We implement a sophisticated, end-to-end Kornia tutorial and show how trendy, differentiable laptop imaginative and prescient may be constructed solely in PyTorch. We begin by establishing GPU-accelerated, synchronized augmentation pipelines for pictures, masks, and keypoints, then transfer into differentiable geometry by optimizing a homography immediately by way of gradient descent. We additionally present how realized characteristic matching with LoFTR integrates with Kornia’s RANSAC to estimate strong homographies and produce a easy stitched output, even beneath constrained or offline-safe circumstances. Lastly, we floor these concepts in apply by coaching a light-weight CNN on CIFAR-10 utilizing Kornia’s GPU augmentations, highlighting how research-grade imaginative and prescient pipelines translate naturally into studying methods. Take a look at the FULL CODES right here.
import os, math, time, random, urllib.request
from dataclasses import dataclass
from typing import Tuple
import sys, subprocess
def pip_install(pkgs):
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q"] + pkgs)
pip_install([
"kornia==0.8.2",
"torch",
"torchvision",
"matplotlib",
"numpy",
"opencv-python-headless"
])
import numpy as np
import torch
import torch.nn as nn
import torch.nn.useful as F
import torchvision
import torchvision.transforms.useful as TF
import matplotlib.pyplot as plt
import cv2
import kornia
import kornia.augmentation as Okay
import kornia.geometry.remodel as KG
from kornia.geometry.ransac import RANSAC
from kornia.characteristic import LoFTR
torch.manual_seed(0)
np.random.seed(0)
random.seed(0)
print("Torch:", torch.__version__)
print("Kornia:", kornia.__version__)
print("Gadget:", system)We start by establishing a completely reproducible surroundings, putting in Kornia and its core dependencies to make sure GPU-accelerated, differentiable laptop imaginative and prescient runs easily in Google Colab. We then import and arrange PyTorch, Kornia, and supporting libraries, establishing a clear basis for geometry, augmentation, and feature-matching workflows. We set the random seed and choose the obtainable compute system so that each one subsequent experiments stay deterministic, debuggable, and performance-aware. Take a look at the FULL CODES right here.
def to_tensor_img_uint8(img_bgr_uint8: np.ndarray) -> torch.Tensor:
img_rgb = cv2.cvtColor(img_bgr_uint8, cv2.COLOR_BGR2RGB)
t = torch.from_numpy(img_rgb).permute(2, 0, 1).float() / 255.0
return t.unsqueeze(0)
def present(img_t: torch.Tensor, title: str = "", max_size: int = 900):
x = img_t.detach().float().cpu().clamp(0, 1)
if x.form[1] == 1:
x = x.repeat(1, 3, 1, 1)
x = x[0].permute(1, 2, 0).numpy()
h, w = x.form[:2]
scale = min(1.0, max_size / max(h, w))
if scale < 1.0:
x = cv2.resize(x, (int(w * scale), int(h * scale)), interpolation=cv2.INTER_AREA)
plt.determine(figsize=(7, 5))
plt.imshow(x)
plt.axis("off")
plt.title(title)
plt.present()
def show_mask(mask_t: torch.Tensor, title: str = ""):
x = mask_t.detach().float().cpu().clamp(0, 1)[0, 0].numpy()
plt.determine(figsize=(6, 4))
plt.imshow(x)
plt.axis("off")
plt.title(title)
plt.present()
def obtain(url: str, path: str):
os.makedirs(os.path.dirname(path), exist_ok=True)
if not os.path.exists(path):
urllib.request.urlretrieve(url, path)
def safe_download(url: str, path: str) -> bool:
attempt:
os.makedirs(os.path.dirname(path), exist_ok=True)
if not os.path.exists(path):
urllib.request.urlretrieve(url, path)
return True
besides Exception as e:
print("Obtain failed:", e)
return False
