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b/cls_main/cls_rk3568.rknn differ diff --git a/cls_main（框架完成rknn还没训练）/12.png b/cls_main（框架完成rknn还没训练）/12.png deleted file mode 100644 index c1a7b46..0000000 Binary files a/cls_main（框架完成rknn还没训练）/12.png and /dev/null differ diff --git a/cls_main（框架完成rknn还没训练）/cls_inference.py b/cls_main（框架完成rknn还没训练）/cls_inference.py deleted file mode 100644 index 61b79f2..0000000 --- a/cls_main（框架完成rknn还没训练）/cls_inference.py +++ /dev/null @@ -1,91 +0,0 @@ -import cv2 -import numpy as np -import platform -from rknnlite.api import RKNNLite - -# ------------------- 全局变量 ------------------- -_global_rknn_instance = None - -labels = {0: '夹具未夹紧', 1: '夹具夹紧'} - -DEVICE_COMPATIBLE_NODE = '/proc/device-tree/compatible' - -def get_host(): - system = platform.system() - machine = platform.machine() - os_machine = system + '-' + machine - if os_machine == 'Linux-aarch64': - try: - with open(DEVICE_COMPATIBLE_NODE) as f: - device_compatible_str = f.read() - if 'rk3562' in device_compatible_str: - host = 'RK3562' - elif 'rk3576' in device_compatible_str: - host = 'RK3576' - elif 'rk3588' in device_compatible_str: - host = 'RK3588' - else: - host = 'RK3566_RK3568' - except IOError: - print('Read device node {} failed.'.format(DEVICE_COMPATIBLE_NODE)) - exit(-1) - else: - host = os_machine - return host - -# ------------------- 图像预处理 ------------------- -def preprocess(raw_image, target_size=(640, 640)): - img = cv2.resize(raw_image, target_size) - img = np.expand_dims(img, 0) # 添加 batch 维度 - return img - -# ------------------- RKNN 模型初始化 ------------------- -def init_rknn_model(model_path): - global _global_rknn_instance - if _global_rknn_instance is None: - rknn_lite = RKNNLite(verbose=False) - ret = rknn_lite.load_rknn(model_path) - if ret != 0: - print(f'[ERROR] Load model failed (code: {ret})') - exit(ret) - ret = rknn_lite.init_runtime(core_mask=RKNNLite.NPU_CORE_0) - if ret != 0: - print(f'[ERROR] Init runtime failed (code: {ret})') - exit(ret) - _global_rknn_instance = rknn_lite - print(f'[INFO] Model loaded successfully: {model_path}') - return _global_rknn_instance - -# ------------------- 推理 ------------------- -def yolov11_cls_inference(model_path, raw_image, target_size=(640, 640)): - """ - 返回：(class_id, boolean) - 类别 0 -> False - 类别 1 -> True - """ - img = preprocess(raw_image, target_size) - rknn = init_rknn_model(model_path) - outputs = rknn.inference([img]) - - # 获取类别 ID - output = outputs[0].reshape(-1) - class_id = int(np.argmax(output)) - bool_value = True if class_id == 1 else False - - return class_id, bool_value - -# ------------------- 测试 ------------------- -if __name__ == '__main__': - image_path = "12.png" - bgr_image = cv2.imread(image_path) - if bgr_image is None: - print(f"Failed to read image from {image_path}") - exit(-1) - - rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB) - class_id, bool_value = yolov11_cls_inference( - model_path="yolov11_cls.rknn", - raw_image=rgb_image, - target_size=(640, 640) - ) - print(f"类别ID: {class_id}, 布尔值: {bool_value}") diff --git a/cls_main（框架完成rknn还没训练）/readme.md b/cls_main（框架完成rknn还没训练）/readme.md deleted file mode 100644 index 8e4792b..0000000 --- a/cls_main（框架完成rknn还没训练）/readme.md +++ /dev/null @@ -1,48 +0,0 @@ -# yolov11_cls_inference README - -## 概述 -该模块用于对米厂输入图像执行二分类推理，用于判断机械臂夹爪是否夹紧。 - -类别定义： - -0 → 夹具未夹紧 (False) -1 → 夹具夹紧 (True) - -rknn模型只加载一次，复用全局实例，提高推理效率。 - -## 调用示例 - -您可以直接调用 yolov11_cls_inference 函数，以便集成到其他项目中： - -示例 1: 测试仅获取单张图片的类别和布尔值 - -```bash -from yolov11_cls_inference import yolov11_cls_inference -import cv2 - -# 读取图像 -bgr_image = cv2.imread("11.jpg") -rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB) - -# 调用推理函数 -class_id, bool_value = yolov11_cls_inference( - model_path="yolov11_cls.rknn", - raw_image=rgb_image, - target_size=(640, 640) -) - -print(f"类别ID: {class_id}, 布尔值: {bool_value}") -``` - -示例 2: 直接在其他项目中集成使用 -```bash - -from yolov11_cls_inference import yolov11_cls_inference - -# raw_image 已经读取或处理好的图像 -class_id, bool_value = yolov11_cls_inference(model_path="yolov11_cls.rknn", raw_image=raw_image) -if bool_value: - print("夹具夹紧") -else: - print("夹具未夹紧") -``` diff --git a/cls_main（框架完成rknn还没训练）/yolov11_cls.rknn b/cls_main（框架完成rknn还没训练）/yolov11_cls.rknn deleted file mode 100644 index eca9557..0000000 Binary files a/cls_main（框架完成rknn还没训练）/yolov11_cls.rknn and /dev/null differ diff --git a/obb_main/README.md b/obb_main/README.md deleted file mode 100644 index 24dd210..0000000 --- a/obb_main/README.md +++ /dev/null @@ -1,94 +0,0 @@ -# RKNN 关键点推理与偏移量计算工具 - -该工具通过使用RKNN模型对输入图像进行关键点检测，并根据检测结果计算相对于固定参考点的偏移量（单位：毫米）。此外，还提供了可视化选项来展示计算结果。 - -## 目录结构 - -├── calculate_offset.py # 主程序脚本 -├── point.rknn # RKNN 模型文件 (请确保正确路径) -└── README.md # 说明文档 - -## 配置 - -在 `calculate_offset.py` 文件顶部的配置区中，您可以修改如下参数以适应您的需求： - -- **MODEL_PATH**: RKNN 模型文件路径。 -- **OUTPUT_DIR**: 输出目录路径。 -- **FIXED_REF_POINT**: 固定参考点坐标（像素）。 -- **SCALE_X**, **SCALE_Y**: 缩放因子，用于将像素坐标转换为毫米。 -- **IMG_SIZE**: 输入图像尺寸。 - -## 安装依赖 - -请确保安装了必要的 Python 库。可以通过 pip 安装： - -```bash -pip install opencv-python numpy rknnlite -``` - -## 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) -``` - -## 函数调用2.0 - -示例 1: 仅获取偏移量（不画图） - -```bash -from calculate_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 calculate_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） - 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 - -##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/obb_main/caculate_diff(可用设备树版本，测试，不用做推理).py b/obb_main/caculate_diff(可用设备树版本，测试，不用做推理).py deleted file mode 100644 index 7e37a3c..0000000 --- a/obb_main/caculate_diff(可用设备树版本，测试，不用做推理).py +++ /dev/null @@ -1,256 +0,0 @@ -# detect_fixed.py -import cv2 -import numpy as np -import os -import platform -from rknnlite.api import RKNNLite - -# ====================== 配置区 ====================== - -IMAGE_PATH = "11.jpg" # 测试图片 -MODEL_PATH = "point.rknn" -OUTPUT_DIR = "./output_rknn" -os.makedirs(OUTPUT_DIR, exist_ok=True) - -# 固定参考点 & 缩放因子 -FIXED_REF_POINT = (535, 605) -width_mm, width_px = 70.0, 42 -height_mm, height_px = 890.0, 507 -SCALE_X = width_mm / float(width_px) -SCALE_Y = height_mm / float(height_px) -print(f"[INFO] Scale factors: X={SCALE_X:.3f} mm/px, Y={SCALE_Y:.3f} mm/px") - -IMG_SIZE = (640, 640) - -# 设备树路径（用于自动识别平台） -DEVICE_COMPATIBLE_NODE = '/proc/device-tree/compatible' - - -def get_host(): - system = platform.system() - machine = platform.machine() - if system == 'Linux' and machine == 'aarch64': - try: - with open(DEVICE_COMPATIBLE_NODE, 'r') as f: - compatible = f.read() - if 'rk3588' in compatible: - return 'RK3588' - elif 'rk3576' in compatible: - return 'RK3576' - elif 'rk3562' in compatible: - return 'RK3562' - else: - return 'RK3566_RK3568' - except Exception as e: - print(f"Read device tree failed: {e}") - exit(-1) - else: - return f"{system}-{machine}" - - -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): - xywh_ = np.zeros(4) - xywh = np.zeros(4) - 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)) - - 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] - - 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 - - xywh[0] -= xywh[2] / 2 - xywh[1] -= xywh[3] / 2 - return xywh - - -def parse_pose_outputs(outputs, conf_threshold=0.5, dx=0, dy=0, scale=1.0): - boxes = [] - obj_confs = [] - class_ids = [] - - for idx in range(3): # det head - det = np.array(outputs[idx])[0] - C, H, W = det.shape - num_classes = C - 64 - 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] - - x, y, w, h = best_box - x = (x - dx) / scale - y = (y - dy) / scale - w /= scale - h /= scale - best_box = [x, y, w, h] - - kpt_output = np.array(outputs[3])[0] - 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_raw = kpt_data[i] - vis_prob = safe_sigmoid(vis_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 - cy = (p1[1] + p2[1]) / 2 - 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() - fx, fy = map(int, fixed_point) - cx, cy, dx_mm, dy_mm = offset_info - - for i, (x, y, conf) in enumerate(keypoints[:2]): - if conf > 0.5: - color = (0, 0, 255) if i == 0 else (0, 255, 255) - cv2.circle(vis, (int(x), int(y)), 6, color, -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) - - x, y, w, h = bbox - cv2.rectangle(vis, (int(x), int(y)), (int(x + w), int(y + h)), (255, 0, 0), 2) - 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 main(): - host_name = get_host() - print(f"[INFO] Running on: {host_name}") - - rknn = RKNNLite() - - # ---- Load Model ---- - ret = rknn.load_rknn(MODEL_PATH) - if ret != 0: - print("❌ Failed to load RKNN model!") - exit(ret) - print("✅ Model loaded successfully.") - - # ---- Init Runtime ---- - if host_name in ['RK3576', 'RK3588']: - ret = rknn.init_runtime(core_mask=RKNNLite.NPU_CORE_0) - else: - ret = rknn.init_runtime() - if ret != 0: - print("❌ Init runtime failed!") - rknn.release() - exit(ret) - print("✅ Runtime initialized.") - - # ---- Preprocess ---- - ori_img = cv2.imread(IMAGE_PATH) - if ori_img is None: - print(f"❌ Cannot read image: {IMAGE_PATH}") - return - - img_resized, scale, dx, dy = letterbox_resize(ori_img, IMG_SIZE) - input_tensor = np.expand_dims(img_resized[..., ::-1], 0).astype(np.uint8) # RGB - - # ---- Inference ---- - print("🔍 Starting inference...") - outputs = rknn.inference(inputs=[input_tensor]) - print("✅ Inference