HDMI输入直至Yolov5识别全流程代码

NpuYoloV5/04_yolov5/camera_demo.py

@@ -0,0 +1,41 @@
+import cv2
+import time
+import numpy as np
+
+def main():
+    cap = cv2.VideoCapture('/dev/video10')
+    frames, loopTime, initTime = 0, time.time(), time.time()
+    fps = 0
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            print("Failed to read frame")
+            break
+
+        # 检查帧数据是否需要重塑
+        if frame.size == 1280 * 720 * 3:  # 检查是否为扁平化数据
+            frame = frame.reshape((720, 1280, 3)).astype(np.uint8)
+        else:
+            print(f"Unexpected frame shape: {frame.shape}")
+
+        # 显示帧
+        frames += 1
+        if frames % 30 == 0:
+            fps = 30 / (time.time() - loopTime)
+            print(f"30帧平均帧率: {fps:.2f} 帧")
+            loopTime = time.time()
+
+        cv2.putText(frame, f"FPS: {fps:.2f}", (10, 30), 
+                   cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+        cv2.imshow("MIPI Camera", frame)
+
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+
+    print(f"总平均帧率: {frames / (time.time() - initTime):.2f}")
+    cap.release()
+    cv2.destroyAllWindows()
+if __name__ == "__main__":
+    main()
+

