问题 BL: 判断三角形形状

题目描述

给你三角形的三条边,你能告诉我它是哪种三角形吗?
如果是直角三角形,请输出“good”。如果是等腰三角形,请输出“perfect”。否则,请输出“just a triangle”。
题目保证输入数据合法。

输入

输入的第一行为一个整数t,表示测试样例的数量。
每组样例包含了三个整数a,b,c,代表了三角形的三条边的长度。(0<a,b,c<300)

输出

对于每组样例,输出结果,每组结果占一行。

样例输入 Copy

4
3 4 5
2 2 3 
1 4 4
4 6 3

样例输出 Copy

good
perfect
perfect
just a triangle

代码

#include<iostream>
#include<math.h>
using namespace std;
int main() {
	int n,a,b,c,temp;
	while (cin >> n) {
		for (int i = 0; i < n; i++) {
			cin >> a >> b >> c;
			if (a + b <= c || a + c <= b || b + c <= a || a <= 0 || b <= 0 || c <= 0 || a >= 300 || b >= 300 || c >= 300) return 0;
			if (a < b) {
				temp = b;
				b = a;
				a = temp;
			}
			if (a < c) {
				temp = c;
				c = a;
				a = temp;
			}
			if (a == b || b == c||a==c) {
				cout << "perfect" << endl;
			}
			else if (pow(a, 2) == pow(b, 2) + pow(c, 2)) {
				cout << "good" << endl;
			}
			else {
				cout << "just a triangle" << endl;
			}
		}
	}
}
/* HDU3188 Just A Triangle */
 
#include <iostream>
 
using namespace std;
 
int main()
{
    int t, a, b, c;
 
    cin >> t;
    while(t--) {
        cin >> a >> b >> c;
 
        if(a == b || b == c || a == c)
            cout << "perfect" << endl;
        else if(a * a + b * b == c * c || b * b + c * c == a * a || a * a + c * c == b * b)
            cout << "good" << endl;
        else
            cout << "just a triangle" << endl;
    }
 
