diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..10b731c
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,5 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
diff --git a/.idea/ailai_image_obb.iml b/.idea/ailai_image_obb.iml
new file mode 100644
index 0000000..8770519
--- /dev/null
+++ b/.idea/ailai_image_obb.iml
@@ -0,0 +1,12 @@
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..adde6f5
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,7 @@
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..9c3175b
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..8306744
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,7 @@
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/ailai_pc/1.jpg b/ailai_pc/1.jpg
new file mode 100644
index 0000000..e69faee
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diff --git a/ailai_pc/2.jpg b/ailai_pc/2.jpg
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index 0000000..e6af4b6
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diff --git a/ailai_pc/22222.jpg b/ailai_pc/22222.jpg
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diff --git a/ailai_pc/3.jpg b/ailai_pc/3.jpg
new file mode 100644
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diff --git a/ailai_pc/best.pt b/ailai_pc/best.pt
index a9e6a58..70b8aa6 100644
Binary files a/ailai_pc/best.pt and b/ailai_pc/best.pt differ
diff --git a/ailai_pc/best1.pt b/ailai_pc/best1.pt
new file mode 100644
index 0000000..a9e6a58
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diff --git a/ailai_pc/detect.py b/ailai_pc/detect.py
new file mode 100644
index 0000000..a01c9da
--- /dev/null
+++ b/ailai_pc/detect.py
@@ -0,0 +1,134 @@
+import cv2
+import numpy as np
+from rknnlite.api import RKNNLite
+
+MODEL_PATH = "detect.rknn"
+CLASS_NAMES = ["bag"] # 单类
+
+
+class Yolo11Detector:
+ def __init__(self, model_path):
+ self.rknn = RKNNLite(verbose=False)
+
+ # 加载 RKNN 模型
+ ret = self.rknn.load_rknn(model_path)
+ assert ret == 0, "❌ Load RKNN model failed"
+
+ # 初始化运行时（使用 NPU 核心0）
+ ret = self.rknn.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
+ assert ret == 0, "❌ Init runtime failed"
+
+ # 模型输入大小
+ self.input_size = 640
+ # YOLO anchors（根据你训练的模型）
+ self.anchors = {
+ 8: [[10, 13], [16, 30], [33, 23]],
+ 16: [[30, 61], [62, 45], [59, 119]],
+ 32: [[116, 90], [156, 198], [373, 326]]
+ }
+
+ def preprocess(self, img):
+ """高性能预处理：缩放+RGB"""
+ h, w = img.shape[:2]
+ scale = min(self.input_size / w, self.input_size / h)
+ new_w, new_h = int(w * scale), int(h * scale)
+ img_resized = cv2.resize(img, (new_w, new_h))
+ canvas = np.full((self.input_size, self.input_size, 3), 114, dtype=np.uint8)
+ dw, dh = (self.input_size - new_w) // 2, (self.input_size - new_h) // 2
+ canvas[dh:dh + new_h, dw:dw + new_w, :] = img_resized
+ img_rgb = cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB)
+ return np.expand_dims(img_rgb, 0).astype(np.uint8), scale, dw, dh
+
+ def postprocess(self, outputs, scale, dw, dh, conf_thresh=0.25, iou_thresh=0.45):
+ """解析 YOLO 输出"""
+ # 注意：根据 RKNN 输出节点选择
+ preds = outputs[0].reshape(-1, outputs[0].shape[1]) # 假设输出 [1, N, C]
