/
example_frames.py
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/
example_frames.py
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# coding: utf-8
# In[1]:
#imports
import cv2
import numpy as np
from os import listdir
from os.path import isfile, join
import glob
import os
import time
import math
from collections import defaultdict
# In[2]:
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
def nothing(x):
pass
# In[ ]:
cv2.destroyAllWindows()
# In[3]:
visualize = True
def detect(img):
img_gs = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
img_gs = clahe.apply(img_gs)
img_gs_f = np.float32(img_gs)
dst = cv2.cornerHarris(img_gs_f,2,3,0.04)
#result is dilated for marking the corners, not important
dst = cv2.dilate(dst,None)
coords = np.transpose(np.nonzero(dst>0.01*dst.max()))
# Threshold for an optimal value, it may vary depending on the image.
#if visualize:
#img[dst>0.01*dst.max()]=[0,0,255]
#print coords
return (coords,img_gs_f, img_gs)
def dist2(a, b):
return (a[0]-b[0])*(a[0]-b[0]) + (a[1]-b[1])*(a[1]-b[1])
def subimage(image, center, ts, tc, width, height):
v_x = (tc, ts)
v_y = (-ts,tc)
s_x = center[0] - v_x[0] * (width / 2) - v_y[0] * (height / 2)
s_y = center[1] - v_x[1] * (width / 2) - v_y[1] * (height / 2)
mapping = np.array([[v_x[0],v_y[0], s_x],
[v_x[1],v_y[1], s_y]])
return cv2.warpAffine(
image,
mapping,
(int(width), int(height)),
flags=cv2.WARP_INVERSE_MAP,
borderMode=cv2.BORDER_REPLICATE)
#get image of a line between two points with given width
def subimage2(image, c1, c2, width):
w = c1[0]-c2[0]
h = c1[1]-c2[1]
center = topoint((c1+c2)/2)
c = math.sqrt(dist2(c1, c2))
ts = -w/c
tc = h/c
#print (topoint(c1), topoint(c2), center, ts, tc, w, h, c)
return subimage(image, center, ts, tc, width, c)
def circleCoords(coords, mask_zero, radius = 2, maxSize = 22):
mask = mask_zero[:,:,0].copy()
for c in coords:
cv2.circle(mask, (c[1],c[0]), radius,255,-1)
ret, markers, stats, centroids = cv2.connectedComponentsWithStats(mask)
if visualize:#for visualization only
max_ = np.amax(markers)
markers *= 255/max_
markers = markers.astype(np.uint8)
markers_col = cv2.applyColorMap(markers, cv2.COLORMAP_JET)
candidates = []
for (i,c) in enumerate(centroids):
if (stats[i,cv2.CC_STAT_WIDTH] < maxSize and stats[i,cv2.CC_STAT_HEIGHT] < maxSize and
(stats[i,cv2.CC_STAT_WIDTH] > 4*radius or stats[i,cv2.CC_STAT_HEIGHT] >4*radius)):
if visualize:
cv2.circle(markers_col, (int(c[0]),int(c[1])), maxSize/2,(0,0,255),1)
candidates += [c]
if visualize:#for visualization only
cv2.imshow('markers_col', markers_col)
return candidates
def topoint(x):
return (int(x[0]),int(x[1]))
def findGraph(candidates, img_gs, max_mean=40):
mask = np.zeros_like(img_gs)
graph = defaultdict(lambda:[])
if visualize:
cv2.imshow('clahe', img_gs)
for (i,c) in enumerate(candidates):
for j in range(i):
sub = subimage2(img_gs, c, candidates[j],10)
min_ = np.amin(sub, axis = 1)
if min_.mean() < max_mean:
if visualize:
cv2.line(mask, topoint(c), topoint(candidates[j]),255,1)
graph[i] += [j]
graph[j] += [i]
if visualize:
cv2.imshow('graph', mask)
return graph, mask
#http://stackoverflow.com/a/246063
def CrossProductZ(a,b):
return a[0] * b[1] - a[1] * b[0]
def Orientation(a, b, c):
return CrossProductZ(a, b) + CrossProductZ(b, c) + CrossProductZ(c, a)
def OrientationC(g, a, b, c):
return Orientation(g[a],g[b],g[c])
def add1(a):
