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琉璃月光
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rknn-multi-threaded-nosigmoid/func.py
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246
rknn-multi-threaded-nosigmoid/func.py
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# 下面代码基于你给出的 yolov5 示例做最小修改的适配版
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import cv2
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import numpy as np
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OBJ_THRESH, NMS_THRESH, IMG_SIZE = 0.25, 0.45, 640
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CLASSES = ("person", "bicycle", "car", "motorbike ", "aeroplane ", "bus ", "train", "truck ", "boat", "traffic light",
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"fire hydrant", "stop sign ", "parking meter", "bench", "bird", "cat", "dog ", "horse ", "sheep", "cow", "elephant",
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"bear", "zebra ", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
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"baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife ",
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"spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza ", "donut", "cake", "chair", "sofa",
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"pottedplant", "bed", "diningtable", "toilet ", "tvmonitor", "laptop\t", "mouse\t", "remote ", "keyboard ", "cell phone", "microwave ",
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"oven ", "toaster", "sink", "refrigerator ", "book", "clock", "vase", "scissors ", "teddy bear ", "hair drier", "toothbrush ")
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# ---------- 保留你原来的辅助函数（process/filter/nms/xywh2xyxy） ----------
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def xywh2xyxy(x):
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y = np.copy(x)
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y[:, 0] = x[:, 0] - x[:, 2] / 2
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y[:, 1] = x[:, 1] - x[:, 3] / 2
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y[:, 2] = x[:, 0] + x[:, 2] / 2
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y[:, 3] = x[:, 1] + x[:, 3] / 2
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return y
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def process(input, mask, anchors):
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# input: (grid_h, grid_w, 3, attrs) attrs >=5+num_classes
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# anchors: list of (w,h) pairs for the 3 anchors
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anchors = [anchors[i] for i in mask]
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grid_h, grid_w = map(int, input.shape[0:2])
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box_confidence = input[..., 4]
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box_confidence = np.expand_dims(box_confidence, axis=-1)
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box_class_probs = input[..., 5:]
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# YOLO11 style decode used originally in your code:
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box_xy = input[..., :2] * 2 - 0.5
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# build grid
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col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
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row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
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col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
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row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
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grid = np.concatenate((col, row), axis=-1)
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box_xy += grid
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box_xy *= int(IMG_SIZE / grid_h)
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box_wh = pow(input[..., 2:4] * 2, 2)
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# multiply by provided anchors (we will use unit anchors if we want to neutralize)
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box_wh = box_wh * anchors
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return np.concatenate((box_xy, box_wh), axis=-1), box_confidence, box_class_probs
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def filter_boxes(boxes, box_confidences, box_class_probs):
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boxes = boxes.reshape(-1, 4)
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box_confidences = box_confidences.reshape(-1)
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box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])
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_box_pos = np.where(box_confidences >= OBJ_THRESH)
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boxes = boxes[_box_pos]
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box_confidences = box_confidences[_box_pos]
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box_class_probs = box_class_probs[_box_pos]
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class_max_score = np.max(box_class_probs, axis=-1)
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classes = np.argmax(box_class_probs, axis=-1)
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_class_pos = np.where(class_max_score >= OBJ_THRESH)
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return boxes[_class_pos], classes[_class_pos], (class_max_score * box_confidences)[_class_pos]
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def nms_boxes(boxes, scores):
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x = boxes[:, 0]
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y = boxes[:, 1]
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w = boxes[:, 2] - boxes[:, 0]
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h = boxes[:, 3] - boxes[:, 1]
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areas = w * h
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order = scores.argsort()[::-1]
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keep = []
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eps = 1e-7
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while order.size > 0:
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i = order[0]
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keep.append(i)
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xx1 = np.maximum(x[i], x[order[1:]])
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yy1 = np.maximum(y[i], y[order[1:]])
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xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
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yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
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w1 = np.maximum(0.0, xx2 - xx1 + 1e-5)
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h1 = np.maximum(0.0, yy2 - yy1 + 1e-5)
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inter = w1 * h1
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denom = (areas[i] + areas[order[1:]] - inter)
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denom = np.maximum(denom, eps)
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ovr = inter / denom
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inds = np.where(ovr <= NMS_THRESH)[0]
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order = order[inds + 1]
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return np.array(keep)
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# ---------- 关键：把你的 9 输出拼成原来 yolov5_post_process 需要的 input_data 格式 ----------
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def yolov11_to_yolov5_style_input(outputs):
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"""
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outputs: list of 9 tensors (1, C, H, W) in this order per your print:
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[reg80, cls80, obj80, reg40, cls40, obj40, reg20, cls20, obj20]
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We will convert each scale to a (H, W, 3, 5+num_classes) array and repeat
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the same per-anchor slice so that your existing yolov5_post_process can be reused.
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To avoid anchor scaling changing box_wh, we set anchors to 1x1 in later call.