    return 0;
}
import cv2 import numpy as np import time # A4纸标准尺寸 (单位:mm) A4_WIDTH = 210 A4_HEIGHT = 297 BLACK_EDGE_WIDTH_MM = 20 # 2cm黑边 def detect_a4_paper_with_black_edge(frame): """检测带有2cm黑边的A4纸""" start_time = time.time() # 调整输入图像尺寸 frame = cv2.resize(frame, (800, 600)) output_frame = frame.copy() # 创建灰度处理图像 gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) blurred = cv2.GaussianBlur(gray, (7, 7), 0) edged = cv2.Canny(blurred, 30, 120) # 灰度图像显示准备 gray_display = cv2.cvtColor(edged, cv2.COLOR_GRAY2BGR) # 查找A4纸轮廓 - 特别关注外边缘 contours, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) a4_contour = None for contour in contours: if cv2.contourArea(contour) < 5000: continue peri = cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, 0.02 * peri, True) # 只考虑凸四边形 if len(approx) == 4 and cv2.isContourConvex(approx): a4_contour = approx break extracted_image = None has_black_edge = False if a4_contour is not None: # 绘制A4纸外边缘轮廓(黑色边框外边缘) cv2.drawContours(output_frame, [a4_contour], -1, (0, 0, 255), 2) cv2.putText(output_frame, "A4纸外边缘", (a4_contour[0][0][0], a4_contour[0][0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2) # 透视变换准备 pts = a4_contour.reshape(4, 2) rect = np.zeros((4, 2), dtype="float32") s = pts.sum(axis=1) rect[0] = pts[np.argmin(s)] # 左上 rect[2] = pts[np.argmax(s)] # 右下 diff = np.diff(pts, axis=1) rect[1] = pts[np.argmin(diff)] # 右上 rect[3] = pts[np.argmax(diff)] # 左下 # 计算A4纸尺寸 (tl, tr, br, bl) = rect widthA = np.linalg.norm(br - bl) widthB = np.linalg.norm(tr - tl) maxWidth = max(int(widthA), int(widthB)) heightA = np.linalg.norm(tr - br) heightB = np.linalg.norm(tl - bl) maxHeight = max(int(heightA), int(heightB)) # 目标点定义 dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype="float32") try: # 执行透视变换 M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(frame, M, (maxWidth, maxHeight)) extracted_image = warped.copy() # 计算像素到毫米的转换比例 px_per_mm_width = maxWidth / A4_WIDTH px_per_mm_height = maxHeight / A4_HEIGHT px_per_mm = min(px_per_mm_width, px_per_mm_height) # 计算2cm黑边对应的像素宽度 edge_px = int(BLACK_EDGE_WIDTH_MM * px_per_mm) # 创建黑边检测区域 (向内缩进2cm) inner_rect = np.array([ [edge_px, edge_px], [maxWidth - 1 - edge_px, edge_px], [maxWidth - 1 - edge_px, maxHeight - 1 - edge_px], [edge_px, maxHeight - 1 - edge_px] ], dtype=np.int32) # 检测黑边 - 使用HSV颜色空间提高准确性 warped_hsv = cv2.cvtColor(warped, cv2.COLOR_BGR2HSV) # 创建黑边检测掩码 edge_mask = np.zeros(warped.shape[:2], dtype=np.uint8) cv2.fillPoly(edge_mask, [inner_rect], 255) edge_mask = cv2.bitwise_not(edge_mask) # 分析边缘区域颜色 (使用HSV值) edge_region = cv2.bitwise_and(warped_hsv, warped_hsv, mask=edge_mask) edge_v = edge_region[:, :, 2] # 提取亮度通道 # 判断是否为黑边 (平均亮度值 < 30) if np.mean(edge_v) < 30: has_black_edge = True cv2.putText(output_frame, "检测到2cm黑边", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2) # 在矫正图像上绘制黑边区域 cv2.polylines(warped, [inner_rect], True, (0, 255, 255), 3) cv2.putText(warped, "黑边区域", (inner_rect[0][0], inner_rect[0][1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2) # 提取内部图像 (去除黑边) inner_mask = np.zeros(warped.shape[:2], dtype=np.uint8) cv2.fillPoly(inner_mask, [inner_rect], 255) extracted_image = cv2.bitwise_and(warped, warped, mask=inner_mask) # 在输出帧左上角显示矫正后的图像 h, w = warped.shape[:2] scale = min(300 / h, 300 / w) warped_display = cv2.resize(warped, (int(w * scale), int(h * scale))) h_disp, w_disp = warped_display.shape[:2] output_frame[10:10 + h_disp, 10:10 + w_disp] = warped_display # 在右上角显示提取的内部图像 if extracted_image is not None: h_inner, w_inner = extracted_image.shape[:2] scale_inner = min(200 / h_inner, 200 / w_inner) extracted_display = cv2.resize(extracted_image, (int(w_inner * scale_inner), int(h_inner * scale_inner))) h_ext, w_ext = extracted_display.shape[:2] output_frame[10:10 + h_ext, output_frame.shape[1] - w_ext - 10:output_frame.shape[1] - 10] = extracted_display cv2.putText(output_frame, "内部图像", (output_frame.shape[1] - w_ext - 10, 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 1) except Exception as e: print(f"处理出错: {e}") # 显示帧率 fps = 1.0 / (time.time() - start_time) cv2.putText(output_frame, f"帧率: {int(fps)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) return output_frame, gray_display, extracted_image def main(): cap = cv2.VideoCapture(0) if not cap.isOpened(): print("无法打开摄像头") return print("程序已启动 - 检测带黑边的A4纸") print("按 'q' 键退出程序") # 创建三个命名窗口并设置初始位置 cv2.namedWindow("A4纸检测") cv2.moveWindow("A4纸检测", 50, 50) cv2.namedWindow("边缘处理") cv2.moveWindow("边缘处理", 700, 50) cv2.namedWindow("提取内容") cv2.moveWindow("提取内容", 50, 400) try: while True: ret, frame = cap.read() if not ret: break # 处理帧 processed_frame, gray_frame, extracted_img = detect_a4_paper_with_black_edge(frame) # 显示三个窗口 cv2.imshow("A4纸检测", processed_frame) cv2.imshow("边缘处理", gray_frame) # 显示提取的图像 if extracted_img is not None: cv2.imshow("提取内容", extracted_img) else: placeholder = np.zeros((300, 300, 3), dtype=np.uint8) cv2.putText(placeholder, "等待检测带黑边的A4纸...", (20, 150), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2) cv2.imshow("提取内容", placeholder) # 调整窗口位置确保不重叠 cv2.moveWindow("A4纸检测", 50, 50) cv2.moveWindow("边缘处理", 700, 50) cv2.moveWindow("提取内容", 50, 400) if cv2.waitKey(1) & 0xFF == ord('q'): break finally: cap.release() cv2.destroyAllWindows() print("程序已退出") if __name__ == "__main__": main() 测量出图中a4纸的长度与宽度,以及三角形或矩形或圆形的面积
07-31
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