Initial commit

obb_angle/obb_angle.py

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+import cv2
+import numpy as np
+import math
+from shapely.geometry import Polygon
+from rknnlite.api import RKNNLite
+import os
+
+# ------------------- 全局配置变量 -------------------
+# 模型相关
+CLASSES = ['clamp']
+nmsThresh = 0.4
+objectThresh = 0.35
+
+# 可视化与保存控制（全局变量，可外部修改）
+DRAW_RESULT = True      # 是否在输出图像上绘制旋转框
+SAVE_PATH = None        # 保存路径，如 "./result.jpg"；设为 None 则不保存
+
+# RKNN 单例
+_rknn_instance = None
+
+# ------------------- RKNN 管理函数 -------------------
+def init_rknn(model_path):
+    """只加载一次 RKNN 模型"""
+    global _rknn_instance
+    if _rknn_instance is None:
+        _rknn_instance = RKNNLite(verbose=False)
+        ret = _rknn_instance.load_rknn(model_path)
+        if ret != 0:
+            print(f"[ERROR] Failed to load RKNN model: {ret}")
+            return None
+        ret = _rknn_instance.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
+        if ret != 0:
+            print(f"[ERROR] Failed to init runtime: {ret}")
+            return None
+    return _rknn_instance
+
+def release_rknn():
+    """释放 RKNN 对象"""
+    global _rknn_instance
+    if _rknn_instance:
+        _rknn_instance.release()
+        _rknn_instance = None
+
+# ------------------- 工具函数 -------------------
+def letterbox_resize(image, size, bg_color=114):
+    target_width, target_height = size
+    image_height, image_width, _ = image.shape
+    scale = min(target_width / image_width, target_height / image_height)
+    new_width, new_height = int(image_width * scale), int(image_height * scale)
+    image_resized = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)
+    canvas = np.ones((target_height, target_width, 3), dtype=np.uint8) * bg_color
+    offset_x, offset_y = (target_width - new_width) // 2, (target_height - new_height) // 2
+    canvas[offset_y:offset_y + new_height, offset_x:offset_x + new_width] = image_resized
+    return canvas, scale, offset_x, offset_y
+
+class DetectBox:
+    def __init__(self, classId, score, xmin, ymin, xmax, ymax, angle):
+        self.classId = classId
+        self.score = score
+        self.xmin = xmin
+        self.ymin = ymin
+        self.xmax = xmax
+        self.ymax = ymax
+        self.angle = angle
+
+def rotate_rectangle(x1, y1, x2, y2, a):
+    cx, cy = (x1 + x2) / 2, (y1 + y2) / 2
+    cos_a, sin_a = math.cos(a), math.sin(a)
+    pts = [(x1, y1), (x1, y2), (x2, y2), (x2, y1)]
+    return [[int(cx + (xx - cx) * cos_a - (yy - cy) * sin_a),
+             int(cy + (xx - cx) * sin_a + (yy - cy) * cos_a)] for xx, yy in pts]
+
+def intersection(g, p):
+    g = Polygon(np.array(g).reshape(-1, 2))
+    p = Polygon(np.array(p).reshape(-1, 2))
+    if not g.is_valid or not p.is_valid:
+        return 0
+    inter = g.intersection(p).area
+    union = g.area + p.area - inter
+    return 0 if union == 0 else inter / union
+
+def NMS(detectResult):
+    predBoxs = []
+    sort_detectboxs = sorted(detectResult, key=lambda x: x.score, reverse=True)
+    for i in range(len(sort_detectboxs)):
+        if sort_detectboxs[i].classId == -1:
+            continue
+        p1 = rotate_rectangle(sort_detectboxs[i].xmin, sort_detectboxs[i].ymin,
+                              sort_detectboxs[i].xmax, sort_detectboxs[i].ymax,
+                              sort_detectboxs[i].angle)
+        predBoxs.append(sort_detectboxs[i])
+        for j in range(i + 1, len(sort_detectboxs)):
+            if sort_detectboxs[j].classId == sort_detectboxs[i].classId:
+                p2 = rotate_rectangle(sort_detectboxs[j].xmin, sort_detectboxs[j].ymin,
+                                      sort_detectboxs[j].xmax, sort_detectboxs[j].ymax,
+                                      sort_detectboxs[j].angle)
+                if intersection(p1, p2) > nmsThresh:
+                    sort_detectboxs[j].classId = -1
+    return predBoxs
+
+def sigmoid(x):
+    x = np.clip(x, -709, 709)  # 防止 exp 溢出
+    return np.where(x >= 0, 1 / (1 + np.exp(-x)), np.exp(x) / (1 + np.exp(x)))
+
+def softmax(x, axis=-1):
+    exp_x = np.exp(x - np.max(x, axis=axis, keepdims=True))
+    return exp_x / np.sum(exp_x, axis=axis, keepdims=True)
+
+def process(out, model_w, model_h, stride, angle_feature, index, scale_w=1, scale_h=1):
+    class_num = len(CLASSES)
+    angle_feature = angle_feature.reshape(-1)
+    xywh = out[:, :64, :]
+    conf = sigmoid(out[:, 64:, :])
+    conf = conf.reshape(-1)
+    boxes = []
+    for ik in range(model_h * model_w * class_num):
