Added Hand Tracking, a calibration option, start of a HUD and Blender Files
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DuOtto
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import cv2
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import mediapipe as mp
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import numpy as np
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# ----- Helper Functions -----
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def compute_distance(p1, p2):
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"""Compute Euclidean distance between two points p1 and p2"""
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return np.hypot(p2[0] - p1[0], p2[1] - p1[1])
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# ----- Configuration -----
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CAMERA_INDEX = 0 # Change if multiple cameras
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FRAME_WIDTH = 1280
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FRAME_HEIGHT = 720
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# Initial calibration: approximate pixels per inch
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PIXELS_PER_INCH = 20
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# Gesture thresholds
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PINCH_THRESHOLD = 40 # px distance index-middle to start action
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RELEASE_THRESHOLD = 60 # px distance to end action
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# Circle touch threshold for object calibration
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CIRCLE_TOUCH_THRESHOLD = 20 # px tolerance to detect finger on circle
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# ----- Initialize Hand Detector -----
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mp_hands = mp.solutions.hands
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mp_draw = mp.solutions.drawing_utils
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hands = mp_hands.Hands(
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static_image_mode=False,
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max_num_hands=1,
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min_detection_confidence=0.7,
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min_tracking_confidence=0.5
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)
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# ----- State Variables -----
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measuring = False # Flag for measurement gesture
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start_pt = None
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calibrating = False # Flag for pinch-based calibration mode
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cal_start = None
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object_calibrating = False # Flag for object-based calibration mode
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cal_circle = None # Stores calibrated circle (x, y, r)
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# ----- Main Loop -----
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def main():
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global measuring, start_pt, calibrating, cal_start, object_calibrating, PIXELS_PER_INCH
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cap = cv2.VideoCapture(CAMERA_INDEX)
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cap.set(cv2.CAP_PROP_FRAME_WIDTH, FRAME_WIDTH)
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cap.set(cv2.CAP_PROP_FRAME_HEIGHT, FRAME_HEIGHT)
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if not cap.isOpened():
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print(f"Error: cannot open camera {CAMERA_INDEX}")
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return
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print("Press 'c' for pinch calibration, 'o' for object circle calibration, 'q' to quit.")
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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frame = cv2.flip(frame, 1)
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h, w, _ = frame.shape
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# Hand detection
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img_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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img_rgb.flags.writeable = False
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results = hands.process(img_rgb)
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img_rgb.flags.writeable = True
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frame = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR)
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fingertip_idx = None
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fingertip_mid = None
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index_extended = False
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middle_extended = False
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if results.multi_hand_landmarks:
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hand = results.multi_hand_landmarks[0]
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mp_draw.draw_landmarks(frame, hand, mp_hands.HAND_CONNECTIONS)
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# get index and middle finger tips and PIP to check extension
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idx_tip = hand.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP]
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idx_pip = hand.landmark[mp_hands.HandLandmark.INDEX_FINGER_PIP]
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mid_tip = hand.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_TIP]
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mid_pip = hand.landmark[mp_hands.HandLandmark.MIDDLE_FINGER_PIP]
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ix, iy = int(idx_tip.x * w), int(idx_tip.y * h)
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mx, my = int(mid_tip.x * w), int(mid_tip.y * h)
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fingertip_idx = (ix, iy)
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fingertip_mid = (mx, my)
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# draw fingertips
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cv2.circle(frame, fingertip_idx, 8, (0,255,0), -1)
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cv2.circle(frame, fingertip_mid, 8, (0,255,0), -1)
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# determine if fingers are extended (tip above PIP)
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index_extended = idx_tip.y < idx_pip.y
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middle_extended = mid_tip.y < mid_pip.y
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# pinch distance between index and middle
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pinch_dist = compute_distance(fingertip_idx, fingertip_mid)
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cv2.putText(frame, f"Pinch: {int(pinch_dist)}px", (10,30),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0), 2)
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# Pinch-based calibration
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if calibrating:
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if pinch_dist < PINCH_THRESHOLD and cal_start is None:
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cal_start = fingertip_idx
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print("Pinch calibration start point set.")
