#!/usr/bin/env python3 """Webcam Calibration Studio (desktop / offline version) - Lecture 6 live demo. pip install opencv-python numpy python webcam_calibration_studio.py # 9 x 6 board, 25 mm squares, camera 0 python webcam_calibration_studio.py --board 9x6 --mm 24 --camera 1 --width 1280 --height 720 Keys (click the video window first): SPACE capture a view now A auto-capture on/off (still board + new position) C calibrate (>= 8 views) U live undistort: raw | split | corrected M measure distance mode G straight-line grid on/off D delete the last view S save camera.yaml + the captured images R reset ESC / Q quit Same steps as the in-browser studio (lectures/06_webcam_calibration_studio.html), but runs without internet. """ import argparse import os import time import cv2 import numpy as np CRIT = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) FLAGS = cv2.CALIB_CB_ADAPTIVE_THRESH | cv2.CALIB_CB_NORMALIZE_IMAGE def signature(c, w, h, nx): c = c.reshape(-1, 2) a, b, d, e = c[0], c[nx - 1], c[-1], c[-nx] area = abs((b[0] - e[0]) * (d[1] - a[1]) - (d[0] - a[0]) * (b[1] - e[1])) / 2 v1, v2 = b - a, e - a ang = np.arccos(np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2) + 1e-9)) return np.array([c[:, 0].mean() / w, c[:, 1].mean() / h, np.sqrt(area / (w * h)), min(1, 2 * abs(np.pi / 2 - ang))]) def coverage(sigs): lim = np.array([0.7, 0.7, 0.4, 0.5]) if not sigs: return np.zeros(4) s = np.array(sigs) return np.minimum(1, (s.max(0) - s.min(0)) / lim) def put(img, text, org, scale=0.6, col=(255, 255, 255), bg=(30, 30, 30)): (tw, th), _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, scale, 2) cv2.rectangle(img, (org[0] - 4, org[1] - th - 6), (org[0] + tw + 4, org[1] + 6), bg, -1) cv2.putText(img, text, org, cv2.FONT_HERSHEY_SIMPLEX, scale, col, 2, cv2.LINE_AA) def main(): ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) ap.add_argument("--board", default="9x6", help="inner corners (the course pattern.png is 9x6)") ap.add_argument("--mm", type=float, default=25.0, help="measured square size in mm") ap.add_argument("--camera", default="0", help="camera index, or a video file to test without a webcam") ap.add_argument("--width", type=int, default=640) ap.add_argument("--height", type=int, default=480) ap.add_argument("--out", default="my_webcam_calibration") a = ap.parse_args() nx, ny = (int(v) for v in a.board.lower().split("x")) objp = np.zeros((nx * ny, 3), np.float32) objp[:, :2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2) * a.mm cap = cv2.VideoCapture(int(a.camera) if a.camera.isdigit() else a.camera) cap.set(cv2.CAP_PROP_FRAME_WIDTH, a.width) cap.set(cv2.CAP_PROP_FRAME_HEIGHT, a.height) ok, frame = cap.read() if not ok: raise SystemExit("cannot open the webcam - try --camera 1, or close Zoom/Teams") h, w = frame.shape[:2] print(f"camera {a.camera}: {w}x{h}. Board {nx}x{ny} inner corners, {a.mm} mm squares.") imgpoints, sigs, frames = [], [], [] K = D = None maps = None auto, und_mode, measure, grid = False, 0, False, False last, still, last_auto, flash = None, 0, 0.0, 0.0 win = "Webcam Calibration Studio (SPACE capture, A auto, C calibrate, U undistort, M measure, S save, Q quit)" while True: ok, frame = cap.read() if not ok: break gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) small = cv2.resize(gray, None, fx=min(1, 640 / w), fy=min(1, 640 / w)) s = small.shape[1] / w found, c = cv2.findChessboardCorners(small, (nx, ny), FLAGS | cv2.CALIB_CB_FAST_CHECK) corners = c.reshape(-1, 1, 2) / s if found else None if corners is not None and last is not None: still = still + 1 if np.abs(corners - last).mean() < 1.5 else 0 else: still = 0 last = corners def do_capture(): nonlocal flash ok2, cc = cv2.findChessboardCorners(gray, (nx, ny), FLAGS) if not ok2: return False cc = cv2.cornerSubPix(gray, cc.reshape(-1, 1, 2), (11, 11), (-1, -1), CRIT).reshape(-1, 1, 