+        if conf[ik] > objectThresh:
+            w = ik % model_w
+            h = (ik % (model_w * model_h)) // model_w
+            c = ik // (model_w * model_h)
+            xywh_ = xywh[0, :, (h * model_w) + w].reshape(1, 4, 16, 1)
+            data = np.arange(16).reshape(1, 1, 16, 1)
+            xywh_ = softmax(xywh_, 2)
+            xywh_ = np.sum(xywh_ * data, axis=2).reshape(-1)
+            xywh_add = xywh_[:2] + xywh_[2:]
+            xywh_sub = (xywh_[2:] - xywh_[:2]) / 2
+            angle = (angle_feature[index + (h * model_w) + w] - 0.25) * math.pi
+            cos_a, sin_a = math.cos(angle), math.sin(angle)
+            xy = xywh_sub[0] * cos_a - xywh_sub[1] * sin_a, xywh_sub[0] * sin_a + xywh_sub[1] * cos_a
+            xywh1 = np.array([xy[0] + w + 0.5, xy[1] + h + 0.5, xywh_add[0], xywh_add[1]])
+            xywh1 *= stride
+            xmin = (xywh1[0] - xywh1[2] / 2) * scale_w
+            ymin = (xywh1[1] - xywh1[3] / 2) * scale_h
+            xmax = (xywh1[0] + xywh1[2] / 2) * scale_w
+            ymax = (xywh1[1] + xywh1[3] / 2) * scale_h
+            boxes.append(DetectBox(c, conf[ik], xmin, ymin, xmax, ymax, angle))
+    return boxes
+
+# ------------------- 主推理函数 -------------------
+def detect_two_box_angle(model_path, rgb_frame):
+    """
+    输入模型路径和 RGB 图像（numpy array），输出夹角和结果图像。
+    可视化和保存由全局变量 DRAW_RESULT 和 SAVE_PATH 控制。
+    """
+    global _rknn_instance, DRAW_RESULT, SAVE_PATH
+
+    if not isinstance(rgb_frame, np.ndarray) or rgb_frame is None:
+        print(f"[ERROR] detect_two_box_angle 接收到错误类型: {type(rgb_frame)}")
+        return None, np.zeros((640, 640, 3), np.uint8)
+
+    # 注意：输入是 BGR（因为 cv2.imread 返回 BGR），但内部会转为 RGB 给模型
+    img = rgb_frame.copy()
+    img_resized, scale, offset_x, offset_y = letterbox_resize(img, (640, 640))
+    infer_img = np.expand_dims(cv2.cvtColor(img_resized, cv2.COLOR_BGR2RGB), 0)
+
+    try:
+        rknn = init_rknn(model_path)
+        if rknn is None:
+            return None, img
+        results = rknn.inference([infer_img])
+    except Exception as e:
+        print(f"[ERROR] RKNN 推理失败: {e}")
+        return None, img
+
+    outputs = []
+    for x in results[:-1]:
+        index, stride = 0, 0
+        if x.shape[2] == 20:
+            stride, index = 32, 20*4*20*4 + 20*2*20*2
+        elif x.shape[2] == 40:
+            stride, index = 16, 20*4*20*4
+        elif x.shape[2] == 80:
+            stride, index = 8, 0
+        feature = x.reshape(1, 65, -1)
+        outputs += process(feature, x.shape[3], x.shape[2], stride, results[-1], index)
+
+    predbox = NMS(outputs)
+    print(f"[DEBUG] 检测到 {len(predbox)} 个框")
+
+    if len(predbox) < 2:
+        print("检测少于两个目标，无法计算夹角。")
+        return None, img
+
+    predbox = sorted(predbox, key=lambda x: x.score, reverse=True)
+    box1, box2 = predbox[:2]
+
+    output_img = img.copy() if DRAW_RESULT else img  # 若不绘制，则直接用原图
+
+    if DRAW_RESULT:
+        for box in [box1, box2]:
+            xmin = int((box.xmin - offset_x) / scale)
+            ymin = int((box.ymin - offset_y) / scale)
+            xmax = int((box.xmax - offset_x) / scale)
+            ymax = int((box.ymax - offset_y) / scale)
+            points = rotate_rectangle(xmin, ymin, xmax, ymax, box.angle)
+            cv2.polylines(output_img, [np.array(points, np.int32)], True, (0, 255, 0), 2)
+
+    def main_direction(box):
+        w, h = (box.xmax - box.xmin)/scale, (box.ymax - box.ymin)/scale
+        direction = box.angle if w >= h else box.angle + np.pi/2
+        return direction % np.pi
+
+    dir1 = main_direction(box1)
+    dir2 = main_direction(box2)
+    diff = abs(dir1 - dir2)
+    diff = min(diff, np.pi - diff)
+    angle_deg = np.degrees(diff)
+
+    # 保存结果（如果需要）
+    if SAVE_PATH:
+        save_dir = os.path.dirname(SAVE_PATH)
+        if save_dir:  # 非空目录才创建
+            os.makedirs(save_dir, exist_ok=True)
+        cv2.imwrite(SAVE_PATH, output_img)
+
+    return angle_deg, output_img
+
+# ------------------- 示例调用 -------------------
+if __name__ == "__main__":
+    MODEL_PATH = "./obb.rknn"
+    IMAGE_PATH = "./11.jpg"
+
+    # === 全局控制开关 ===
+    DRAW_RESULT = True          # 是否绘制框
+    SAVE_PATH = "./result11.jpg"  # 保存路径，设为 None 则不保存
+
+    frame = cv2.imread(IMAGE_PATH)
+    if frame is None:
+        print(f"[ERROR] 无法读取图像: {IMAGE_PATH}")
+    else:
+        angle_deg, output_image = detect_two_box_angle(MODEL_PATH, frame)
+        if angle_deg is not None:
+            print(f"检测到的角度差: {angle_deg:.2f}°")
+        else:
+            print("未能成功检测到目标或计算角度差")