DuOtto
279 lines
11 KiB
Python
279 lines
11 KiB
Python
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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return np.hypot(p2[0] - p1[0], p2[1] - p1[1])
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# ----- Configuration -----
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CAMERA_INDICES = [0] # List of camera device indices
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FRAME_WIDTH = 1280
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FRAME_HEIGHT = 720
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PIXELS_PER_INCH = 38 # will be set by calibration
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PINCH_THRESHOLD = 40 # px to start touch
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RELEASE_THRESHOLD = 60 # px to end touch
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CIRCLE_TOUCH_THRESHOLD = 20 # px tolerance for circle touch
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# HSV range for shape color (tune for your arrow: now tailored for orange)
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LOWER_SHAPE = np.array([10, 100, 100]) # hue from 10° (orange) to
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UPPER_SHAPE = np.array([30, 255, 255]) # hue up to 30°, full sat/val range
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# dynamic list of registered arrows
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# each entry will be {'lower': np.array, 'upper': np.array, 'tip': (x,y) or None}
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shape_ranges = []
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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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# ----- Global State -----
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measuring = False # measurement in progress
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start_pt = None # measurement start point
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calibrating = False # pinch-based calibration flag
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cal_start = None # pinch calibration start point
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object_calibrating = False # object calibration flag
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cal_circle = None # reference circle (x,y,r)
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fingertip_idx_global = None # last detected fingertip position
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arrow_tip = None # detected arrow tip position
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# ----- Per-camera Processing -----
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def process_frame(frame):
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global measuring, start_pt, calibrating, cal_start
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global object_calibrating, cal_circle, PIXELS_PER_INCH
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global fingertip_idx_global, arrow_tip
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frame_out = cv2.flip(frame, 1)
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h, w, _ = frame_out.shape
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# Hand detection
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rgb = cv2.cvtColor(frame_out, cv2.COLOR_BGR2RGB)
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rgb.flags.writeable = False
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results = hands.process(rgb)
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rgb.flags.writeable = True
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frame_vis = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
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fingertip_idx = None
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fingertip_mid = None
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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_vis, hand, mp_hands.HAND_CONNECTIONS)
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# get index and middle finger tips and PIP joints
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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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fingertip_idx_global = fingertip_idx
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# draw fingertips
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cv2.circle(frame_vis, fingertip_idx, 8, (0,255,0), -1)
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cv2.circle(frame_vis, fingertip_mid, 8, (0,255,0), -1)
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# check extension
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index_ext = idx_tip.y < idx_pip.y
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middle_ext = mid_tip.y < mid_pip.y
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# if measurement in progress but fingers no longer both extended, stop measuring
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if measuring and not (index_ext and middle_ext):
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measuring = False
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# pinch distance
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pinch = compute_distance(fingertip_idx, fingertip_mid)
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cv2.putText(frame_vis, f"Pinch: {int(pinch)} px", (10,30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,255,0),2)
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# pinch calibration
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if calibrating:
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if pinch < PINCH_THRESHOLD and cal_start is None:
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cal_start = fingertip_idx
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print("Pinch calibration start set")
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elif pinch > RELEASE_THRESHOLD and cal_start is not None:
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cal_end = fingertip_idx
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px = compute_distance(cal_start, cal_end)
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inches = float(input("Enter actual distance between points (inches): "))
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PIXELS_PER_INCH = px / inches
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print(f"Calibrated: {PIXELS_PER_INCH:.2f} px/inch")
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calibrating = False
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cal_start = None
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# measurement gesture
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elif not object_calibrating and index_ext and middle_ext:
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if pinch < 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 > RELEASE_THRESHOLD and measuring:
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measuring = False
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# object calibration
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if object_calibrating and fingertip_idx is not None:
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hsv = cv2.cvtColor(frame_vis, cv2.COLOR_BGR2HSV)
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mask = cv2.inRange(hsv, np.array([10,100,100]), np.array([25,255,255]))
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masked = cv2.bitwise_and(frame_vis, frame_vis, mask=mask)
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gray = cv2.cvtColor(masked, cv2.COLOR_BGR2GRAY)
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gray = cv2.medianBlur(gray,5)
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circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1.2, 100,
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param1=50, param2=30, minRadius=10, maxRadius=300)
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if circles is not None:
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circles = np.round(circles[0]).astype(int)
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touched = [(x,y,r) for x,y,r in circles
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if abs(compute_distance((x,y), fingertip_idx)-r) < CIRCLE_TOUCH_THRESHOLD]
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if touched:
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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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cal_circle = (x,y,r)
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PIXELS_PER_INCH = 2 * r
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print(f"Circle calib: {PIXELS_PER_INCH:.2f} px/inch")
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object_calibrating = False
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# permanent reference circle
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if cal_circle:
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cx,cy,cr = cal_circle
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cv2.circle(frame_vis,(cx,cy),cr,(0,0,255),2)
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cv2.drawMarker(frame_vis,(cx,cy),(0,0,255),cv2.MARKER_TILTED_CROSS,15,1)
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cv2.putText(frame_vis,f"Ref r={cr} px",(cx-cr,cy+cr+20),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,255),1)
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# ----- Arrow Shape Detection & HUD Placement -----
