4.2 MiB
4.2 MiB
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import cv2
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from pathlib import Path
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img_src = "../DATASETS/hof/webcam20231103-2.png"
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img_path = Path(img_src)
output_path = img_path.with_stem(img_path.stem + '-homography').with_suffix('.txt')
Setup¶
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from os import PathLike
def render_projection(src, dst, img: PathLike, points = []):
return render_projection_frame(src, dst, cv2.imread(str(img)), points)
def render_projection_frame(src, dst, frame, points = []):
x_min = min(dst[:,0])
if x_min < 0:
dst[:,0] += x_min * -1
y_min = min(dst[:,1])
if y_min < 0:
dst[:,1] += y_min * -1
# dst[:,0] += 1500
# dst[:,1] += 600
# print("from", src)
# print("to", dst)
H, status = cv2.findHomography(src,dst)
f, axes = plt.subplots(1, 2, figsize=(16,8))
img = frame
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for i, p in enumerate(src):
cv2.circle(img, p, 3, (0,255,0), 0)
cv2.putText(img, str(i), p, cv2.FONT_HERSHEY_COMPLEX, .5, (0,255,0))
# print (H)
dst = cv2.warpPerspective(img,H,(2500,1920))
axes[0].imshow(img)
axes[1].imshow(dst)
def onclick(event):
points.append([event.xdata,event.ydata])
f.canvas.mpl_connect('button_press_event', onclick)
plt.tight_layout()
plt.show()
# display(Image.fromarray(img).resize((600,600)), Image.fromarray(dst).resize((600,600)))
return H
First recording¶
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src = np.array([[924, 341],
[988, 343],
[908,125],
[941, 404],])
dst = np.array([[2,0],
[100,0],
[105, -695],
[0, 130]])
render_projection(src, dst, img_path)
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In [8]:
src = np.array([[924, 341],
[988, 343],
[810, 330],
[620, 325],
[908,143],
[1072,616],
[941, 404],
])
dst = np.array([[0,0],
[97,0],
[-160,-33],
[-460,-33],
[105, -500],
[87, 420],
[-2, 112]
], dtype=np.float64)
H = render_projection(src, dst, img_path)
print(H)
# np.savetxt(output_path, H, delimiter=",")
# cv2.imwrite("/tmp/test.jpg", dst)
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scaled_dst = dst / 100
H = render_projection(src, scaled_dst, img_path)
print(H)
# np.savetxt(output_path, H, delimiter=",")
second recording¶
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img_src = "../DATASETS/hof/webcam20240110.png"
img_path = Path(img_src)
output_path = img_path.with_stem(img_path.stem + '-homography').with_suffix('.txt')
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src = np.array([
[954, 326], # 0
[1010, 320], # 1
[840, 325], # 2
[645, 317], # 3
[930,123], # 4
[1115,598], # 5
[970, 390], # 6
])
dst = np.array([[0,0],
[97,0],
[-160,-33],
[-460,-33],
[105, -500],
[87, 420],
[-2, 112]
], dtype=np.float64)
H = render_projection(src, dst, img_path)
print(H)
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scaled_dst = dst / 100
H = render_projection(src, scaled_dst, img_path)
print(H)
print(output_path)
# np.savetxt(output_path, H, delimiter=",")
Compare¶
Compare the different input images and homeographies.
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# cv2.perspectiveTransform(np.array([[[530, 307.56]]]),H)
coords = cv2.perspectiveTransform(np.array([[[0., 0.], [1280., 0], [0, 720], [1280, 720]]]),H)
xs = coords[0,:,0]
ys = coords[0,:,1]
minx, maxx = min(xs), max(xs)
miny, maxy = min(ys), max(ys)
print(f"{minx} < x < {maxx}")
print(f"{miny} < y < {maxy}")
Expand to multiple video files¶
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# collect all files
video_paths = list(Path('../DATASETS/hof/').glob("*.m4v"))
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for video_path in video_paths:
print(video_path)
video = cv2.VideoCapture(str(video_path))
fps = video.get(cv2.CAP_PROP_FPS)
target_frame_duration = 1./fps
if '-' in video_path.stem:
path_stem = video_path.stem[:video_path.stem.rfind('-')]
else:
path_stem = video_path.stem
path_stem += "-homography"
homography_path = video_path.with_stem(path_stem).with_suffix('.txt')
if homography_path.exists():
#print(f'Found custom homography file! Using {homography_path}')
video_H = np.loadtxt(homography_path, delimiter=',')
else:
print(f"\tNo homography for {video_path}")
_, frame = video.read()
render_projection_frame()
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