+ boxes, scores, class_ids = [], [], []
+
+ for p in preds:
+ conf = p[4]
+ if conf < conf_thresh:
+ continue
+ cls_conf = p[5] # 单类模型
+ score = conf * cls_conf
+ if score < conf_thresh:
+ continue
+ cx, cy, w, h = p[:4]
+ x1 = (cx - w / 2 - dw) / scale
+ y1 = (cy - h / 2 - dh) / scale
+ x2 = (cx + w / 2 - dw) / scale
+ y2 = (cy + h / 2 - dh) / scale
+ boxes.append([x1, y1, x2, y2])
+ scores.append(score)
+ class_ids.append(0) # 单类
+
+ if len(boxes) == 0:
+ return []
+
+ boxes = np.array(boxes)
+ scores = np.array(scores)
+ class_ids = np.array(class_ids)
+
+ # 简单 NMS
+ idxs = np.argsort(scores)[::-1]
+ keep = []
+ while len(idxs) > 0:
+ i = idxs[0]
+ keep.append(i)
+ if len(idxs) == 1:
+ break
+ x1, y1, x2, y2 = boxes[i]
+ xx1 = np.maximum(x1, boxes[idxs[1:], 0])
+ yy1 = np.maximum(y1, boxes[idxs[1:], 1])
+ xx2 = np.minimum(x2, boxes[idxs[1:], 2])
+ yy2 = np.minimum(y2, boxes[idxs[1:], 3])
+ inter = np.maximum(0, xx2 - xx1) * np.maximum(0, yy2 - yy1)
+ area_i = (x2 - x1) * (y2 - y1)
+ area_j = (boxes[idxs[1:], 2] - boxes[idxs[1:], 0]) * (boxes[idxs[1:], 3] - boxes[idxs[1:], 1])
+ iou = inter / (area_i + area_j - inter + 1e-6)
+ idxs = idxs[1:][iou < iou_thresh]
+
+ results = []
+ for i in keep:
+ results.append({
+ "box": boxes[i],
+ "score": scores[i],
+ "class_id": class_ids[i]
+ })
+ return results
+
+ def detect(self, img):
+ img_data, scale, dw, dh = self.preprocess(img)
+ outputs = self.rknn.inference([img_data])
+ results = self.postprocess(outputs, scale, dw, dh)
+ return results
+
+ def release(self):
+ self.rknn.release()
+
+
+if __name__ == "__main__":
+ detector = Yolo11Detector(MODEL_PATH)
+ cap = cv2.VideoCapture(0) # 可以换成图片路径
+
+ while True:
+ ret, frame = cap.read()
+ if not ret:
+ break
+
+ results = detector.detect(frame)
+
+ for r in results:
+ x1, y1, x2, y2 = map(int, r["box"])
+ cls_id = r["class_id"]
+ score = r["score"]
+ cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+ cv2.putText(frame, f"{CLASS_NAMES[cls_id]} {score:.2f}", (x1, y1 - 10),
+ cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+ cv2.imshow("YOLOv11 Detection", frame)
+ if cv2.waitKey(1) & 0xFF == ord('q'):
+ break
+
+ detector.release()
+ cap.release()
diff --git a/ailai_pc/detet_pc.py b/ailai_pc/detet_pc.py
new file mode 100644
index 0000000..b9abdef
--- /dev/null
+++ b/ailai_pc/detet_pc.py
@@ -0,0 +1,72 @@
+# detect_pt.py
+import cv2
+import torch
+from ultralytics import YOLO
+
+# ======================
+# 配置参数
+# ======================
+MODEL_PATH = 'best.pt' # 你的训练模型路径（yolov8n.pt 或你自己训练的）
+#IMG_PATH = '/home/hx/开发/ailai_image_obb/ailai_pc/train/192.168.0.234_01_202510141514352.jpg' # 测试图像路径
+IMG_PATH = '1.jpg'
+OUTPUT_PATH = '/home/hx/开发/ailai_image_obb/ailai_pc/output_pt.jpg' # 可视化结果保存路径
+CONF_THRESH = 0.5 # 置信度阈值
+CLASS_NAMES = ['bag'] # 你的类别名列表（按训练时顺序）
+
+# 是否显示窗口（适合有 GUI 的 PC）
+SHOW_IMAGE = True
+
+# ======================
+# 主函数
+# ======================
+def main():
+ # 检查 CUDA
+ device = 'cuda' if torch.cuda.is_available() else 'cpu'
+ print(f"✅ 使用设备: {device}")
+
+ # 加载模型
+ print("➡️ 加载 YOLO 模型...")