completed.") - - # ---- Post-process ---- - try: - keypoints, cls_id, obj_conf, bbox = parse_pose_outputs( - outputs, dx=dx, dy=dy, scale=scale) - - offset_info = compute_offset(keypoints, FIXED_REF_POINT, SCALE_X, SCALE_Y) - if offset_info is None: - print("⚠️ Not enough keypoints detected.") - return - - cx, cy, dx_mm, dy_mm = offset_info - vis_save_path = os.path.join(OUTPUT_DIR, f"result_{os.path.basename(IMAGE_PATH)}") - visualize_result(ori_img, keypoints, bbox, FIXED_REF_POINT, offset_info, vis_save_path) - - print(f"\n🎯 Detection Result:") - print(f"Center: ({cx:.1f}, {cy:.1f})") - print(f"Offset: ΔX={dx_mm:+.2f}mm, ΔY={dy_mm:+.2f}mm") - print(f"Class: {cls_id}, Confidence: {obj_conf:.3f}") - print(f"Saved result to: {vis_save_path}") - - except Exception as e: - print(f"❌ Post-processing error: {e}") - import traceback - traceback.print_exc() - - finally: - rknn.release() - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/obb_main/calculate_diff.py b/obb_main/calculate_diff.py deleted file mode 100644 index 98ff4bd..0000000 --- a/obb_main/calculate_diff.py +++ /dev/null @@ -1,230 +0,0 @@ -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): - """保持比例缩放并填充到指定大小""" - 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 parse_pose_outputs(outputs, dx=0, dy=0, scale=1.0): - """ - 解析 RKNN YOLO-Pose 关键点输出 - outputs[3]: shape (1, 4, 3, 8400) -> [kpt_id, (x,y,conf), anchor] - """ - kpt_output = np.array(outputs[3])[0] # (4, 3, 8400) - confs = kpt_output[:, 2, :] # 取每个关键点的 visible_conf - mean_conf_per_anchor = np.mean(confs, axis=0) # 每个 anchor 的平均可见性 - best_anchor_idx = np.argmax(mean_conf_per_anchor) - kpt_data = kpt_output[:, :, best_anchor_idx] # (4, 3): x, y, vis_conf - - keypoints = [] - for i in range(4): - x_img = kpt_data[i, 0] - y_img = kpt_data[i, 1] - vis_conf_raw = kpt_data[i, 2] - vis_prob = safe_sigmoid(vis_conf_raw) - - # 映射回原图坐标 - x_orig = (x_img - dx) / scale - y_orig = (y_img - dy) / scale - keypoints.append([x_orig, y_orig, vis_prob]) - - return np.array(keypoints) - - -def compute_offset(keypoints, fixed_point, scale_x, scale_y): - """ - 计算中心点相对于固定参考点的偏移量（mm） - 中心点 = P0 和 P1 的中点 - 返回: (center_x, center_y, dx_mm, dy_mm) - """ - 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_px = cx - fixed_point[0] - dy_px = cy - fixed_point[1] - dx_mm = dx_px * scale_x - dy_mm = dy_px * scale_y - - return cx, cy, dx_mm, dy_mm - - -def visualize_result(image, keypoints, fixed_point, offset_info, save_path): - """ - 可视化关键点、参考点、中心点、偏移箭头和文字 - """ - vis = image.copy() - colors = [(0, 0, 255), (255, 0, 0), (0, 255, 0), (255, 255, 0)] - 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): - if conf > 0.5: - cv2.circle(vis, (int(x), int(y)), 8, colors[i], -1) - cv2.putText(vis, f"P{i}", (int(x) + 10, int(y) - 10), - cv2.FONT_HERSHEY_SIMPLEX, 0.8, colors[i], 2) - - # 绘制中心点 - cv2.circle(vis, (int(cx), int(cy)), 12, (0, 255, 0), 3) - cv2.putText(vis, "Center", (int(cx) + 20, int(cy)), - cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 3) - - # 绘制参考点 - cv2.circle(vis, (fx, fy), 15, (255, 255, 0), 3) - cv2.putText(vis, "Ref", (fx + 