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homo.py
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homo.py
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import cv2
import numpy as np
import cv2.cv as cv
import utils
import math
from numpy import linalg
def findDimensions( image, homography):
base_p1 = np.ones(3, np.float32)
base_p2 = np.ones(3, np.float32)
base_p3 = np.ones(3, np.float32)
base_p4 = np.ones(3, np.float32)
(y, x) = image.shape[:2]
base_p1[:2] = [0,0]
base_p2[:2] = [x,0]
base_p3[:2] = [0,y]
base_p4[:2] = [x,y]
max_x = None
max_y = None
min_x = None
min_y = None
for pt in [base_p1, base_p2, base_p3, base_p4]:
hp = np.matrix(homography, np.float32) * np.matrix(pt, np.float32).T
hp_arr = np.array(hp, np.float32)
normal_pt = np.array([hp_arr[0]/hp_arr[2], hp_arr[1]/hp_arr[2]], np.float32)
if ( max_x == None or normal_pt[0,0] > max_x ):
max_x = normal_pt[0,0]
if ( max_y == None or normal_pt[1,0] > max_y ):
max_y = normal_pt[1,0]
if ( min_x == None or normal_pt[0,0] < min_x ):
min_x = normal_pt[0,0]
if ( min_y == None or normal_pt[1,0] < min_y ):
min_y = normal_pt[1,0]
min_x = min(0, min_x)
min_y = min(0, min_y)
return (min_x, min_y, max_x, max_y)
def stitch(base_frame,to_be_stitched_frame,H,status):
inlierRatio = float(np.sum(status)) / float(len(status))
H = H / H[2,2]
H_inv = linalg.inv(H)
if ( inlierRatio > 0.1 ):
(min_x, min_y, max_x, max_y)=findDimensions(to_be_stitched_frame, H_inv)
# Adjust max_x and max_y by base img size
max_x = max(max_x, base_frame.shape[1])
max_y = max(max_y, base_frame.shape[0])
move_h = np.matrix(np.identity(3), np.float32)
if ( min_x < 0 ):
move_h[0,2] += -min_x
max_x += -min_x
if ( min_y < 0 ):
move_h[1,2] += -min_y
max_y += -min_y
#print "Inverse Homography: \n", H_inv
#print "Min Points: ", (min_x, min_y)
mod_inv_h = move_h * H_inv
img_w = int(math.ceil(max_x))
img_h = int(math.ceil(max_y))
#print "New Dimensions: ", (img_w, img_h)
# stitch
base_img_warp = cv2.warpPerspective(base_frame, move_h, (img_w, img_h))
to_be_stitched_warp = cv2.warpPerspective(to_be_stitched_frame, mod_inv_h, (img_w, img_h))
enlarged_base_img = np.zeros((img_h, img_w, 3), np.uint8)
#print "Enlarged Image Shape: ", enlarged_base_img.shape
#print "Base Image Shape: ", base_frame.shape
#print "Base Image Warp Shape: ", base_frame.shape
(ret,data_map) = cv2.threshold(cv2.cvtColor(to_be_stitched_warp, cv2. COLOR_BGR2GRAY), 0, 255, cv2.THRESH_BINARY)
enlarged_base_img = cv2.add(enlarged_base_img, base_img_warp, mask=np.bitwise_not(data_map), dtype=cv2.CV_8U)
# Now add the warped image
final_img = cv2.add(enlarged_base_img, to_be_stitched_warp, dtype=cv2.CV_8U)
# Crop off the black edges
final_gray = cv2.cvtColor(final_img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(final_gray, 1, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
#print "Found %d contours..." % (len(contours))
max_area = 0
best_rect = (0,0,0,0)
for cnt in contours:
x,y,w,h = cv2.boundingRect(cnt)
# print "Bounding Rectangle: ", (x,y,w,h)
deltaHeight = h-y
deltaWidth = w-x
area = deltaHeight * deltaWidth
if ( area > max_area and deltaHeight > 0 and deltaWidth > 0):
max_area = area
best_rect = (x,y,w,h)
if ( max_area > 0 ):
#print "Maximum Contour: ", max_area
#print "Best Rectangle: ", best_rect
final_img_crop = final_img[best_rect[1]:best_rect[1]+best_rect[3],best_rect[0]:best_rect[0]+best_rect[2]]
return final_img_crop
left_cap = cv2.VideoCapture("football_left.mp4")
mid_cap = cv2.VideoCapture("football_mid.mp4")
right_cap = cv2.VideoCapture("football_right.mp4")
width= mid_cap.get(cv.CV_CAP_PROP_FRAME_HEIGHT)
height= mid_cap.get(cv.CV_CAP_PROP_FRAME_WIDTH)
fps=mid_cap.get(cv.CV_CAP_PROP_FPS)
frameCount= mid_cap.get(cv.CV_CAP_PROP_FRAME_COUNT)
print "height=",height
print "width=",width
print "fps",fps
print "framecount=",frameCount
print left_cap.get(cv.CV_CAP_PROP_FOURCC)