+ model = YOLO(MODEL_PATH) # 自动加载架构和权重
+ model.to(device)
+
+ # 推理
+ print("➡️ 开始推理...")
+ results = model(IMG_PATH, imgsz=640, conf=CONF_THRESH, device=device)
+
+ # 获取第一张图的结果
+ r = results[0]
+
+ # 获取原始图像（BGR）
+ img = cv2.imread(IMG_PATH)
+ if img is None:
+ raise FileNotFoundError(f"无法读取图像: {IMG_PATH}")
+
+ print("\n📋 检测结果:")
+ for box in r.boxes:
+ # 获取数据
+ xyxy = box.xyxy[0].cpu().numpy() # [x1, y1, x2, y2]
+ conf = box.conf.cpu().numpy()[0] # 置信度
+ cls_id = int(box.cls.cpu().numpy()[0]) # 类别 ID
+ cls_name = CLASS_NAMES[cls_id] # 类别名
+
+ x1, y1, x2, y2 = map(int, xyxy)
+ print(f" 类别: {cls_name}, 置信度: {conf:.3f}, 框: [{x1}, {y1}, {x2}, {y2}]")
+
+ # 画框
+ cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+ # 画标签
+ label = f"{cls_name} {conf:.2f}"
+ cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
+
+ # 保存结果
+ cv2.imwrite(OUTPUT_PATH, img)
+ print(f"\n🖼️ 可视化结果已保存: {OUTPUT_PATH}")
+
+ # 显示（可选）
+ if SHOW_IMAGE:
+ cv2.imshow("YOLOv8 Detection", img)
+ cv2.waitKey(0)
+ cv2.destroyAllWindows()
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/ailai_pc/output_pt.jpg b/ailai_pc/output_pt.jpg
new file mode 100644
index 0000000..d935dfa
Binary files /dev/null and b/ailai_pc/output_pt.jpg differ
diff --git a/ailai_pc/yolo_obb_dataset/1.jpg b/ailai_pc/yolo_obb_dataset/1.jpg
new file mode 100644
index 0000000..fd8a6cc
Binary files /dev/null and b/ailai_pc/yolo_obb_dataset/1.jpg differ
diff --git a/main/README.md b/main/README.md
index e53edcc..418fd8b 100644
--- a/main/README.md
+++ b/main/README.md
@@ -26,25 +26,50 @@
pip install opencv-python numpy rknnlite
```
-## 函数调用
+## 函数调用1.0
您也可以直接调用 calculate_offset_from_image 函数，以便集成到其他项目中：
示例 1: 仅获取偏移量（不画图）
-**
+
+```bash
from calculate_offset import calculate_offset_from_image
result = calculate_offset_from_image("your_image_path.jpg", visualize=False)
if result['success']:
print(f"Offset: DeltaX={result['dx_mm']:+.2f} mm, DeltaY={result['dy_mm']:+.2f} mm")
else:
print("Error:", result['message'])
-**
+```
示例 2: 获取偏移量并保存可视化图
-**
+
+```bash
from calculate_offset import calculate_offset_from_image
result = calculate_offset_from_image("your_image_path.jpg", visualize=True)
-**
+```
-该函数返回一个包含下列字段的字典：
+## 函数调用2.0
+
+示例 1: 仅获取偏移量（不画图）
+
+```bash
+from caculate_diff2.0 import calculate_offset_from_image
+
+result = calculate_offset_from_image("11.jpg", visualize=False)
+if result['success']:
+ print(f"Offset: DeltaX={result['dx_mm']:+.2f} mm, DeltaY={result['dy_mm']:+.2f} mm")
+else:
+ print("Error:", result['message'])
+
+```
+示例 2: 获取偏移量并保存可视化图
+
+```bash