def make_grid_mask(h: int, w: int, cell: int = 32) -> torch.Tensor:
yy, xx = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij")
m = (((yy // cell) % 2) ^ ((xx // cell) % 2)).float()
return m.unsqueeze(0).unsqueeze(0)
def draw_matches(img0_rgb: np.ndarray, img1_rgb: np.ndarray, pts0: np.ndarray, pts1: np.ndarray, max_draw: int = 200) -> np.ndarray:
h0, w0 = img0_rgb.form[:2]
h1, w1 = img1_rgb.form[:2]
out = np.zeros((max(h0, h1), w0 + w1, 3), dtype=np.uint8)
out[:h0, :w0] = img0_rgb
out[:h1, w0:w0+w1] = img1_rgb
n = min(len(pts0), len(pts1), max_draw)
if n == 0:
return out
idx = np.random.alternative(len(pts0), dimension=n, substitute=False) if len(pts0) > n else np.arange(n)
for i in idx:
x0, y0 = pts0[i]
x1, y1 = pts1[i]
x1_shift = x1 + w0
p0 = (int(spherical(x0)), int(spherical(y0)))
p1 = (int(spherical(x1_shift)), int(spherical(y1)))
cv2.circle(out, p0, 2, (255, 255, 255), -1, lineType=cv2.LINE_AA)
cv2.circle(out, p1, 2, (255, 255, 255), -1, lineType=cv2.LINE_AA)
cv2.line(out, p0, p1, (255, 255, 255), 1, lineType=cv2.LINE_AA)
return out
def normalize_img_for_loftr(img_rgb01: torch.Tensor) -> torch.Tensor:
if img_rgb01.form[1] == 3:
return kornia.colour.rgb_to_grayscale(img_rgb01)
return img_rgb01We outline a set of reusable helper utilities for picture conversion, visualization, secure knowledge downloading, and artificial masks era, maintaining the imaginative and prescient pipeline clear and modular. We additionally implement strong visualization and matching helpers that permit us to examine augmented pictures, masks, and LoFTR correspondences immediately throughout experimentation. We normalize picture inputs to the precise tensor codecs anticipated by Kornia and LoFTR, guaranteeing that each one downstream geometry and feature-matching parts function persistently and accurately. Take a look at the FULL CODES right here.
print("n[1] Differentiable augmentations: picture + masks + keypoints")
B, C, H, W = 1, 3, 256, 384
img = torch.rand(B, C, H, W, system=system)
masks = make_grid_mask(H, W, cell=24).to(system)
kps = torch.tensor([[
[40.0, 40.0],
[W - 50.0, 50.0],
[W * 0.6, H * 0.8],
[W * 0.25, H * 0.65],
]], system=system)
aug = Okay.AugmentationSequential(
Okay.RandomResizedCrop((224, 224), scale=(0.6, 1.0), ratio=(0.8, 1.25), p=1.0),
Okay.RandomHorizontalFlip(p=0.5),
Okay.RandomRotation(levels=18.0, p=0.7),
Okay.ColorJiggle(0.2, 0.2, 0.2, 0.1, p=0.8),
data_keys=["input", "mask", "keypoints"],
same_on_batch=True
).to(system)
img_aug, mask_aug, kps_aug = aug(img, masks, kps)
print("picture:", tuple(img.form), "->", tuple(img_aug.form))
print("masks :", tuple(masks.form), "->", tuple(mask_aug.form))
print("kps :", tuple(kps.form), "->", tuple(kps_aug.form))
print("Instance keypoints (earlier than -> after):")
print(torch.cat([kps[0], kps_aug[0]], dim=1))
present(img, "Unique (artificial)")
show_mask(masks, "Unique masks (artificial)")
present(img_aug, "Augmented (synced)")
show_mask(mask_aug, "Augmented masks (synced)")We assemble a synchronized, totally differentiable augmentation pipeline that applies the identical geometric transformations to pictures, masks, and keypoints on the GPU. We generate artificial knowledge to obviously show how spatial consistency is preserved throughout modalities whereas nonetheless introducing reasonable variability by way of cropping, rotation, flipping, and colour jitter. We visualize the before-and-after outcomes to confirm that the augmented pictures, segmentation masks, and keypoints stay completely aligned after transformation. Take a look at the FULL CODES right here.