#CV_FOURCC('M','S','V','C')
#828601953
#video =cv2.VideoWriter("Stitched_video3.avi", -1, 1, (2409,615))
for iterate in range (0,1005):
# Capture frame-by-frame
ret1, left_frame = left_cap.read()
ret2, mid_frame = mid_cap.read()
ret3, right_frame = right_cap.read()
#while(1):
for iterate in range (0,1):
# Capture frame-by-frame
ret1, left_frame = left_cap.read()
ret2, mid_frame = mid_cap.read()
ret3, right_frame = right_cap.read()
# Get gray scale Image
left_gray = cv2.cvtColor(left_frame, cv2.COLOR_BGR2GRAY)
mid_gray = cv2.cvtColor(mid_frame, cv2.COLOR_BGR2GRAY)
right_gray = cv2.cvtColor(right_frame, cv2.COLOR_BGR2GRAY)
#Get Gaussian blur
left_blur = cv2.GaussianBlur(left_gray, (5,5), 0)
mid_blur = cv2.GaussianBlur(mid_gray, (5,5), 0)
right_blur = cv2.GaussianBlur(right_gray, (5,5), 0)
# Use the SIFT feature detector
detector = cv2.SIFT()
# Find key points in base image for motion estimation
left_features, left_descs = detector.detectAndCompute(left_blur, None)
mid_features, mid_descs = detector.detectAndCompute(mid_blur, None)
right_features, right_descs = detector.detectAndCompute(right_blur, None)
left_points = []
mid_points = []
right_points = []
for kp in left_features:
left_points.append((int(kp.pt[0]),int(kp.pt[1])))
for kp in mid_features:
mid_points.append((int(kp.pt[0]),int(kp.pt[1])))
for kp in right_features:
right_points.append((int(kp.pt[0]),int(kp.pt[1])))
# Parameters for nearest-neighbor matching
FLANN_INDEX_KDTREE = 1
flann_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
matcher = cv2.FlannBasedMatcher(flann_params, {})
#find left to mid first
left_to_mid_matches = matcher.knnMatch(left_descs, trainDescriptors=mid_descs, k=2)
#print "Left-to-Mid Match Count: ", len(left_to_mid_matches)
#find right to mid next
right_to_mid_matches = matcher.knnMatch(right_descs, trainDescriptors=mid_descs, k=2)
#print "Right-to-Mid Match Count: ", len(right_to_mid_matches)
left_to_mid_filtered_matches = []
right_to_mid_filtered_matches = []
ratio = 0.75
for m in left_to_mid_matches:
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
left_to_mid_filtered_matches.append(m[0])
left_to_mid_matches_subset = left_to_mid_filtered_matches
for m in right_to_mid_matches:
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
right_to_mid_filtered_matches.append(m[0])
right_to_mid_matches_subset = right_to_mid_filtered_matches
#print "Left-to-Mid Filtered Match Count: ", len(left_to_mid_matches_subset)
#print "Right-to-Mid Filtered Match Count: ", len(right_to_mid_matches_subset)
left_to_mid_sumDistance = 0.0
for match in left_to_mid_matches_subset:
left_to_mid_sumDistance += match.distance
left_to_mid_distance=left_to_mid_sumDistance
right_to_mid_sumDistance = 0.0
for match in right_to_mid_matches_subset:
right_to_mid_sumDistance += match.distance
right_to_mid_distance=right_to_mid_sumDistance
#print "Left-to-Mid Distance: ", left_to_mid_distance
#print "right-to-Mid Distance: ", right_to_mid_distance
left_to_mid_averagePointDistance = left_to_mid_distance/float(len(left_to_mid_matches_subset))
right_to_mid_averagePointDistance = right_to_mid_distance/float(len(right_to_mid_matches_subset))
#print "Left-to-Mid Average Distance: ", left_to_mid_averagePointDistance
#print "Right-to-Mid Average Distance: ", right_to_mid_averagePointDistance
left_kp = []
mid_kp = []
#print "left_features size: ", len(left_features)
#print "mid_features size: ", len(mid_features)
#print "right_features size: ", len(right_features)
for match in left_to_mid_matches_subset:
mid_kp.append(mid_features[match.trainIdx])
left_kp.append(left_features[match.queryIdx])
p1 = np.array([k.pt for k in mid_kp])
p2 = np.array([k.pt for k in left_kp])
H_left, status_left = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
left_half_frame=stitch(mid_frame,left_frame,H_left,status_left)
