Transform/warp coordinates-CodePudding

I have an array of coordinates that mark an area on the floor.

I want to generate a new array where all the coordinates are transformed, so that I get a warped array. The points should look like the following image. Please note that I want to generate the graphic using the new array. It does not exist yet. It gets generated after having to new array.

I know the distance between all coordinates if it helps. The coordinates json looks like this, where distance_to_next contains the distance to the next point in cm:

[
  {
    "x": 295,
    "y": 228,
    "distance_to_next": 200
  },
  {
    "x": 559,
    "y": 263,
    "distance_to_next": 30
  },
  {
    "x": 551,
    "y": 304,
    "distance_to_next": 50
  },
  {
    "x": 473,
    "y": 290,
    "distance_to_next": 70
  },
  {
    "x": 451,
    "y": 352,
    "distance_to_next": 150
  },
  {
    "x": 249,
    "y": 313,
    "distance_to_next": 100
  }
]

The first point is always in the top left.

I'm using python and opencv2 and I know about various functions like cv.warpAffine, cv.warpPerspective, cv.findHomography, cv.perspectiveTransform, etc, but I'm not sure which one to use here.

Can someone point me in the right direction? Am I missing something obvious?

CodePudding user response：

The points in your coordinates json do not align with the white polygon. If I use them I get the green polygon as shown below:

import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
 
# Load the image
img = cv2.imread('./input.jpg') 
 
# Create a copy of the image
img_copy = np.copy(img)
img_copy = cv2.cvtColor(img_copy,cv2.COLOR_BGR2RGB)
 
pts = np.array([[295,228],[559,263],[551,304],[473,290],[451,352],[249,313]], np.int32)
pts = pts.reshape((-1,1,2))
img_copy2 = cv2.polylines(img_copy,[pts],True,(0,255,0), thickness=3)
plt.imshow(img_copy2)

So I manually found the approx coordinates of your white polygon and overlayed them on white polygon in green. And you need the coordinates of desired polygon. I displayed them in red.

img = cv2.imread('./input.jpg') 
img_copy = np.copy(img)
img_copy = cv2.cvtColor(img_copy,cv2.COLOR_BGR2RGB)

input_pts = np.array([[300,300],[890,380],[830,600],[200,480]], np.int32)
pts = input_pts.reshape((-1,1,2))
img_copy3 = cv2.polylines(img_copy,[pts],True,(0,255,0), thickness=3)

output_pts= np.array([[300,300],[850,300],[850,520],[300,520]], np.int32)
pts = output_pts.reshape((-1,1,2))
img_copy3 = cv2.polylines(img_copy3,[pts],True,(255,0,0), thickness=3)

plt.imshow(img_copy3)

We need to use cv2.getPerspectiveTransform here. But it takes only 4 points as input, so discard 2 points.

img = cv2.imread('./input.jpg') 
img_copy = np.copy(img)
img_copy = cv2.cvtColor(img_copy,cv2.COLOR_BGR2RGB)

input_pts = np.float32(input_pts)
output_pts = np.float32(output_pts)

# Compute the perspective transform M
M = cv2.getPerspectiveTransform(input_pts,output_pts)
 
# Apply the perspective transformation to the image
out = cv2.warpPerspective(img_copy,M,(img_copy.shape[1], img_copy.shape[0]),flags=cv2.INTER_LINEAR)
 
# Display the transformed image
plt.imshow(out)