Python：一个单针刻度表的辨认步伐 - ToB企服应用市场:ToB评测及商务社交产业平台

import cv2
import numpy as np
import math
import matplotlib.pyplot as plt
def preprocess_image(image_path):
# 读取图像
img = cv2.imread(image_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 图像去噪
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
# 边缘检测
edges = cv2.Canny(blurred, 50, 150)
return img, gray, edges
def detect_dial_contour(edges):
# 检测圆形轮廓
circles = cv2.HoughCircles(edges, cv2.HOUGH_GRADIENT, 1, 20,
param1=50, param2=30, minRadius=10, maxRadius=100)
if circles is not None:
circles = np.round(circles[0, :]).astype("int")
return circles
return None
def detect_hand(gray, circles):
if circles is None:
return None, None
# 选择最大的圆作为表盘
(cX, cY, cR) = circles[0]
mask = np.zeros_like(gray)
cv2.circle(mask, (cX, cY), cR, 255, -1)
masked_gray = cv2.bitwise_and(gray, gray, mask=mask)
# 找到针
_, thresh = cv2.threshold(masked_gray, 1, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours:
# 假设最大的轮廓是指针
contour = max(contours, key=cv2.contourArea)
M = cv2.moments(contour)
if M["m00"] != 0:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
return (cX, cY), (cX, cY)
return None, None
def calculate_angle(hand_point, center):
dx = hand_point[0] - center[0]
dy = hand_point[1] - center[1]
angle = math.atan2(dy, dx) * 180 / np.pi
if angle < 0:
angle += 360
return angle
def main(image_path):
img, gray, edges = preprocess_image(image_path)
circles = detect_dial_contour(edges)
hand_point, center = detect_hand(gray, circles)
if hand_point is not None:
# Calculate angle
angle = calculate_angle(hand_point, center)
print(f"Detected angle: {angle:.2f} degrees")
# Draw results
cv2.circle(img, center, 5, (0, 255, 0), -1)
cv2.line(img, center, hand_point, (0, 0, 255), 2)
cv2.imshow("Clock", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
else:
print("No hand detected")
if __name__ == "__main__":
main("clock.jpg")

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