+from caculate_diff2.0 import calculate_offset_from_image
+
+result = calculate_offset_from_image("11.jpg", visualize=True)
+
+```
+
+##该函数返回一个包含下列字段的字典1.0：
success: 成功标志（True/False）
dx_mm: 水平偏移（毫米）
@@ -52,3 +77,18 @@ result = calculate_offset_from_image("your_image_path.jpg", visualize=True)
cx: 中心点 x 坐标（像素）
cy: 中心点 y 坐标（像素）
message: 错误信息或成功提示
+
+##该函数返回一个包含下列字段的字典2.0：
+
+ success: 成功标志（True/False）
+ dx_mm: 水平偏移（毫米）
+ dy_mm: 垂直偏移（毫米）
+ cx: 中心点 x 坐标（像素）
+ cy: 中心点 y 坐标（像素）
+ message: 错误信息或成功提示
+ class_id: 检测类别 ID #这里是bag的id是0
+ obj_conf: 检测置信度 #这就是识别为料袋的置信度
+ bbox: 检测矩形框 [x_left, y_top, width, height]
+ message: 错误信息或成功提示
+
+
diff --git a/main/caculate_diff2.0.py b/main/caculate_diff2.0.py
new file mode 100644
index 0000000..549c8ed
--- /dev/null
+++ b/main/caculate_diff2.0.py
@@ -0,0 +1,235 @@
+import cv2
+import numpy as np
+import os
+from rknnlite.api import RKNNLite
+
+# ====================== 配置区 ======================
+MODEL_PATH = "point.rknn"
+OUTPUT_DIR = "./output_rknn"
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# 固定参考点（像素坐标）
+FIXED_REF_POINT = (535, 605)
+
+# mm/px 缩放因子（根据标定数据填写）
+width_mm = 70.0
+width_px = 42
+SCALE_X = width_mm / float(width_px)
+height_mm = 890.0
+height_px = 507
+SCALE_Y = height_mm / float(height_px)
+print(f"Scale factors: SCALE_X={SCALE_X:.3f} mm/px, SCALE_Y={SCALE_Y:.3f} mm/px")
+
+# 输入尺寸
+IMG_SIZE = (640, 640)
+
+
+def letterbox_resize(image, size, bg_color=114):
+ target_w, target_h = size
+ h, w = image.shape[:2]
+ scale = min(target_w / w, target_h / h)
+ new_w, new_h = int(w * scale), int(h * scale)
+ resized = cv2.resize(image, (new_w, new_h))
+ canvas = np.full((target_h, target_w, 3), bg_color, dtype=np.uint8)
+ dx, dy = (target_w - new_w) // 2, (target_h - new_h) // 2
+ canvas[dy:dy + new_h, dx:dx + new_w] = resized
+ return canvas, scale, dx, dy
+
+
+def safe_sigmoid(x):
+ x = np.clip(x, -50, 50)
+ return 1.0 / (1.0 + np.exp(-x))
+
+
+def softmax(x):
+ x = x - np.max(x)
+ e = np.exp(x)
+ return e / e.sum()
+
+
+def dfl_to_xywh(loc, grid_x, grid_y, stride):
+ """将 DFL 输出解析为 xywh"""
+ xywh_ = np.zeros(4)
+ xywh = np.zeros(4)
+
+ # 每个维度 16 bins 做 softmax
+ for i in range(4):
+ l = loc[i * 16:(i + 1) * 16]
+ l = softmax(l)
+ xywh_[i] = sum([j * l[j] for j in range(16)])
+
+ # 对应公式
+ xywh_[0] = (grid_x + 0.5) - xywh_[0]
+ xywh_[1] = (grid_y + 0.5) - xywh_[1]
+ xywh_[2] = (grid_x + 0.5) + xywh_[2]
+ xywh_[3] = (grid_y + 0.5) + xywh_[3]
+
+ # 转成中心点 + 宽高
+ xywh[0] = ((xywh_[0] + xywh_[2]) / 2) * stride
+ xywh[1] = ((xywh_[1] + xywh_[3]) / 2) * stride
