vis = np.zeros((h, w*2, 3), np.uint8)
|
||||
vis[:h,:w] = self.frame
|
||||
if self.ref_frame is not None:
|
||||
vis[:h,w:] = self.ref_frame
|
||||
x0, y0, x1, y1 = self.ref_rect
|
||||
cv2.rectangle(vis, (x0+w, y0), (x1+w, y1), (0, 255, 0), 2)
|
||||
draw_keypoints(vis[:,w:], self.ref_points)
|
||||
draw_keypoints(vis, self.frame_points)
|
||||
|
||||
if playing and self.ref_frame is not None:
|
||||
if playing:
|
||||
self.on_frame(vis)
|
||||
|
||||
self.rect_sel.draw(vis)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user