print("n[2] Differentiable homography alignment by optimization")
base = torch.rand(1, 1, 240, 320, system=system)
present(base, "Base picture (grayscale)")
true_H_px = torch.eye(3, system=system).unsqueeze(0)
true_H_px[:, 0, 2] = 18.0
true_H_px[:, 1, 2] = -12.0
true_H_px[:, 0, 1] = 0.03
true_H_px[:, 1, 0] = -0.02
true_H_px[:, 2, 0] = 1e-4
true_H_px[:, 2, 1] = -8e-5
goal = KG.warp_perspective(base, true_H_px, dsize=(base.form[-2], base.form[-1]), align_corners=True)
present(goal, "Goal (base warped by true homography)")
p = torch.zeros(1, 8, system=system, requires_grad=True)
def params_to_H(p8: torch.Tensor) -> torch.Tensor:
Bp = p8.form[0]
Hm = torch.eye(3, system=p8.system).unsqueeze(0).repeat(Bp, 1, 1)
Hm[:, 0, 0] = 1.0 + p8[:, 0]
Hm[:, 0, 1] = p8[:, 1]
Hm[:, 0, 2] = p8[:, 2]
Hm[:, 1, 0] = p8[:, 3]
Hm[:, 1, 1] = 1.0 + p8[:, 4]
Hm[:, 1, 2] = p8[:, 5]
Hm[:, 2, 0] = p8[:, 6]
Hm[:, 2, 1] = p8[:, 7]
return Hm
choose = torch.optim.Adam([p], lr=0.08)
losses = []
for step in vary(120):
choose.zero_grad(set_to_none=True)
H_est = params_to_H(p)
pred = KG.warp_perspective(base, H_est, dsize=(base.form[-2], base.form[-1]), align_corners=True)
loss_photo = (pred - goal).abs().imply()
loss_reg = 1e-3 * (p ** 2).imply()
loss = loss_photo + loss_reg
loss.backward()
choose.step()
losses.append(loss.merchandise())
print("Ultimate loss:", losses[-1])
plt.determine(figsize=(6,4))
plt.plot(losses)
plt.title("Homography optimization loss")
plt.xlabel("step")
plt.ylabel("loss")
plt.present()
H_est_final = params_to_H(p.detach())
pred_final = KG.warp_perspective(base, H_est_final, dsize=(base.form[-2], base.form[-1]), align_corners=True)
present(pred_final, "Recovered warp (optimized)")
present((pred_final - goal).abs(), "Abs error (recovered vs goal)")
print("True H (pixel):n", true_H_px.squeeze(0).detach().cpu().numpy())
print("Est H:n", H_est_final.squeeze(0).detach().cpu().numpy())We show that geometric alignment may be handled as a differentiable optimization downside by immediately recovering a homography through gradient descent. We first generate a goal picture by warping a base picture with a identified homography after which study the transformation parameters by minimizing a photometric reconstruction loss with regularization. Additionally, we visualize the optimized warp and error map to substantiate that the estimated homography intently matches the ground-truth transformation. Take a look at the FULL CODES right here.
print("n[3] LoFTR matching + RANSAC homography + stitching (403-safe)")
data_dir = "/content material/kornia_demo"
os.makedirs(data_dir, exist_ok=True)
img0_path = os.path.be a part of(data_dir, "img0.png")
img1_path = os.path.be a part of(data_dir, "img1.png")
ok0 = safe_download(
"https://uncooked.githubusercontent.com/opencv/opencv/grasp/samples/knowledge/graf1.png",
img0_path
)
ok1 = safe_download(
"https://uncooked.githubusercontent.com/opencv/opencv/grasp/samples/knowledge/graf3.png",
img1_path
)
if not (ok0 and ok1):
print("⚠️ Utilizing artificial fallback pictures (no community / blocked downloads)")
base_rgb = torch.rand(1, 3, 480, 640, system=system)
H_syn = torch.tensor([[
[1.0, 0.05, 40.0],
[-0.03, 1.0, 25.0],
[1e-4, -8e-5, 1.0]
]], system=system)
t0 = base_rgb
t1 = KG.warp_perspective(base_rgb, H_syn, dsize=(480, 640), align_corners=True)
img0_rgb = (t0[0].permute(1,2,0).detach().cpu().numpy() * 255).astype(np.uint8)
img1_rgb = (t1[0].permute(1,2,0).detach().cpu().numpy() * 255).astype(np.uint8)
else:
img0_bgr = cv2.imread(img0_path, cv2.IMREAD_COLOR)
img1_bgr = cv2.imread(img1_path, cv2.IMREAD_COLOR)
if img0_bgr is None or img1_bgr is None:
increase RuntimeError("Didn't load downloaded pictures.")
