更新60液面diff代码

yemian_seg_diff_60/main.py

@@ -0,0 +1,291 @@
+import os
+import cv2
+import numpy as np
+from rknnlite.api import RKNNLite
+
+# ---------------------------
+# 配置
+# ---------------------------
+ROIS = [
+    (604,182,594,252),
+]
+
+IMG_SIZE = 640
+STRIDES = [8, 16, 32]
+OBJ_THRESH = 0.25
+MASK_THRESH = 0.5
+
+_global_rknn = None
+
+# ---------------------------
+# RKNN 全局加载
+# ---------------------------
+def init_rknn_model(model_path):
+    global _global_rknn
+    if _global_rknn is not None:
+        return _global_rknn
+
+    rknn = RKNNLite(verbose=False)
+    ret = rknn.load_rknn(model_path)
+    if ret != 0:
+        raise RuntimeError(f"Load RKNN failed: {ret}")
+    ret = rknn.init_runtime()
+    if ret != 0:
+        raise RuntimeError(f"Init runtime failed: {ret}")
+
+    _global_rknn = rknn
+    print(f"[INFO] RKNN Seg 模型加载成功: {model_path}")
+    return rknn
+
+# ---------------------------
+# 工具函数
+# ---------------------------
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
+
+def dfl_decode(dfl):
+    bins = np.arange(16)
+    dfl = sigmoid(dfl)
+    dfl /= np.sum(dfl, axis=1, keepdims=True)
+    return np.sum(dfl * bins, axis=1)
+
+def largest_intersect_cc(mask_bin, bbox):
+    x1, y1, x2, y2 = bbox
+    contours, _ = cv2.findContours(mask_bin, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    if not contours:
+        return np.zeros_like(mask_bin, dtype=np.uint8)
+
+    max_inter = 0
+    best = np.zeros_like(mask_bin, dtype=np.uint8)
+    for cnt in contours:
+        tmp = np.zeros_like(mask_bin, dtype=np.uint8)
+        cv2.drawContours(tmp, [cnt], -1, 1, -1)
+        cx, cy, cw, ch = cv2.boundingRect(cnt)
+        ix1 = max(cx, x1)
+        iy1 = max(cy, y1)
+        ix2 = min(cx + cw, x2)
+        iy2 = min(cy + ch, y2)
+        area = max(0, ix2 - ix1) * max(0, iy2 - iy1)
+        if area > max_inter:
+            max_inter = area
+            best = tmp
+    return best
+
+# ---------------------------
+# RANSAC 直线拟合（核心新增）
+# ---------------------------
+def fit_line_ransac(pts, max_dist=2.5, min_inliers_ratio=0.6, iters=100):
+    """
+    拟合 x = m*y + b
+    pts: Nx2 -> [x,y]
+    """
+    if len(pts) < 10:
+        return None
+
+    xs = pts[:, 0]
+    ys = pts[:, 1]
+
+    best_m, best_b = None, None
+    best_inliers = 0
+
+    for _ in range(iters):
+        idx = np.random.choice(len(pts), 2, replace=False)
+        y1, y2 = ys[idx]
+        x1, x2 = xs[idx]
+        if abs(y2 - y1) < 1e-3:
+            continue
+
+        m = (x2 - x1) / (y2 - y1)
+        b = x1 - m * y1
+
+        x_pred = m * ys + b
+        dist = np.abs(xs - x_pred)
+        inliers = dist < max_dist
+        cnt = np.sum(inliers)
+
+        if cnt > best_inliers:
+            best_inliers = cnt
+            best_m, best_b = m, b
+
+    if best_m is None:
+        return None
+
+    if best_inliers / len(pts) < min_inliers_ratio:
+        return None
+
+    return best_m, best_b
+
+# ---------------------------
+# Seg 推理
+# ---------------------------
+def seg_infer(roi):
+    rknn = _global_rknn
+    h0, w0 = roi.shape[:2]
+
+    inp_img = cv2.resize(roi, (IMG_SIZE, IMG_SIZE))
+    inp = inp_img[..., ::-1][None, ...]  # BGR -> RGB
+    outputs = rknn.inference([inp])
+
+    proto = outputs[12][0]
+    proto_h, proto_w = proto.shape[1:]
+
+    best_score = -1
+    best_coef = None
+    best_bbox = None
+
+    out_i = 0
+    for stride in STRIDES:
+        reg = outputs[out_i][0]
+        cls = outputs[out_i + 1][0, 0]
+        obj = outputs[out_i + 2][0, 0]
+        coef = outputs[out_i + 3][0]
+        out_i += 4
+
+        score_map = sigmoid(cls) * sigmoid(obj)
+        y, x = np.unravel_index(np.argmax(score_map), score_map.shape)
+        score = score_map[y, x]
+
+        if score > best_score and score > OBJ_THRESH:
+            best_score = score
+            best_coef = coef[:, y, x]
+
+            dfl = reg[:, y, x].reshape(4, 16)
+            l, t, r, b = dfl_decode(dfl)
+
+            cx = (x + 0.5) * stride
+            cy = (y + 0.5) * stride
+
+            scale = proto_w / IMG_SIZE
+            x1 = int((cx - l) * scale)
+            y1 = int((cy - t) * scale)
+            x2 = int((cx + r) * scale)