#cv2.imwrite("left.jpg", left_half_frame)
mid2_kp = []
right_kp = []
for match in right_to_mid_matches_subset:
mid2_kp.append(mid_features[match.trainIdx])
right_kp.append(right_features[match.queryIdx])
p1 = np.array([k.pt for k in mid2_kp])
p2 = np.array([k.pt for k in right_kp])
H_right, status_right = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
right_half_frame=stitch(mid_frame,right_frame,H_right,status_right)
#cv2.imwrite("right.jpg", right_half_frame)
#utils.showImage(right_half_frame, scale=(0.2, 0.2), timeout=0)
#utils.showImage(left_half_frame, scale=(0.2, 0.2), timeout=0)
#new_gray = cv2.cvtColor(left_half_frame, cv2.COLOR_BGR2GRAY)
#new_blur = cv2.GaussianBlur(new_gray, (5,5), 0)
left_src = left_half_frame
right_src = right_half_frame
left_small_image = cv2.resize(left_src, (0,0), fx=0.2, fy=0.2)
right_small_image = cv2.resize(right_src, (0,0), fx=0.2, fy=0.2)
#cv2.imwrite("reducedleft.jpg", left_small_image)
#cv2.imwrite("reducedright.jpg", right_small_image)
# Use the SIFT feature detector
detector2 = cv2.SIFT()
# Find key points in base image for motion estimation
new_left_gray = cv2.cvtColor(left_small_image, cv2.COLOR_BGR2GRAY)
new_right_gray = cv2.cvtColor(right_small_image, cv2.COLOR_BGR2GRAY)
#blur image
new_left_blur = cv2.GaussianBlur(new_left_gray, (5,5), 0)
new_right_blur = cv2.GaussianBlur(new_right_gray, (5,5), 0)
# Find key points in base image for motion estimation
new_left_features, new_left_descs = detector.detectAndCompute(new_left_blur, None)
new_right_features, new_right_descs = detector.detectAndCompute(new_right_blur, None)
left_to_right_matches = matcher.knnMatch(new_left_descs, trainDescriptors=new_right_descs, k=2)
new_left_kp = []
new_right_kp = []
left_to_right_filtered_matches = []
ratio = 0.75
for m in left_to_right_matches:
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
left_to_right_filtered_matches.append(m[0])
left_to_right_matches_subset = left_to_right_filtered_matches
for match in left_to_right_matches_subset:
new_right_kp.append(new_right_features[match.trainIdx])
new_left_kp.append(new_left_features[match.queryIdx])
p1 = np.array([k.pt for k in new_left_kp])
p2 = np.array([k.pt for k in new_right_kp])
H_final, status_final = cv2.findHomography(p1, p2, cv2.RANSAC, 5.0)
#print '%d / %d inliers/matched' % (np.sum(status), len(status))
final_frame=stitch(left_small_image,right_small_image,H_final,status_final)
#video.write(final_frame)
cv2.imwrite("final"+str(iterate)+".jpg", final_frame)
finalheight=len(final_frame)
finalwidth=len(final_frame[0])
print "height", len(final_frame)
print "width", len(final_frame[0])
utils.showImage(final_frame, scale=(0.5, 0.5), timeout=0)
cv2.waitKey(1)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
#detector=cv2.SIFT()
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video =cv2.VideoWriter("Stitched_video3.avi", -1, 24, (finalwidth,finalheight))
left_cap = cv2.VideoCapture("football_left.mp4")
mid_cap = cv2.VideoCapture("football_mid.mp4")
right_cap = cv2.VideoCapture("football_right.mp4")
for iterate in range (0,7200):
# Capture frame-by-frame
ret1, left_frame = left_cap.read()
ret2, mid_frame = mid_cap.read()
ret3, right_frame = right_cap.read()
left_half_frame=stitch(mid_frame,left_frame,H_left,status_left)
right_half_frame=stitch(mid_frame,right_frame,H_right,status_right)
left_src = left_half_frame
right_src = right_half_frame
left_small_image = cv2.resize(left_src, (0,0), fx=0.2, fy=0.2)
right_small_image = cv2.resize(right_src, (0,0), fx=0.2, fy=0.2)
final_frame=(finalwidth,finalheight)
final_frame=stitch(left_small_image,right_small_image,H_final,status_final)
print "height", len(final_frame)
print "width", len(final_frame[0])
video.write(final_frame)
print "pass",iterate
#release capture
#cap1.release()
print "End"
video.release()
cv2.destroyAllWindows()