img0_rgb = cv2.cvtColor(img0_bgr, cv2.COLOR_BGR2RGB)
img1_rgb = cv2.cvtColor(img1_bgr, cv2.COLOR_BGR2RGB)
t0 = to_tensor_img_uint8(img0_bgr).to(system)
t1 = to_tensor_img_uint8(img1_bgr).to(system)
present(t0, "Picture 0")
present(t1, "Picture 1")
g0 = normalize_img_for_loftr(t0)
g1 = normalize_img_for_loftr(t1)
loftr = LoFTR(pretrained="outside").to(system).eval()
with torch.inference_mode():
correspondences = loftr({"image0": g0, "image1": g1})
mkpts0 = correspondences["keypoints0"]
mkpts1 = correspondences["keypoints1"]
mconf = correspondences.get("confidence", None)
print("Uncooked matches:", mkpts0.form[0])
if mkpts0.form[0] < 8:
increase RuntimeError("Too few matches to estimate homography.")
if mconf will not be None:
mconf = mconf.detach()
topk = min(2000, mkpts0.form[0])
idx = torch.topk(mconf, ok=topk, largest=True).indices
mkpts0 = mkpts0[idx]
mkpts1 = mkpts1[idx]
print("Stored high matches:", mkpts0.form[0])
ransac = RANSAC(
model_type="homography",
inl_th=3.0,
batch_size=4096,
max_iter=10,
confidence=0.999,
max_lo_iters=5
).to(system)
with torch.inference_mode():
H01, inliers = ransac(mkpts0, mkpts1)
print("Estimated H form:", tuple(H01.form))
print("Inliers:", int(inliers.sum().merchandise()), "/", int(inliers.numel()))
vis = draw_matches(
img0_rgb,
img1_rgb,
mkpts0.detach().cpu().numpy(),
mkpts1.detach().cpu().numpy(),
max_draw=250
)
plt.determine(figsize=(10,5))
plt.imshow(vis)
plt.axis("off")
plt.title("LoFTR matches (subset)")
plt.present()
H01 = H01.unsqueeze(0) if H01.ndim == 2 else H01
warped0 = KG.warp_perspective(t0, H01, dsize=(t1.form[-2], t1.form[-1]), align_corners=True)
stitched = torch.max(warped0, t1)
present(warped0, "Image0 warped into Image1 body (through RANSAC homography)")
present(stitched, "Easy stitched mix (max)")We carry out realized characteristic matching utilizing LoFTR to determine dense correspondences between two pictures, whereas guaranteeing robustness by way of a network-safe fallback mechanism. We then apply Kornia’s RANSAC to estimate a secure homography from these matches and warp one picture into the coordinate body of the opposite. We visualize the correspondences and produce a easy stitched outcome to validate the geometric alignment end-to-end. Take a look at the FULL CODES right here.