+            y2 = int((cy + b) * scale)
+
+            best_bbox = (
+                max(0, x1), max(0, y1),
+                min(proto_w, x2), min(proto_h, y2)
+            )
+
+    if best_coef is None:
+        return np.zeros((h0, w0), dtype=np.uint8)
+
+    proto_mask = sigmoid(np.tensordot(best_coef, proto, axes=1)) > MASK_THRESH
+    proto_mask = proto_mask.astype(np.uint8)
+
+    mask_final = largest_intersect_cc(proto_mask, best_bbox)
+    mask_roi = cv2.resize(mask_final, (w0, h0), interpolation=cv2.INTER_NEAREST) * 255
+    return mask_roi.astype(np.uint8)
+
+# ---------------------------
+# PC 后处理
+# ---------------------------
+def extract_left_right_edge_points(mask_bin):
+    h, w = mask_bin.shape
+    left_pts, right_pts = [], []
+    for y in range(h):
+        xs = np.where(mask_bin[y] > 0)[0]
+        if len(xs) >= 2:
+            left_pts.append([xs.min(), y])
+            right_pts.append([xs.max(), y])
+    return np.array(left_pts), np.array(right_pts)
+
+def filter_by_seg_y_ratio(pts, y_start=0.35, y_end=0.85):
+    if len(pts) < 2:
+        return pts
+    y_min, y_max = pts[:, 1].min(), pts[:, 1].max()
+    h = y_max - y_min
+    if h < 10:
+        return pts
+    y0 = y_min + int(h * y_start)
+    y1 = y_min + int(h * y_end)
+    return pts[(pts[:, 1] >= y0) & (pts[:, 1] <= y1)]
+
+def get_y_ref(mask_bin):
+    h, w = mask_bin.shape
+    ys = []
+    for x in range(int(w * 0.2), int(w * 0.8)):
+        y = np.where(mask_bin[:, x] > 0)[0]
+        if len(y):
+            ys.append(y.max())
+    return int(np.mean(ys)) if ys else h // 2
+
+# ---------------------------
+# 单张图计算函数
+# ---------------------------
+def caculate_yemian_diff(img, return_vis=True):
+    if _global_rknn is None:
+        raise RuntimeError("请先 init_rknn_model()")
+
+    vis = img.copy() if return_vis else None
+    result_data = None
+
+    for rx, ry, rw, rh in ROIS:
+        roi = img[ry:ry + rh, rx:rx + rw]
+        mask_bin = seg_infer(roi) // 255
+
+        if return_vis:
+            green = np.zeros_like(roi)
+            green[mask_bin == 1] = (0, 255, 0)
+            vis[ry:ry + rh, rx:rx + rw] = cv2.addWeighted(
+                roi, 0.7, green, 0.3, 0
+            )
+
+        left_pts, right_pts = extract_left_right_edge_points(mask_bin)
+        left_pts = filter_by_seg_y_ratio(left_pts)
+        right_pts = filter_by_seg_y_ratio(right_pts)
+
+        left_line = fit_line_ransac(left_pts)
+        right_line = fit_line_ransac(right_pts)
+        if left_line is None or right_line is None:
+            continue
+
+        m1, b1 = left_line
+        m2, b2 = right_line
+
+        y_ref = get_y_ref(mask_bin)
+        x_left = int(m1 * y_ref + b1)
+        x_right = int(m2 * y_ref + b2)
+
+        X_L, X_R, Y = rx + x_left, rx + x_right, ry + y_ref
+        diff = X_R - X_L
+        result_data = (X_L, Y, X_R, Y, diff)
+
+        if return_vis:
+            roi_vis = vis[ry:ry + rh, rx:rx + rw]
+            cv2.line(roi_vis, (int(b1), 0), (int(m1 * rh + b1), rh), (0, 0, 255), 3)
+            cv2.line(roi_vis, (int(b2), 0), (int(m2 * rh + b2), rh), (255, 0, 0), 3)
+            cv2.line(roi_vis, (0, y_ref), (rw, y_ref), (0, 255, 255), 2)
+            cv2.circle(roi_vis, (x_left, y_ref), 6, (0, 0, 255), -1)
+            cv2.circle(roi_vis, (x_right, y_ref), 6, (255, 0, 0), -1)
+            cv2.putText(
+                roi_vis, f"diff={diff}px",
+                (10, 40),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                1, (0, 255, 255), 2
+            )
+
+    return result_data, vis
+
+# ---------------------------
+# main
+# ---------------------------
+if __name__ == "__main__":
+    RKNN_MODEL_PATH = "seg700.rknn"
+    IMAGE_PATH = "7.png"
+
+    init_rknn_model(RKNN_MODEL_PATH)
+
+    img = cv2.imread(IMAGE_PATH)
+    if img is None:
+        raise FileNotFoundError(IMAGE_PATH)
+
+    result_data, vis_img = caculate_yemian_diff(img, return_vis=True)
+
+    if result_data:
+        XL, YL, XR, YR, diff = result_data
+        print(f"左交点: ({XL},{YL}) 右交点: ({XR},{YR}) diff={diff}px")
+
+    if vis_img is not None:
+        cv2.imwrite("vis_output.png", vis_img)
+        print("可视化结果保存到 vis_output.png")
+