+ xywh[2] = (xywh_[2] - xywh_[0]) * stride
+ xywh[3] = (xywh_[3] - xywh_[1]) * stride
+
+ # 转为左上角坐标
+ xywh[0] = xywh[0] - xywh[2] / 2
+ xywh[1] = xywh[1] - xywh[3] / 2
+ return xywh
+
+
+def parse_pose_outputs(outputs, conf_threshold=0.5, dx=0, dy=0, scale=1.0):
+ """
+ 完整解析 RKNN YOLO-Pose 输出
+ 返回 keypoints, class_id, obj_conf, bbox（已映射回原图）
+ """
+ boxes = []
+ obj_confs = []
+ class_ids = []
+
+ # 遍历前三个输出 tensor (det 输出)
+ for idx in range(3):
+ det = np.array(outputs[idx])[0] # (C,H,W)
+ C, H, W = det.shape
+ num_classes = C - 64 # 前64通道为 DFL bbox
+ stride = 640 // H
+
+ for h in range(H):
+ for w in range(W):
+ for c in range(num_classes):
+ conf = safe_sigmoid(det[64 + c, h, w])
+ if conf >= conf_threshold:
+ loc = det[:64, h, w].astype(np.float32)
+ xywh = dfl_to_xywh(loc, w, h, stride)
+ boxes.append(xywh)
+ obj_confs.append(conf)
+ class_ids.append(c)
+
+ if not obj_confs:
+ best_box = np.array([0, 0, 0, 0])
+ class_id = -1
+ obj_conf = 0.0
+ else:
+ max_idx = np.argmax(obj_confs)
+ best_box = boxes[max_idx]
+ class_id = class_ids[max_idx]
+ obj_conf = obj_confs[max_idx]
+
+ # 🔹 bbox 坐标映射回原图
+ x, y, w, h = best_box
+ x = (x - dx) / scale
+ y = (y - dy) / scale
+ w = w / scale
+ h = h / scale
+ best_box = np.array([x, y, w, h])
+
+ # 🔹 关键点解析
+ kpt_output = np.array(outputs[3])[0] # (num_kpts, 3, num_anchor)
+ confs = kpt_output[:, 2, :]
+ best_anchor_idx = np.argmax(np.mean(confs, axis=0))
+ kpt_data = kpt_output[:, :, best_anchor_idx]
+
+ keypoints = []
+ for i in range(kpt_data.shape[0]):
+ x_img, y_img, vis_conf_raw = kpt_data[i]
+ vis_prob = safe_sigmoid(vis_conf_raw)
+ x_orig = (x_img - dx) / scale
+ y_orig = (y_img - dy) / scale
+ keypoints.append([x_orig, y_orig, vis_prob])
+
+ return np.array(keypoints), class_id, obj_conf, best_box
+
+
+def compute_offset(keypoints, fixed_point, scale_x, scale_y):
+ if len(keypoints) < 2:
+ return None
+ p1, p2 = keypoints[0], keypoints[1]
+ cx = (p1[0] + p2[0]) / 2.0
+ cy = (p1[1] + p2[1]) / 2.0
+ dx_mm = (cx - fixed_point[0]) * scale_x
+ dy_mm = (cy - fixed_point[1]) * scale_y
+ return cx, cy, dx_mm, dy_mm
+
+
+def visualize_result(image, keypoints, bbox, fixed_point, offset_info, save_path):
+ vis = image.copy()
+ colors = [(0, 0, 255), (0, 255, 255)]
+ cx, cy, dx_mm, dy_mm = offset_info
+ fx, fy = map(int, fixed_point)
+
+ # 绘制关键点
+ for i, (x, y, conf) in enumerate(keypoints[:2]):
+ if conf > 0.5:
+ cv2.circle(vis, (int(x), int(y)), 6, colors[i], -1)
+ if len(keypoints) >= 2:
+ cv2.line(vis,
+ (int(keypoints[0][0]), int(keypoints[0][1])),