print("n[4] Mini coaching loop with Kornia augmentations (quick subset)")
cifar = torchvision.datasets.CIFAR10(root="/content material/knowledge", prepare=True, obtain=True)
num_samples = 4096
indices = np.random.permutation(len(cifar))[:num_samples]
subset = torch.utils.knowledge.Subset(cifar, indices.tolist())
def collate(batch):
imgs = []
labels = []
for im, y in batch:
imgs.append(TF.to_tensor(im))
labels.append(y)
return torch.stack(imgs, 0), torch.tensor(labels)
loader = torch.utils.knowledge.DataLoader(
subset, batch_size=256, shuffle=True, num_workers=2, pin_memory=True, collate_fn=collate
)
aug_train = Okay.ImageSequential(
Okay.RandomHorizontalFlip(p=0.5),
Okay.RandomAffine(levels=12.0, translate=(0.08, 0.08), scale=(0.9, 1.1), p=0.7),
Okay.ColorJiggle(0.2, 0.2, 0.2, 0.1, p=0.8),
Okay.RandomGaussianBlur((3, 3), (0.1, 1.5), p=0.3),
).to(system)
class TinyCifarNet(nn.Module):
def __init__(self, num_classes=10):
tremendous().__init__()
self.conv1 = nn.Conv2d(3, 48, 3, padding=1)
self.conv2 = nn.Conv2d(48, 96, 3, padding=1)
self.conv3 = nn.Conv2d(96, 128, 3, padding=1)
self.head = nn.Linear(128, num_classes)
def ahead(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv3(x))
x = x.imply(dim=(-2, -1))
return self.head(x)
mannequin = TinyCifarNet().to(system)
choose = torch.optim.AdamW(mannequin.parameters(), lr=2e-3, weight_decay=1e-4)
mannequin.prepare()
t_start = time.time()
working = []
for it, (xb, yb) in enumerate(loader):
xb = xb.to(system, non_blocking=True)
yb = yb.to(system, non_blocking=True)
xb = aug_train(xb)
logits = mannequin(xb)
loss = F.cross_entropy(logits, yb)
choose.zero_grad(set_to_none=True)
loss.backward()
choose.step()
working.append(loss.merchandise())
if (it + 1) % 10 == 0:
print(f"iter {it+1:03d}/{len(loader)} | loss {np.imply(working[-10:]):.4f}")
if it >= 39:
break
print("Completed in", spherical(time.time() - t_start, 2), "sec")
plt.determine(figsize=(6,4))
plt.plot(working)
plt.title("Coaching loss (fast demo)")
plt.xlabel("iteration")
plt.ylabel("loss")
plt.present()
xb0, yb0 = subsequent(iter(loader))
xb0 = xb0[:8].to(system)
xbA = aug_train(xb0)
def tile8(x):
x = x.detach().cpu().clamp(0,1)
grid = torchvision.utils.make_grid(x, nrow=4)
return grid.permute(1,2,0).numpy()
plt.determine(figsize=(10,5))
plt.imshow(tile8(xb0))
plt.axis("off")
plt.title("CIFAR batch (unique)")
plt.present()
plt.determine(figsize=(10,5))
plt.imshow(tile8(xbA))
plt.axis("off")
plt.title("CIFAR batch (Kornia-augmented on GPU)")
plt.present()
print("n✅ Tutorial full.")
print("Subsequent concepts:")
print("- Feathered stitching (delicate masks) as an alternative of max-blend.")
print("- Evaluate LoFTR vs DISK/LightGlue utilizing kornia.characteristic.")
print("- Multi-scale homography optimization + SSIM/Charbonnier losses.")We show how Kornia’s GPU-based augmentations combine immediately into a normal coaching loop by making use of them on the fly to a subset of the CIFAR-10 dataset. We prepare a light-weight convolutional community end-to-end, demonstrating that differentiable augmentations incur minimal overhead whereas enhancing knowledge variety. Eventually, we visualize unique versus augmented batches to substantiate that the transformations are utilized persistently and effectively throughout studying.
In conclusion, we demonstrated that Kornia allows a unified imaginative and prescient workflow the place knowledge augmentation, geometric reasoning, characteristic matching, and studying stay differentiable and GPU-friendly inside a single framework. By combining LoFTR matching, RANSAC-based homography estimation, and optimization-driven alignment with a sensible coaching loop, we confirmed how classical imaginative and prescient and deep studying complement one another slightly than compete. It serves as a basis for extending towards production-grade stitching, strong pose estimation, or large-scale coaching pipelines, and we emphasize that the identical patterns we used right here scale naturally to extra complicated, real-world imaginative and prescient methods.
Take a look at the FULL CODES right here. Additionally, be happy to observe us on Twitter and don’t neglect to affix our 100k+ ML SubReddit and Subscribe to our E-newsletter. Wait! are you on telegram? now you’ll be able to be a part of us on telegram as properly.
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