Camera Field of View Calculator with Real-Time Distortion Visualization

How Does Field of View Calculation Work?

The field of view formula assumes an ideal pinhole camera with rectilinear projection. For lenses with minimal distortion (typically telephoto and standard focal lengths), this formula provides accurate predictions. However, wide-angle and fisheye lenses deviate significantly from the rectilinear model, which is why this calculator includes distortion correction and multiple projection models.

What Does the Distortion Visualizer Show?

The distortion visualizer displays a reference grid as it would appear through the selected lens. Barrel distortion (negative coefficients) curves straight lines outward from the image center — common in wide-angle M12 lenses below 4mm focal length. Pincushion distortion (positive coefficients) curves lines inward — occasionally seen in telephoto designs. The visualizer helps engineers understand the actual image geometry before committing to a lens selection.

What Parameters Affect Field of View?

有効焦点距離（EFL）

The effective focal length determines the angular field of view through the relationship FOV ∝ arctan(1/EFL). Shorter focal lengths provide wider coverage; longer focal lengths narrow the field but increase magnification. Browse M12 lenses sorted by focal length from 0.8mm fisheye to 75mm telephoto, or explore C-mount lenses for larger format sensors.

Note: back focal length (BFL) describes the physical distance from the rear lens element to the sensor plane and should not be used for FOV calculations. See our effective focal length calculator for more details on the distinction.

Sensor Active Area

FOV calculations require the active sensor dimensions, not the nominal format name. A "1/2.3 inch" sensor actually measures approximately 6.17 × 4.55 mm — the fraction refers to historical vidicon tube conventions, not physical dimensions. The calculator includes preset dimensions for common Sony, OmniVision, and OnSemi sensors used in machine vision and robotics applications.

Lens Image Circle

The lens must project an image circle larger than the sensor diagonal to avoid dark corners (vignetting). Most M12 lenses designed for 1/2" format sensors provide approximately 8-10 mm image circles. When using larger sensors, verify coverage in the lens specifications — our product pages include sensor compatibility information for each lens. For applications requiring precise corner illumination, consider our low-distortion M12 lenses which are optimized for uniform field illumination.

Distortion and Projection Model

Barrel distortion in wide-angle lenses maps more angular content to the image periphery than the rectilinear formula predicts. A lens with -15% TV distortion at the image edge may capture 10-20% more angular coverage than the undistorted calculation suggests. Fisheye lenses use specific mathematical projections — equidistant (r = f·θ), equisolid-angle (r = 2f·sin(θ/2)), or stereographic (r = 2f·tan(θ/2)) — that produce dramatically different FOV from rectilinear lenses of the same focal length.

For more on how lens characteristics affect image quality, see our technical blog articles covering topics like sensor matching and optical performance.

How Do I Apply FOV Results to System Design?

Determining Working Distance

To find the working distance required for a specific scene width, rearrange the geometry:

Example: To view a 2-meter wide scene with 60° HFOV, the required working distance is approximately 1.73 m. Verify that the depth of field at this distance covers your subject depth using the depth of field calculator.

Selecting Focal Length for Target FOV

If you know your required field of view, use our angle of view calculator to determine the target FOV from your scene requirements, then calculate the required focal length:

Example: To achieve 70° HFOV on a Sony IMX477 sensor (6.29 mm width), select a lens with approximately 4.5 mm focal length. Browse M12 lenses filtered by focal length to find matching options, or use our EFL calculator for precise focal length determination.

What Are Common FOV Calculation Mistakes?

Confusing Total FOV with Angular Resolution

Total field of view indicates the angular extent captured by the sensor. Angular resolution (IFOV, instantaneous field of view) indicates the angle subtended by a single pixel: IFOV = FOV / pixel_count. For a 90° HFOV across 1920 pixels, each pixel spans approximately 0.047° (2.8 arcminutes). At 1 meter working distance, this corresponds to roughly 0.8 mm per pixel — critical for determining whether your system can resolve the features you need to detect in machine vision applications.

Using Nominal Format Instead of Active Area

Sensor format designations (1/2.3", 1/1.8", etc.) are historical conventions that do not correspond to physical dimensions. A "1/2.3 inch" sensor has approximately 7.86 mm diagonal, not 11 mm (which would be the actual fraction). Always use the active area dimensions from the sensor datasheet — see our CMOS sensor size reference for common sensor specifications.

Ignoring Distortion in Wide-Angle Systems

For lenses with focal lengths below 4 mm on 1/2" format sensors, barrel distortion typically exceeds -10% at the image corners. This distortion compresses more angular content at the edges, increasing effective FOV beyond the rectilinear prediction. The distortion visualizer shows this effect directly — a heavily distorted grid indicates that the actual FOV exceeds the formula-based calculation.

How Do I Implement Distortion Correction with OpenCV?

This calculator provides initial FOV estimates based on nominal specifications and characterized distortion data. For computer vision applications requiring precise undistortion, you'll need to calibrate your specific lens-sensor combination using physical samples and calibration targets. OpenCV provides two camera models depending on your lens type.

Standard Camera Model (Lenses <120° FOV)

For rectilinear and moderate wide-angle lenses, OpenCV's standard calibration uses the Brown-Conrady distortion model with five coefficients. The cv2.calibrateCamera() function estimates intrinsic parameters (focal length, principal point) and distortion coefficients (k₁, k₂, p₁, p₂, k₃) from checkerboard images.

# Calibrate using checkerboard images
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(
    objpoints,    # 3D points in world coordinates
    imgpoints,    # 2D points in image plane
    gray.shape[::-1], 
    None, None
)

# dist contains [k1, k2, p1, p2, k3]
# Undistort images using the calibration
undistorted = cv2.undistort(img, mtx, dist)

The radial distortion coefficients (k₁, k₂, k₃) model barrel and pincushion distortion, while tangential coefficients (p₁, p₂) correct for lens-sensor misalignment. For most M12 lenses with focal lengths above 3mm, the standard model provides sub-pixel accuracy after calibration.

Fisheye Camera Model (Lenses >120° FOV)

For ultra-wide and fisheye lenses, the standard model fails at extreme angles. OpenCV's fisheye module implements the Kannala-Brandt equidistant projection model with four distortion coefficients (k₁, k₂, k₃, k₄).

# Fisheye calibration for wide-angle lenses
calibration_flags = (
    cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC +
    cv2.fisheye.CALIB_FIX_SKEW
)

ret, K, D, rvecs, tvecs = cv2.fisheye.calibrate(
    objpoints, imgpoints, gray.shape[::-1],
    None, None,
    flags=calibration_flags
)

# D contains [k1, k2, k3, k4] for equidistant model
# Undistort to rectilinear (crops FOV significantly)
map1, map2 = cv2.fisheye.initUndistortRectifyMap(
    K, D, np.eye(3), K, img.shape[:2][::-1], cv2.CV_16SC2
)
undistorted = cv2.remap(img, map1, map2, cv2.INTER_LINEAR)

Calibration Workflow

The recommended workflow for implementing distortion correction in your vision system:

1. Order samples — Select candidate lenses from our M12 or C-mount collections based on FOV estimates from this calculator
2. Capture calibration images — Photograph a checkerboard pattern (typically 9×6 or 7×5 inner corners) at 15-30 different orientations covering the full FOV
3. Run calibration — Use cv2.calibrateCamera() for standard lenses or cv2.fisheye.calibrate() for wide-angle
4. Evaluate reprojection error — Target <0.5 pixels RMS for precision applications
5. Apply correction — Use cv2.undistort() or pre-compute rectification maps for real-time performance