更新60液面diff代码

yemian_seg_diff-area_60/60seg_diff_area.py

@@ -0,0 +1,215 @@
+import cv2
+import numpy as np
+from rknnlite.api import RKNNLite
+
+# ---------------------------
+# 配置
+# ---------------------------
+ROIS = [(604, 182, 594, 252)]  # (x, y, w, h)
+
+IMG_SIZE = 640
+STRIDES = [8, 16, 32]
+OBJ_THRESH = 0.25
+MASK_THRESH = 0.5
+
+_global_rknn = None
+
+
+def init_rknn_model(model_path):
+    global _global_rknn
+    if _global_rknn is not None:
+        return
+    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 model loaded: {model_path}")
+
+
+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
+
+
+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, ...]
+    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)
+
+
+# ---------------------------
+# 主函数：支持可选可视化
+# ---------------------------
+def caculate_yemian_diff(img, visualize=False):
+    """
+    输入:
+        img: BGR 图像 (H, W, 3) np.ndarray
+        visualize: bool, 是否生成可视化结果
+
+    输出:
+        若 visualize=False:
+            (diff14: float, diff43: float, mask_area: int)
+        若 visualize=True:
+            (diff14: float, diff43: float, mask_area: int, vis_img: np.ndarray)
+
+        失败时返回 (0.0, 0.0, 0) 或 (0.0, 0.0, 0, original_img)
+    """
+    if _global_rknn is None:
+        raise RuntimeError("RKNN model not initialized. Call init_rknn_model() first.")
+
+    vis_img = img.copy() if visualize else None
+
+    for (rx, ry, rw, rh) in ROIS:
+        roi = img[ry:ry + rh, rx:rx + rw]
+        mask_full = seg_infer(roi)
+        mask_bin = mask_full // 255
+        mask_area = int(np.sum(mask_bin))
+
+        if visualize:
+            green = np.zeros_like(roi)
+            green[mask_bin == 1] = (0, 255, 0)
+            vis_img[ry:ry + rh, rx:rx + rw] = cv2.addWeighted(roi, 0.7, green, 0.3, 0)
+
+        contours, _ = cv2.findContours(mask_bin, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+        if not contours:
+            continue
+        cnt = max(contours, key=cv2.contourArea)
+        if len(cnt) < 20:
+            continue
+
+        pts_all = cnt.reshape(-1, 2)
+        P1 = pts_all[np.argmin(pts_all[:, 0])]  # x_min
+        P4 = pts_all[np.argmin(pts_all[:, 1])]  # y_min
+        P3 = pts_all[np.argmax(pts_all[:, 0])]  # x_max
+
+        # 全局坐标
+        P1_g = (int(P1[0] + rx), int(P1[1] + ry))
+        P4_g = (int(P4[0] + rx), int(P4[1] + ry))
+        P3_g = (int(P3[0] + rx), int(P3[1] + ry))
+
+        diff14 = float(np.linalg.norm(np.array(P1_g) - np.array(P4_g)))
+        diff43 = float(np.linalg.norm(np.array(P4_g) - np.array(P3_g)))
+
+        if visualize:
+            roi_vis = vis_img[ry:ry + rh, rx:rx + rw]
+            local_pts = [P1.astype(int), P4.astype(int), P3.astype(int)]
+            colors = [(255, 0, 0), (0, 255, 0)]
+            lengths = [diff14, diff43]
+            # 画线
+            cv2.line(vis_img, P1_g, P4_g, colors[0], 2)
+            cv2.line(vis_img, P4_g, P3_g, colors[1], 2)
+            # 标长度
+            mid14 = ((P1_g[0] + P4_g[0]) // 2, (P1_g[1] + P4_g[1]) // 2 - 10)
+            mid43 = ((P4_g[0] + P3_g[0]) // 2, (P4_g[1] + P3_g[1]) // 2 - 10)
+            cv2.putText(vis_img, f"{diff14:.1f}", mid14, cv2.FONT_HERSHEY_SIMPLEX, 0.5, colors[0], 1)
+            cv2.putText(vis_img, f"{diff43:.1f}", mid43, cv2.FONT_HERSHEY_SIMPLEX, 0.5, colors[1], 1)
+            # 标点名
+            labels = ["P1", "P4", "P3"]
+            points = [P1_g, P4_g, P3_g]
+            offsets = [(-25, -10), (-25, -10), (10, -10)]
+            for lab, pt, off in zip(labels, points, offsets):
+                x = max(10, min(pt[0] + off[0], img.shape[1] - 50))
+                y = max(20, min(pt[1] + off[1], img.shape[0] - 10))
+                cv2.putText(vis_img, lab, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+            # 面积
+            cv2.putText(vis_img, f"Area: {mask_area}", (rx + 10, ry + 30),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+
+        if visualize:
+            return diff14, diff43, mask_area, vis_img
+        else:
+            return diff14, diff43, mask_area
+
+    # 检测失败
+    if visualize:
+        return 0.0, 0.0, 0, img.copy()
+    else:
+        return 0.0, 0.0, 0
+
+
+# ---------------------------
+# 示例用法
+# ---------------------------
+if __name__ == "__main__":
+    init_rknn_model("60seg.rknn")
+    img = cv2.imread("1.png")
+    if img is None:
+        raise FileNotFoundError("1.png")
+
+    # 不可视化
+    d14, d43, area = caculate_yemian_diff(img)
+    print(f"Without vis: {d14:.2f}, {d43:.2f}, {area}")
+
+    # 可视化
+    d14, d43, area, vis = caculate_yemian_diff(img, visualize=True)
+    cv2.imwrite("output_vis.png", vis)
+    print(f"With vis: saved to output_vis.png")