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# 1) Build a clean orange mask
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hsv = cv2.cvtColor(frame_vis, cv2.COLOR_BGR2HSV)
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mask = cv2.inRange(hsv, LOWER_SHAPE, UPPER_SHAPE)
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kern = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
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mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kern)
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mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kern)
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# 2) Find and filter contours
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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for cnt in contours:
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area = cv2.contourArea(cnt)
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if area < 1000:
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continue
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# Approximate to polygon and require exactly 5 corners
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peri = cv2.arcLength(cnt, True)
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approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
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if len(approx) != 5:
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continue
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pts = approx.reshape(-1,2)
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centroid = np.mean(pts, axis=0)
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# Find the arrow tip as the corner farthest from centroid
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dists = [np.linalg.norm(pt - centroid) for pt in pts]
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tip_pt = pts[int(np.argmax(dists))]
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raw_tip = (int(tip_pt[0]), int(tip_pt[1]))
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# 3) Smooth the tip over time
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alpha = 0.2
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if arrow_tip is None:
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arrow_tip = raw_tip
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else:
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arrow_tip = (
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int(alpha * raw_tip[0] + (1-alpha) * arrow_tip[0]),
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int(alpha * raw_tip[1] + (1-alpha) * arrow_tip[1])
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)
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# Draw the arrow & tip
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cv2.drawContours(frame_vis, [pts], -1, (0,255,255), 2)
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cv2.circle(frame_vis, arrow_tip, 8, (0,255,255), -1)
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cv2.putText(frame_vis, "Arrow Tip", arrow_tip,
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,255), 2)
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# Place the HUD box in the arrow’s pointing direction
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dir_vec = tip_pt - centroid
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norm = np.linalg.norm(dir_vec)
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if norm>0:
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dir_unit = dir_vec / norm
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offset = 50
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hud_center = (int(arrow_tip[0] + dir_unit[0]*offset),
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int(arrow_tip[1] + dir_unit[1]*offset))
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# Build and rotate a 120×60 HUD rectangle
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w2, h2 = 60, 30
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theta = np.arctan2(dir_unit[1], dir_unit[0])
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R = np.array([[ np.cos(theta), -np.sin(theta)],
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[ np.sin(theta), np.cos(theta)]])
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corners = np.array([[-w2,-h2], [w2,-h2], [w2,h2], [-w2,h2]])
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hud_pts = (corners @ R.T) + np.array(hud_center)
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hud_pts = hud_pts.astype(int)
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cv2.drawContours(frame_vis, [hud_pts], -1, (255,0,255), 2)
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cv2.putText(frame_vis, "HUD", hud_center,
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255,0,255), 2)
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return frame_vis
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# ----- Main -----
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def main():
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caps = []
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for idx in CAMERA_INDICES:
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cap = cv2.VideoCapture(idx)
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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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caps.append(cap)
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if not all(cap.isOpened() for cap in caps):
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print("Error: could not open all cameras")
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return
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print("Press 'c' for pinch calib, 'o' for circle calib, 'q' to quit.")
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while True:
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frames = [cap.read()[1] for cap in caps]
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frame = next((f for f in frames if f is not None), None)
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if frame is None:
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break
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# process
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full_view = process_frame(frame)
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# create proj output
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proj = np.zeros_like(full_view)
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if cal_circle:
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cx,cy,cr = cal_circle
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cv2.circle(proj,(cx,cy),cr,(0,0,255),2)
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if measuring and start_pt and fingertip_idx_global:
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cv2.line(proj, start_pt, fingertip_idx_global, (255,0,0),2)
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px = compute_distance(start_pt, fingertip_idx_global)
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inch = px/PIXELS_PER_INCH; cm = inch*2.54
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mid = ((start_pt[0]+fingertip_idx_global[0])//2,(start_pt[1]+fingertip_idx_global[1])//2)
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cv2.putText(proj,f"{inch:.2f}in/{cm:.1f}cm",(mid[0]+10,mid[1]-10),
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cv2.FONT_HERSHEY_SIMPLEX,0.7,(255,0,0),2)
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# overlay proj onto debug
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debug = full_view.copy()
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# overlay ref circle
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if cal_circle:
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cx,cy,cr = cal_circle
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cv2.circle(debug,(cx,cy),cr,(0,0,255),2)
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# overlay measurement
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if measuring and start_pt and fingertip_idx_global:
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cv2.line(debug, start_pt, fingertip_idx_global, (255,0,0),2)
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cv2.putText(debug,f"{inch:.2f}in/{cm:.1f}cm",(mid[0]+10,mid[1]-10),
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cv2.FONT_HERSHEY_SIMPLEX,0.7,(255,0,0),2)
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# show windows
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cv2.imshow('Hand Measure', debug)
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cv2.imshow('Projector Output', proj)
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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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global calibrating
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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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global object_calibrating
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object_calibrating = True
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print("Entered circle calibration mode.")
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for cap in caps:
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cap.release()
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cv2.destroyAllWindows()
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if __name__ == '__main__':
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main() |