20, fy), - cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 3) - - # 绘制偏移箭头和文字 - cv2.arrowedLine(vis, (fx, fy), (int(cx), int(cy)), (0, 255, 255), 3, tipLength=0.05) - cv2.putText(vis, f"DeltaX={dx_mm:+.1f}mm", (fx + 40, fy - 40), - cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 255), 3) - cv2.putText(vis, f"DeltaY={dy_mm:+.1f}mm", (fx + 40, fy + 40), - cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 255), 3) - - cv2.imwrite(save_path, vis) - - -def calculate_offset_from_image(image_path, visualize=False): - """ - 主函数：输入图片路径，输出偏移量 (dx_mm, dy_mm) - - 参数: - image_path (str): 输入图像路径 - visualize (bool): 是否保存可视化结果 - - 返回: - dict: { - 'success': bool, - 'dx_mm': float or None, - 'dy_mm': float or None, - 'cx': float or None, # 中心点 x - 'cy': float or None, # 中心点 y - 'message': str - } - """ - # 读取图像 - orig = cv2.imread(image_path) - if orig is None: - return { - 'success': False, - 'dx_mm': None, 'dy_mm': None, 'cx': None, 'cy': None, - 'message': f'Failed to load image: {image_path}' - } - - h0, w0 = orig.shape[:2] - - # 预处理 - img_resized, scale, dx, dy = letterbox_resize(orig, IMG_SIZE) - infer_img = np.expand_dims(img_resized[..., ::-1], 0).astype(np.uint8) - - # 加载模型并推理 - rknn = RKNNLite(verbose=False) - ret = rknn.load_rknn(MODEL_PATH) - if ret != 0: - return { - 'success': False, - 'dx_mm': None, 'dy_mm': None, 'cx': None, 'cy': None, - 'message': 'Failed to load RKNN model' - } - - try: - rknn.init_runtime(core_mask=RKNNLite.NPU_CORE_0) - outputs = rknn.inference([infer_img]) - except Exception as e: - rknn.release() - return { - 'success': False, - 'dx_mm': None, 'dy_mm': None, 'cx': None, 'cy': None, - 'message': f'Inference error: {str(e)}' - } - finally: - rknn.release() - - # 解析关键点 - try: - keypoints = parse_pose_outputs(outputs, dx=dx, dy=dy, scale=scale) - except Exception as e: - return { - 'success': False, - 'dx_mm': None, 'dy_mm': None, 'cx': None, 'cy': None, - 'message': f'Parse keypoint error: {str(e)}' - } - - # 计算偏移 - offset_info = compute_offset(keypoints, FIXED_REF_POINT, SCALE_X, SCALE_Y) - if offset_info is None: - return { - 'success': False, - 'dx_mm': None, 'dy_mm': None, 'cx': None, 'cy': None, - 'message': 'Not enough keypoints to compute offset' - } - - cx, cy, dx_mm, dy_mm = offset_info - - # 可视化（可选） - if visualize: - vis_save_path = os.path.join(OUTPUT_DIR, f"result_{os.path.basename(image_path)}") - visualize_result(orig, keypoints, FIXED_REF_POINT, offset_info, vis_save_path) - - return { - 'success': True, - 'dx_mm': dx_mm, - 'dy_mm': dy_mm, - 'cx': cx, - 'cy': cy, - '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") - else: - print("Error:", result['message']) \ No newline at end of file diff --git a/obb_main/calculate_diff2.0.py b/obb_main/calculate_diff2.0.py deleted file mode 100644 index cb45c7a..0000000 --- a/obb_main/calculate_diff2.0.py +++ /dev/null @@ -1,236 +0,0 @@ -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']) diff --git a/obb_main/point.rknn b/obb_main/point.rknn deleted file mode 100644 index 64d89cc..0000000 Binary files a/obb_main/point.rknn and /dev/null differ