return np.append(a,[1])
def findHouse(graph, mask_, img, candidates, primary=5, secondary=4, name='_X', color=(0,0,255),img_gs=None):
if visualize:
mask = mask_.copy()
for i in graph: #subgraph localization
if len(graph[i]) != 2:
continue
[a,b] = graph[i]
if len(graph[a]) != primary or len(graph[a]) != primary:
continue
ok = 1
for c in graph[a]:
if c != b and c!= i and len(graph[c]) != secondary:
ok = 0
break
if ok == 0:
continue
if OrientationC(candidates,i,a,b)<0:
(a,b) = (b,a)
for x in [a]+graph[a]:
for y in graph[x]:
if x<y:
if visualize:
cv2.line(mask, topoint(candidates[x]), topoint(candidates[y]),255,3)
cv2.line(img, topoint(candidates[x]), topoint(candidates[y]),color,1)
used = {i: 1, a:1, b:1}
x = 0
s = 0
if name == '_X':#find middle point
for j in filter(lambda x: x not in used, graph[a]):
s_ = 0
for k in graph[j]:
s_ += dist2(candidates[j],candidates[k])
if x == 0 or s_ < s:
x = j
s = s_
used[x] = 1
cd = filter(lambda x: x not in used, graph[a])
if len(cd) != 2:
#print "cd has len %d" %len(cd)
return (None, None)
[c,d] = cd
if OrientationC(candidates,x,c,d)<0:
(c,d) = (d,c)
points_imdg = [candidates[__] for __ in [a,b,c,d]]#,x,i
if name == '_X':
points_img = [candidates[__] for __ in [a,b,c,d,x]]#,x,i
new_3d = np.float32([[100,0,0],[0,0,0],[100,100,0],[0,100,0],[50,50,0]])
else:
points_img = [candidates[__] for __ in [a,b,c,d]]#,x,i
new_3d = np.float32([[100,0,0],[0,0,0],[100,100,0],[0,100,0]])
p_img = np.array(points_img, np.float32)
cv2.cornerSubPix(img_gs,p_img,(11,11),(-1,-1),criteria)
#cv2.circle(img, topoint(p_img[5]), 5, (255,0,0),-1)
cv2.circle(img, topoint(p_img[0]), 5, (0,0,255),-1)#tr
cv2.circle(img, topoint(p_img[1]), 5, (0,255,0),-1)#tl
#cv2.circle(img, topoint(p_img[4]), 5, (255,255,255),-1)
cv2.circle(img, topoint(p_img[2]), 5, (0,255,255),-1)#br
cv2.circle(img, topoint(p_img[3]), 5, (255,0,255),-1)#bl
return draw3d(img,p_img,new_3d)
#
return (None,None)
goal_3d = np.float32([[50,50,-1]])
def draw3d(img, pts, new_3d):
fx = 0.5 + cv2.getTrackbarPos('focal', 'dst') / 50.0
h, w = img.shape[:2]
K = np.float64([[fx*w, 0, 0.5*(w-1)],
[0, fx*w, 0.5*(h-1)],
[0.0,0.0, 1.0]])
dist_coef = np.zeros(4)
ret, rvec, tvec = cv2.solvePnP(new_3d, pts, K, dist_coef)
goal_3d[0,2] = -1 * cv2.getTrackbarPos('height', 'dst')
verts = cv2.projectPoints(goal_3d, rvec, tvec, K, dist_coef)[0].reshape(-1, 2)
cv2.circle(img, topoint(verts[0]), 5, (255,255,255),-1)
for p in pts:
cv2.line(img, topoint(p), topoint(verts[0]), (255,255,255),2, -1)
def detectHouse(frame, house_type='_X'):
(coords_,img_gs_f, img_gs) = detect(frame)
candidates = circleCoords(coords_, np.zeros_like(frame))
graph, mask = findGraph(candidates, img_gs,max_mean=cv2.getTrackbarPos('max_mean', 'dst'))
findHouse(graph, mask, frame, candidates, 5, 4, '_X', img_gs=img_gs)
#findHouse(graph, mask, frame, candidates, 3, 2, '_N', img_gs=img_gs)
cv2.imshow('dst',frame)
#scanned pictures example
mypath = 'frames'
cv2.namedWindow('dst')
cv2.createTrackbar('focal', 'dst', 25, 50, nothing)
cv2.createTrackbar('height', 'dst', 50, 200, nothing)
cv2.createTrackbar('max_mean', 'dst', 40, 200, nothing)
for f in glob.glob(os.path.join(mypath,'*.jpg')):
frame_orig = cv2.imread(f)
while True:
frame = frame_orig.copy()
detectHouse(frame)
key = cv2.waitKey(100)
if key & 0xFF == ord('q') or key & 0xFF == ord(' '):
break
if key & 0xFF == ord('q'):
break