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elif pinch_dist > RELEASE_THRESHOLD and cal_start is not None:
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cal_end = fingertip_idx
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px_dist = compute_distance(cal_start, cal_end)
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inches = float(input("Enter actual distance between points in inches: "))
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PIXELS_PER_INCH = px_dist / inches
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print(f"Pinch calibration done: {PIXELS_PER_INCH:.2f} pixels/inch")
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calibrating = False
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cal_start = None
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# Measurement gesture (only when not calibrating)
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elif not object_calibrating and index_extended and middle_extended:
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if pinch_dist < PINCH_THRESHOLD and not measuring:
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measuring = True
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start_pt = fingertip_idx
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elif pinch_dist > RELEASE_THRESHOLD and measuring:
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measuring = False
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# Object-based calibration
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if object_calibrating:
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# Mask for orange color to find printed reference circle
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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# HSV range for orange (tune as needed)
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lower_orange = np.array([10, 100, 100])
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upper_orange = np.array([25, 255, 255])
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color_mask = cv2.inRange(hsv, lower_orange, upper_orange)
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masked_frame = cv2.bitwise_and(frame, frame, mask=color_mask)
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# Convert masked area to grayscale for Hough
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gray = cv2.cvtColor(masked_frame, cv2.COLOR_BGR2GRAY)
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gray = cv2.medianBlur(gray, 5)
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circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, dp=1.2, minDist=100,
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param1=50, param2=30, minRadius=10, maxRadius=300)
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if circles is not None and fingertip_idx is not None:
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circles = np.round(circles[0, :]).astype(int)
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# Filter circles by proximity of fingertip to circumference
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touched = []
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for x, y, r in circles:
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dist_c = compute_distance((x, y), fingertip_idx)
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if abs(dist_c - r) < CIRCLE_TOUCH_THRESHOLD:
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touched.append((x, y, r))
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if touched:
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# choose circle closest to exact touch point
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touched.sort(key=lambda c: abs(compute_distance((c[0], c[1]), fingertip_idx) - c[2]))
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x, y, r = touched[0]
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# store calibrated circle permanently
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cal_circle = (x, y, r)
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# draw selected calibration circle
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cv2.circle(frame, (x, y), r, (0, 0, 255), 3)
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cv2.drawMarker(frame, (x, y), (0, 0, 255), markerType=cv2.MARKER_CROSS, markerSize=20, thickness=2)
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cv2.line(frame, (x - r, y), (x + r, y), (0, 0, 255), 2)
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cv2.putText(frame, f"Cal Circle r={r}px", (x - r, y - r - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,0,255), 2)
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# compute pixels per inch from diameter
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PIXELS_PER_INCH = (2 * r) / 1.0
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print(f"Object calibration done: {PIXELS_PER_INCH:.2f} pixels/inch")
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object_calibrating = False
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# Overlay mode text
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if calibrating:
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cv2.putText(frame, "PINCH CALIBRATING...", (10,60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255), 2)
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if object_calibrating:
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cv2.putText(frame, 'PLACE 1" CIRCLE & POINT AT IT', (10,90),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,0,255), 2)
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# Draw measurement line and values
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if measuring and start_pt and fingertip_idx:
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cv2.line(frame, start_pt, fingertip_idx, (255,0,0), 2)
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px = compute_distance(start_pt, fingertip_idx)
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inch = px / PIXELS_PER_INCH
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cm = inch * 2.54
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midpt = ((start_pt[0] + fingertip_idx[0])//2,
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(start_pt[1] + fingertip_idx[1])//2)
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cv2.putText(frame, f"{inch:.2f} in / {cm:.1f} cm", (midpt[0]+10, midpt[1]-10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,0,0), 2)
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# Display
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cv2.imshow('Hand Measure', frame)
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key = cv2.waitKey(1) & 0xFF
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if key == ord('q'):
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break
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elif key == ord('c'):
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calibrating = True
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cal_start = None
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print("Entered pinch calibration mode.")
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elif key == ord('o'):
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object_calibrating = True
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print("Entered object calibration mode. Present a 1-inch circle & point at it.")
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cap.release()
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cv2.destroyAllWindows()
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if __name__ == '__main__':
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main()
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