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"""
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input_data = []
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# scales: (indices and corresponding H,W from tensors)
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for i in range(0, 9, 3):
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reg = outputs[i][0] # (64, H, W)
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cls = outputs[i+1][0] # (80, H, W)
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obj = outputs[i+2][0] # (1, H, W)
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# find H,W from reg
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H = reg.shape[1]
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W = reg.shape[2]
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# xywh: assume first 4 channels of reg are x,y,w,h per cell
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xywh = reg[0:4, :, :] # (4, H, W)
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xywh = np.transpose(xywh, (1, 2, 0)) # (H, W, 4)
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# obj and cls to H,W,?
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obj_hw = np.transpose(obj[0, :, :], (0, 1)) # (H, W)
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cls_hw = np.transpose(cls, (1, 2, 0)) # (H, W, 80)
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# build one anchor slice: [x,y,w,h,obj, cls80] -> shape (H, W, 5+80)
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slice_hw = np.concatenate([xywh, obj_hw[..., None], cls_hw], axis=-1) # (H, W, 85)
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# repeat to make 3 anchors per cell (so shape becomes H,W,3,85)
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slice_3 = np.repeat(slice_hw[:, :, None, :], 3, axis=2) # (H, W, 3, 85)
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input_data.append(slice_3)
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return input_data
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def yolov5_post_process_adapted(input_data):
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"""
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复用你原来的 yolov5_post_process，但使用 unit anchors so that
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box_wh 不会被不正确放缩。
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"""
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masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]] # unused in anchors here, but kept for compatibility
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# use neutral anchors (1,1) to avoid scaling change
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anchors = [[1, 1], [1, 1], [1, 1], [1, 1], [1, 1],
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[1, 1], [1, 1], [1, 1], [1, 1]]
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boxes, classes, scores = [], [], []
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# input_data already is list of 3 arrays shaped (H,W,3,85)
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for input in input_data:
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# process() expects shape (grid_h, grid_w, 3, attrs)
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b, c, s = process(input, [0,1,2], anchors) # mask and anchors values used inside process
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b, c, s = filter_boxes(b, c, s)
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boxes.append(b)
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classes.append(c)
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scores.append(s)
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if len(boxes) == 0:
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return None, None, None
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boxes = np.concatenate(boxes)
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boxes = xywh2xyxy(boxes)
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classes = np.concatenate(classes)
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scores = np.concatenate(scores)
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# nms per class
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nboxes, nclasses, nscores = [], [], []
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for cls_id in set(classes):
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inds = np.where(classes == cls_id)
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b = boxes[inds]
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c = classes[inds]
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s = scores[inds]
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keep = nms_boxes(b, s)
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nboxes.append(b[keep])
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nclasses.append(c[keep])
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nscores.append(s[keep])
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if not nclasses and not nscores:
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return None, None, None
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return np.concatenate(nboxes), np.concatenate(nclasses), np.concatenate(nscores)
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# ---------- draw 保持原样 ----------
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def draw(image, boxes, scores, classes):
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for box, score, cl in zip(boxes, scores, classes):
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top, left, right, bottom = box
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top = int(top)
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left = int(left)
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cv2.rectangle(image, (top, left), (int(right), int(bottom)), (255, 0, 0), 2)
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cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
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(top, left - 6),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.6, (0, 0, 255), 2)
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# ---------- 最终 myFunc（替换你原来的 myFunc） ----------
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def myFunc(rknn_lite, IMG):
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# 1. BGR -> RGB
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IMG = cv2.cvtColor(IMG, cv2.COLOR_BGR2RGB)
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# 2. Resize to model input size (640x640)
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IMG = cv2.resize(IMG, (IMG_SIZE, IMG_SIZE)) # (640, 640, 3)
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# 3. HWC -> CHW
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IMG = np.transpose(IMG, (2, 0, 1)) # (3, 640, 640)
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# 4. Add batch dimension: (1, 3, 640, 640)
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IMG_in = np.expand_dims(IMG, axis=0).astype(np.uint8)
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# 5. Inference
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outputs = rknn_lite.inference(inputs=[IMG_in])
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if outputs is None:
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print("⚠️ Inference failed, skipping frame.")
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return cv2.cvtColor(IMG_in.squeeze().transpose(1, 2, 0), cv2.COLOR_RGB2BGR)
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# 6. Convert 9-output -> yolov5 style input_data
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input_data = yolov11_to_yolov5_style_input(outputs)
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# 7. Run adapted yolov5_post_process
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boxes, classes, scores = yolov5_post_process_adapted(input_data)
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# 8. 如果你只想保留 car（COCO id=2），在这里再过滤一次
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if boxes is not None:
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keep_car = np.where(classes == 2)[0]
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if keep_car.size == 0:
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boxes, classes, scores = None, None, None
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else:
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boxes = boxes[keep_car]
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classes = classes[keep_car]
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scores = scores[keep_car]
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# Convert back to BGR for OpenCV display
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IMG_vis = cv2.cvtColor(IMG_in.squeeze().transpose(1, 2, 0), cv2.COLOR_RGB2BGR)
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if boxes is not None:
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draw(IMG_vis, boxes, scores, classes)
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return IMG_vis
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