Adding a flake.nix.

Cleaning up the captcha for the OCR.

1 files changed, 112 insertions, 0 deletions

diff --git a/src/scripts/login/index.ts b/src/scripts/login/index.ts
new file mode 100644
index 0000000..b7503b9
--- /dev/null
+++ b/src/scripts/login/index.ts
@@ -0,0 +1,112 @@
+import { interceptPost, interceptGet } from "@/shared/intercept-jquery-ajax";
+
+function prepareCaptcha(ctx: CanvasRenderingContext2D, width: number, height: number) {
+	const imgData = ctx.getImageData(0, 0, width, height);
+	const data = imgData.data;
+	const radius = 1;
+
+	function isBlack(r: number, g: number, b: number) {
+		return r < 50 && g < 50 && b < 50;
+	}
+
+	function isGrayish(r: number, g: number, b: number) {
+		return Math.abs(r - g) < 15 && Math.abs(g - b) < 15 && r > 100 && r < 200;
+	}
+
+	function hasNearbyBlack(x: number, y: number) {
+		for (let dx = -radius; dx <= radius; dx++) {
+			for (let dy = -radius; dy <= radius; dy++) {
+				const nx = x + dx;
+				const ny = y + dy;
+				if (nx >= 0 && nx < width && ny >= 0 && ny < height) {
+					const i = (ny * width + nx) * 4;
+					if (isBlack(data[i], data[i + 1], data[i + 2])) return true;
+				}
+			}
+		}
+		return false;
+	}
+
+	for (let y = 0; y < height; y++) {
+		for (let x = 0; x < width; x++) {
+			const i = (y * width + x) * 4;
+			const [r, g, b] = [data[i], data[i + 1], data[i + 2]];
+			if (isGrayish(r, g, b) && !hasNearbyBlack(x, y)) {
+				data[i] = data[i + 1] = data[i + 2] = 255;
+			}
+		}
+	}
+	for (let y = 0; y < height; y++) {
+		for (let x = 0; x < width; x++) {
+			const i = (y * width + x) * 4;
+			const [r, g, b] = [data[i], data[i + 1], data[i + 2]];
+
+			if (isGrayish(r, g, b)) {
+				data[i] = data[i + 1] = data[i + 2] = 0; // make black
+			}
+		}
+	}
+	ctx.putImageData(imgData, 0, 0);
+}
+
+const observer = new MutationObserver((mutationsList, obs) => {
+	for (const mutation of mutationsList) {
+		for (const node of mutation.addedNodes) {
+			if ((node as any).nodeType === 1 && (node as any).id === "loginform") {
+				const img = (node as HTMLFormElement).querySelector("#captchaImg") as HTMLImageElement;
+				const captchaInput = (node as HTMLFormElement).querySelector("txtInput") as HTMLInputElement;
+
+				// Wait for image to load to get correct dimensions
+				img.addEventListener("load", async () => {
+					const { width, height } = img;
+
+					// 1. Create canvas
+					const canvas = document.createElement("canvas");
+					canvas.width = width;
+					canvas.height = height;
+					canvas.style.display = "block";
+
+					// 2. Copy styles (optional)
+					canvas.style.cssText = getComputedStyle(img).cssText;
+					canvas.style.display = "none";
+
+					// 3. Insert canvas before image
+					img.parentNode!.querySelectorAll("canvas").forEach(c => c.remove());
+					img.parentNode!.insertBefore(canvas, img);
+
+					// 4. Hide image
+					img.style.display = "none";
+
+					// 5. Draw the image onto the canvas
+					const ctx = canvas.getContext("2d")!;
+					ctx.drawImage(img, 0, 0);
+					prepareCaptcha(ctx, canvas.width, canvas.height);
+					canvas.style.display = "";
+					const dataURL = canvas.toDataURL("image/png");
+					// TODO: Perform OCR
+					// window.postMessage({
+					// 	type: "RUN_OCR",
+					// 	dataURL: dataURL
+					// }, "*");
+					//
+					// // Listen for OCR result
+					// function recieveOCRResult(event: any) {
+					// 	if (event.data.type === "OCR_RESULT") {
+					// 		console.log("Tesseract result:", event.data.text, "Confidence:", event.data.confidence);
+					// 		window.removeEventListener("message", recieveOCRResult);
+					// 		if (event.data.confidence < 80 || event.data.text.length !== 6) {
+					// 			img.src = "generateCaptcha?" + new Date().getTime();
+					// 		} else {
+					// 			captchaInput.value = event.data.text;
+					// 		}
+					// 	}
+					// }
+					// window.addEventListener("message", recieveOCRResult);
+				});
+			}
+		}
+	}
+});
+
+// Start observing the body
+observer.observe(document.body, { childList: true, subtree: true });