2) imgpoints.append(cc); sigs.append(signature(cc, w, h, nx)); frames.append(frame.copy()) flash = time.time() print(f"view {len(imgpoints)} captured") return True if auto and corners is not None and still >= 3 and time.time() - last_auto > 1.0: sig = signature(corners, w, h, nx) if all(np.abs(sig - q).sum() > 0.15 for q in sigs): do_capture(); last_auto = time.time() # ---------------- display if K is not None and und_mode: if maps is None: newK, _ = cv2.getOptimalNewCameraMatrix(K, D, (w, h), 0) maps = cv2.initUndistortRectifyMap(K, D, None, newK, (w, h), cv2.CV_16SC2) und = cv2.remap(frame, *maps, cv2.INTER_LINEAR) vis = und if und_mode == 2 else np.hstack([frame[:, : w // 2], und[:, w // 2:]]) if und_mode == 1: cv2.line(vis, (w // 2, 0), (w // 2, h), (255, 255, 255), 2) put(vis, "raw", (10, h - 12)); put(vis, "corrected", (w // 2 + 10, h - 12)) else: vis = frame.copy() for q in imgpoints: for x, y in q.reshape(-1, 2): cv2.circle(vis, (int(x), int(y)), 2, (221, 118, 42), -1) if corners is not None: cv2.drawChessboardCorners(vis, (nx, ny), corners.astype(np.float32), True) if measure and K is not None and corners is not None: cc = cv2.cornerSubPix(gray, corners.astype(np.float32), (5, 5), (-1, -1), CRIT) _, rvec, tvec = cv2.solvePnP(objp, cc, K, D) R, _ = cv2.Rodrigues(rvec) dist_mm = np.linalg.norm(R @ objp.mean(0) + tvec.ravel()) cv2.drawFrameAxes(vis, K, D, rvec, tvec, 3 * a.mm, 3) put(vis, f"{dist_mm / 10:.1f} cm away", (w - 230, h - 20), 0.9, (255, 255, 255), (60, 120, 40)) if grid: for i in range(1, 8): cv2.line(vis, (i * w // 8, 0), (i * w // 8, h), (238, 211, 34), 1) for i in range(1, 6): cv2.line(vis, (0, i * h // 6), (w, i * h // 6), (238, 211, 34), 1) cov = coverage(sigs) for k, name in enumerate(["X", "Y", "Size", "Skew"]): y0 = 30 + 22 * k cv2.rectangle(vis, (60, y0 - 12), (220, y0 + 2), (60, 60, 60), -1) col = (77, 122, 31) if cov[k] >= 1 else (60, 162, 230) if cov[k] > 0.5 else (43, 57, 192) cv2.rectangle(vis, (60, y0 - 12), (60 + int(160 * cov[k]), y0 + 2), col, -1) put(vis, name, (10, y0), 0.5) status = f"views {len(imgpoints)} auto {'ON' if auto else 'off'}" status += " board found" if corners is not None else " NO board" if K is not None: status += f" calibrated (RMS {rms:.2f}px)" put(vis, status, (240, 30), 0.6, (255, 255, 255), (40, 86, 138)) if time.time() - flash < 0.15: vis = cv2.addWeighted(vis, 0.5, np.full_like(vis, 255), 0.5, 0) cv2.imshow(win, vis) k = cv2.waitKey(1) & 0xFF if k in (27, ord("q")): break elif k == ord(" "): if not do_capture(): print("board not found in this frame") elif k == ord("a"): auto = not auto elif k == ord("d") and imgpoints: imgpoints.pop(); sigs.pop(); frames.pop() elif k == ord("r"): imgpoints, sigs, frames, K, D, maps = [], [], [], None, None, None elif k == ord("g"): grid = not grid elif k == ord("u") and K is not None: und_mode = (und_mode + 1) % 3 elif k == ord("m") and K is not None: measure = not measure; und_mode = 0 elif k == ord("c"): if len(imgpoints) < 8: print("need at least 8 views") continue rms, K, D, rvecs, tvecs, std_in, _, per_view = cv2.calibrateCameraExtended( [objp] * len(imgpoints), imgpoints, (w, h), None, None) maps = None print(f"\nRMS reprojection error: {rms:.3f} px") print("K =\n", np.round(K, 1)); print("dist =", np.round(D.ravel(), 4)) print("per-view errors:", np.round(per_view.ravel(), 2)) print(f"horizontal FOV ~ {2 * np.degrees(np.arctan(w / 2 / K[0, 0])):.0f} deg") elif k == ord("s") and K is not None: os.makedirs(a.out, exist_ok=True) fs = cv2.FileStorage(os.path.join(a.out, "camera.yaml"), cv2.FILE_STORAGE_WRITE) fs.write("image_width", w); fs.write("image_height", h); fs.write("camera_matrix", K) fs.write("distortion_coefficients", D); fs.write("rms", rms); fs.write("square_size_mm", a.mm); fs.release() for i, f in enumerate(frames): cv2.imwrite(os.path.join(a.out, f"view_{i:02d}.jpg"), f) print("saved", a.out) cap.release() cv2.destroyAllWindows() if __name__ == "__main__": main()