I want to warp subsections of an image to project it on a nonuniform surface. Ultimately I want to warp an image as seen HERE, kinda like was is done in HERE from THIS project.
My problem is when I apply the transformations to each subsection of the image, things just do not line up
This is my process by which I achieve the transformations and then stitch (crop and paste them together on the final image.
- Get a list of all the points
- Create a quadrilaterals Region of Interest (ROI) from set of 4 points
Those 4 points are used to Transform the image with the corresponding original 4 points. This is done using my function called perspective_transform()
a. I take the 2 set of 4 points and pass them in to M = cv2.getPerspectiveTransform(corners, newCorners)
b. Then I call: warped = cv2.warpPerspective(roi, M, (width, height))
After getting the new warped image I use mask’s to stich everything together based on the ROI it was associated with:
a. This is done by the function quadr_croped()
Initialization of screen to Get raw pixels from the screen, save it to a Numpy array
img0 = np.array(sct.grab(monitor)) clone = img0.copy() total_height, total_width, channels = img0.shape xSub =int (input("How many columns would you like to divide the screen in to? (integers only)")) ySub =int (input("How many rows would you like to divide the screen in to? (integers only)")) roi_width = float(total_width/xSub) roi_height = float(total_height/ySub) point_list = []
Third: Use 2 sets of 4 points to warp the perspective of the image
def perspective_transform(image, roi, corners, newCorners, i = -1 ):
corners = list (corners) newCorners = list (newCorners) height, width, pixType = image.shape corners = np.array([[corners[0][0],corners[0][1],corners[0][2],corners[0][3]]],np.float32) newCorners = np.array([[newCorners[0][0],newCorners[0][1],newCorners[0][2],newCorners[0][3]]],np.float32) M = cv2.getPerspectiveTransform(corners, newCorners) #warped = cv2.warpPerspective(roi, M, (width, height), flags=cv2.INTER_LINEAR) warped = cv2.warpPerspective(roi, M, (width, height)) return warped
Second: cut and Paste quadrilateral in to the main image
def quadr_croped (mainImg,image, pts, i): # example
# mask defaulting to black for 3-channel and transparent for 4-channel # (of course replace corners with yours) mask = np.zeros(image.shape, dtype=np.uint8) roi_corners = pts #np.array([[(10,10), (300,300), (10,300)]], dtype=np.int32) # fill the ROI so it doesn't get wiped out when the mask is applied channel_count = image.shape[2] # i.e. 3 or 4 depending on your image ignore_mask_color = (255,)*channel_count cv2.fillConvexPoly(mask, roi_corners, ignore_mask_color) # apply the mask masked_image = cv2.bitwise_and(image, mask) mainImg = cv2.bitwise_or(mainImg, mask) mainImg = mainImg + masked_image # cv2.imshow("debug: image, mainImg: " +str(i), mainImg) return mainImg
First: Starting Function
def draw_quadr(img1):
#set up list for ROIquadrilateral == polygon with 4 sides numb_ROI = xSub * ySub skips =int((numb_ROI-1)/xSub) numb_ROI = skips + numb_ROI quadrilateral_list.clear() for i in range(numb_ROI): if not point_list[i][0] <= point_list[(i+xSub+2)][0]: continue vert_poly = np.array([[ point_list[i], point_list[i+1], point_list[i+xSub+2], point_list[i+xSub+1] ]], dtype=np.int32) verticesPoly_old = np.array([[ H_points_list[i], H_points_list[i+1], H_points_list[i+xSub+2], H_points_list[i+xSub+1] ]], dtype=np.int32) roi = img0.copy() # cv2.imshow("debug: roi"+str(i), roi) overlay = perspective_transform( img1, roi, verticesPoly_old, vert_poly, i) img1 = quadr_croped(img1,overlay,vert_poly,i) cv2.polylines(img1,vert_poly,True,(255,255,0)) quadrilateral_list.append(vert_poly) pt1 = point_list[i] pt2 = point_list[i+xSub+2] cntPt = (int( (pt1[0]+pt2[0])/2),int((pt1[1]+pt2[1])/2) ) cv2.putText(img1,str(len(quadrilateral_list)-1),cntPt,cv2.FONT_HERSHEY_SIMPLEX, 1,(0,255,0),2,cv2.LINE_AA) #cv2.imshow(str(i), img1) return img1
PICTURE result Links
Please look at these as they show the problem really well.
Original Image with no Distortion
This image has a left offset from the center (with no y directional movement)
Results of x directional distortion image
This image has an up offset from the center (with no x directional movement)
Results of y directional distortion image
This image has an up and left offset from the center
Results of x and y directional distortion image
I am new to computer vision and stackoverflow, I hope I have included everything to help describe the problem, let me know if you need to know anything else to help