+ (int(keypoints[1][0]), int(keypoints[1][1])),
+ (0, 255, 0), 2)
+
+ # 绘制 bbox
+ x, y, w, h = bbox
+ cv2.rectangle(vis, (int(x), int(y)), (int(x + w), int(y + h)), (255, 0, 0), 2)
+
+ # 绘制中心点
+ cv2.circle(vis, (int(cx), int(cy)), 10, (0, 255, 0), 3)
+ cv2.circle(vis, (fx, fy), 12, (255, 255, 0), 3)
+ cv2.arrowedLine(vis, (fx, fy), (int(cx), int(cy)), (255, 255, 0), 2, tipLength=0.05)
+ cv2.putText(vis, f"DeltaX={dx_mm:+.1f}mm", (fx + 30, fy - 30),
+ cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)
+ cv2.putText(vis, f"DeltaY={dy_mm:+.1f}mm", (fx + 30, fy + 30),
+ cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)
+
+ cv2.imwrite(save_path, vis)
+
+
+def calculate_offset_from_image(image_path, visualize=False):
+ orig = cv2.imread(image_path)
+ if orig is None:
+ return {'success': False, 'message': f'Failed to load image: {image_path}'}
+
+ img_resized, scale, dx, dy = letterbox_resize(orig, IMG_SIZE)
+ infer_img = np.expand_dims(img_resized[..., ::-1], 0).astype(np.uint8)
+
+ rknn = RKNNLite(verbose=False)
+ ret = rknn.load_rknn(MODEL_PATH)
+ if ret != 0:
+ return {'success': False, 'message': 'Failed to load RKNN model'}
+
+ try:
+ rknn.init_runtime(core_mask=RKNNLite.NPU_CORE_0)
+ outputs = rknn.inference([infer_img])
+ finally:
+ rknn.release()
+
+ try:
+ keypoints, class_id, obj_conf, bbox = parse_pose_outputs(outputs, dx=dx, dy=dy, scale=scale)
+ except Exception as e:
+ return {'success': False, 'message': f'Parse error: {str(e)}'}
+
+ offset_info = compute_offset(keypoints, FIXED_REF_POINT, SCALE_X, SCALE_Y)
+ if offset_info is None:
+ return {'success': False, 'message': 'Not enough keypoints'}
+
+ cx, cy, dx_mm, dy_mm = offset_info
+
+ if visualize:
+ vis_save_path = os.path.join(OUTPUT_DIR, f"result_{os.path.basename(image_path)}")
+ visualize_result(orig, keypoints, bbox, FIXED_REF_POINT, offset_info, vis_save_path)
+
+ return {'success': True, 'dx_mm': dx_mm, 'dy_mm': dy_mm,
+ 'cx': cx, 'cy': cy, 'class_id': class_id,
+ 'obj_conf': obj_conf, 'bbox': bbox,
+ 'message': 'Success'}
+
+
+# ====================== 使用示例 ======================
+if __name__ == "__main__":
+ image_path = "11.jpg"
+ result = calculate_offset_from_image(image_path, visualize=True)
+
+ if result['success']:
+ print(f"Center point: ({result['cx']:.1f}, {result['cy']:.1f})")
+ print(f"Offset: DeltaX={result['dx_mm']:+.2f} mm, DeltaY={result['dy_mm']:+.2f} mm")
+ print(f"Class ID: {result['class_id']}, Confidence: {result['obj_conf']:.3f}")
+ print(f"BBox: {result['bbox']}")
+ else:
+ print("Error:", result['message'])