#OpenCV实战指南：从像素到深度学习的核心应用

import cv2
print(f"✅ OpenCV版本: {cv2.__version__}")

import cv2
import sys

def core_image_ops(img_path):
    # 1. 载入/显示/保存三部曲
    img = cv2.imread(img_path)
    if img is None: sys.exit("❌ 请检查文件路径")
    print(f"📐 图像形状(高,宽,通道): {img.shape}")
    
    # 2. 感兴趣区域(ROI)：提取矩形
    roi = img[50:200, 50:200]  # y:y+h, x:x+w
    
    # 3. 通道操作(默认BGR)
    img_red_zero = img.copy()
    img_red_zero[:, :, 2] = 0  # 清零红色通道
    
    # 展示并保存
    cv2.imshow("原图", img)
    cv2.imshow("红清零", img_red_zero)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    cv2.imwrite("red_zero.jpg", img_red_zero)
    
    return img, roi

# core_image_ops("test.jpg")

def color_convert(img):
    # 1. 转灰度
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 2. 转HSV
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    
    cv2.imshow("原图", img)
    cv2.imshow("灰度", gray)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    
    return gray, hsv

def adaptive_threshold(gray):
    # 高斯加权均值法：自适应邻域阈值
    thresh = cv2.adaptiveThreshold(
        gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
        cv2.THRESH_BINARY, 11, 2  # 核大小11，常数C=2
    )
    cv2.imshow("自适应二值化", thresh)
    cv2.waitKey(0)
    return thresh

def canny_edge(gray):
    # 先高斯模糊降噪，再Canny
    blurred = cv2.GaussianBlur(gray, (5,5), 0)
    edges = cv2.Canny(blurred, 50, 150)  # 低/高阈值
    cv2.imshow("Canny边缘", edges)
    cv2.waitKey(0)
    return edges

import numpy as np
def morph_ops(thresh):
    kernel = np.ones((3,3), np.uint8)  # 3x3正方形核
    # 开运算：先腐蚀后膨胀→去外部小白点
    opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
    # 闭运算：先膨胀后腐蚀→填内部小黑洞
    closing = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
    return opening, closing

def realtime_face_detect():
    # 加载内置Haar模型（路径兼容各环境）
    face_cascade = cv2.CascadeClassifier(
        cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
    )
    cap = cv2.VideoCapture(0)  # 0为默认摄像头
    print("⚠️ 按 q 退出")
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret: break
        
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        # 检测人脸：scaleFactor=1.1(每次缩10%)，minNeighbors=5(候选框数量)
        faces = face_cascade.detectMultiScale(gray, 1.1, 5, minSize=(30,30))
        
        # 绘制矩形
        for (x,y,w,h) in faces:
            cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 2)
        
        cv2.imshow("实时人脸检测", frame)
        if cv2.waitKey(1) & 0xFF == ord('q'): break
    
    cap.release()
    cv2.destroyAllWindows()

# realtime_face_detect()

def yolo_detect(img_path, weights, cfg, coco):
    # 1. 加载模型与标签
    net = cv2.dnn.readNet(weights, cfg)
    with open(coco, 'r') as f:
        classes = [line.strip() for line in f.readlines()]
    colors = np.random.uniform(0,255, (len(classes),3))
    
    # 2. 获取输出层（兼容新旧OpenCV）
    layer_names = net.getLayerNames()
    try:
        output_layers = [layer_names[i-1] for i in net.getUnconnectedOutLayers()]
    except:
        output_layers = [layer_names[i] for i in net.getUnconnectedOutLayers()]
    
    # 3. 预处理与推理
    img = cv2.imread(img_path)
    h,w = img.shape[:2]
    blob = cv2.dnn.blobFromImage(
        img, 1/255.0, (416,416), swapRB=True, crop=False
    )
    net.setInput(blob)
    outputs = net.forward(output_layers)
    
    # 4. 解析结果+非极大值抑制(NMS)去重
    boxes, confs, class_ids = [], [], []
    for out in outputs:
        for det in out:
            scores = det[5:]
            cls_id = np.argmax(scores)
            conf = scores[cls_id]
            if conf > 0.5:
                cx, cy, bw, bh = (det[:4] * [w,h,w,h]).astype('int')
                x = int(cx - bw/2)
                y = int(cy - bh/2)
                boxes.append([x,y,int(bw),int(bh)])
                confs.append(float(conf))
                class_ids.append(cls_id)
    idxs = cv2.dnn.NMSBoxes(boxes, confs, 0.5, 0.4)
    
    # 5. 绘制结果
    if len(idxs) > 0:
        for i in idxs.flatten():
            x,y,w,h = boxes[i]
            label = f"{classes[class_ids[i]]} {confs[i]:.2f}"
            color = colors[class_ids[i]]
            cv2.rectangle(img, (x,y), (x+w,y+h), color, 2)
            cv2.putText(img, label, (x,y-10),
                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
    
    cv2.imshow("YOLO检测", img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

# yolo_detect("test.jpg", "yolov3-tiny.weights", "yolov3-tiny.cfg", "coco.names")