NpuYoloV5/04_yolov5/dataset.txt

@@ -0,0 +1 @@
+bus.jpg

NpuYoloV5/04_yolov5/test.py

@@ -0,0 +1,319 @@
+import os
+import urllib
+import traceback
+import time
+import sys
+import numpy as np
+import cv2
+from rknn.api import RKNN
+
+ONNX_MODEL = 'yolov5s.onnx'
+RKNN_MODEL = 'yolov5s.rknn'
+IMG_PATH = './bus.jpg'
+DATASET = './dataset.txt'
+
+QUANTIZE_ON = True
+
+OBJ_THRESH = 0.25
+NMS_THRESH = 0.45
+IMG_SIZE = 640
+
+CLASSES = ("person", "bicycle", "car", "motorbike ", "aeroplane ", "bus ", "train", "truck ", "boat", "traffic light",
+           "fire hydrant", "stop sign ", "parking meter", "bench", "bird", "cat", "dog ", "horse ", "sheep", "cow", "elephant",
+           "bear", "zebra ", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
+           "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife ",
+           "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza ", "donut", "cake", "chair", "sofa",
+           "pottedplant", "bed", "diningtable", "toilet ", "tvmonitor", "laptop	", "mouse	", "remote ", "keyboard ", "cell phone", "microwave ",
+           "oven ", "toaster", "sink", "refrigerator ", "book", "clock", "vase", "scissors ", "teddy bear ", "hair drier", "toothbrush ")
+
+
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
+
+
+def xywh2xyxy(x):
+    # Convert [x, y, w, h] to [x1, y1, x2, y2]
+    y = np.copy(x)
+    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
+    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
+    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
+    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
+    return y
+
+
+def process(input, mask, anchors):
+
+    anchors = [anchors[i] for i in mask]
+    grid_h, grid_w = map(int, input.shape[0:2])
+
+    box_confidence = sigmoid(input[..., 4])
+    box_confidence = np.expand_dims(box_confidence, axis=-1)
+
+    box_class_probs = sigmoid(input[..., 5:])
+
+    box_xy = sigmoid(input[..., :2])*2 - 0.5
+
+    col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
+    row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
+    col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
+    row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
+    grid = np.concatenate((col, row), axis=-1)
+    box_xy += grid
+    box_xy *= int(IMG_SIZE/grid_h)
+
+    box_wh = pow(sigmoid(input[..., 2:4])*2, 2)
+    box_wh = box_wh * anchors
+
+    box = np.concatenate((box_xy, box_wh), axis=-1)
+
+    return box, box_confidence, box_class_probs
+
+
+def filter_boxes(boxes, box_confidences, box_class_probs):
+    """Filter boxes with box threshold. It's a bit different with origin yolov5 post process!
+
+    # Arguments
+        boxes: ndarray, boxes of objects.
+        box_confidences: ndarray, confidences of objects.
+        box_class_probs: ndarray, class_probs of objects.
+
+    # Returns
+        boxes: ndarray, filtered boxes.
+        classes: ndarray, classes for boxes.
+        scores: ndarray, scores for boxes.
+    """
+    boxes = boxes.reshape(-1, 4)
+    box_confidences = box_confidences.reshape(-1)
+    box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])
+
+    _box_pos = np.where(box_confidences >= OBJ_THRESH)
+    boxes = boxes[_box_pos]
+    box_confidences = box_confidences[_box_pos]
+    box_class_probs = box_class_probs[_box_pos]
+
+    class_max_score = np.max(box_class_probs, axis=-1)
+    classes = np.argmax(box_class_probs, axis=-1)
+    _class_pos = np.where(class_max_score >= OBJ_THRESH)
+
+    boxes = boxes[_class_pos]
+    classes = classes[_class_pos]
+    scores = (class_max_score* box_confidences)[_class_pos]
+
+    return boxes, classes, scores
+
+
+def nms_boxes(boxes, scores):
+    """Suppress non-maximal boxes.
+
+    # Arguments
+        boxes: ndarray, boxes of objects.
+        scores: ndarray, scores of objects.
+
+    # Returns
+        keep: ndarray, index of effective boxes.
+    """
+    x = boxes[:, 0]
+    y = boxes[:, 1]
+    w = boxes[:, 2] - boxes[:, 0]
+    h = boxes[:, 3] - boxes[:, 1]
+
+    areas = w * h
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+
+        xx1 = np.maximum(x[i], x[order[1:]])
+        yy1 = np.maximum(y[i], y[order[1:]])
+        xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
+        yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
+
+        w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
+        h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
+        inter = w1 * h1
+
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+        inds = np.where(ovr <= NMS_THRESH)[0]
+        order = order[inds + 1]
+    keep = np.array(keep)
+    return keep
+
+
+def yolov5_post_process(input_data):
+    masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+    anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
+               [59, 119], [116, 90], [156, 198], [373, 326]]
+
+    boxes, classes, scores = [], [], []
+    for input, mask in zip(input_data, masks):
+        b, c, s = process(input, mask, anchors)
+        b, c, s = filter_boxes(b, c, s)
+        boxes.append(b)
+        classes.append(c)
+        scores.append(s)
+
+    boxes = np.concatenate(boxes)
+    boxes = xywh2xyxy(boxes)
+    classes = np.concatenate(classes)
+    scores = np.concatenate(scores)
+
+    nboxes, nclasses, nscores = [], [], []
+    for c in set(classes):
+        inds = np.where(classes == c)
+        b = boxes[inds]
+        c = classes[inds]
+        s = scores[inds]
+
+        keep = nms_boxes(b, s)
+
+        nboxes.append(b[keep])
+        nclasses.append(c[keep])
+        nscores.append(s[keep])
+
+    if not nclasses and not nscores:
+        return None, None, None
+
+    boxes = np.concatenate(nboxes)
+    classes = np.concatenate(nclasses)
+    scores = np.concatenate(nscores)
+
+    return boxes, classes, scores
+
+
+def draw(image, boxes, scores, classes):
+    """Draw the boxes on the image.
+
+    # Argument:
+        image: original image.
+        boxes: ndarray, boxes of objects.
+        classes: ndarray, classes of objects.
+        scores: ndarray, scores of objects.
+        all_classes: all classes name.
+    """
+    for box, score, cl in zip(boxes, scores, classes):
+        top, left, right, bottom = box
+        print('class: {}, score: {}'.format(CLASSES[cl], score))
+        print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))
+        top = int(top)
+        left = int(left)
+        right = int(right)
+        bottom = int(bottom)
+
+        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
+        cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
+                    (top, left - 6),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.6, (0, 0, 255), 2)
+
+
+def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):
+    # Resize and pad image while meeting stride-multiple constraints
+    shape = im.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # Scale ratio (new / old)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+
+    # Compute padding
+    ratio = r, r  # width, height ratios
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+
+    dw /= 2  # divide padding into 2 sides
+    dh /= 2
+
+    if shape[::-1] != new_unpad:  # resize
+        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+    return im, ratio, (dw, dh)
+
+
+if __name__ == '__main__':
+
+    # Create RKNN object
+    rknn = RKNN(verbose=True)
+
+    # pre-process config
+    print('--> Config model')
+    rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform="rk3588")
+    print('done')
+
+    # Load ONNX model
+    print('--> Loading model')
+    ret = rknn.load_onnx(model=ONNX_MODEL)
+    if ret != 0:
+        print('Load model failed!')
+        exit(ret)
+    print('done')
+
+    # Build model
+    print('--> Building model')
+    ret = rknn.build(do_quantization=QUANTIZE_ON, dataset=DATASET)
+    if ret != 0:
+        print('Build model failed!')
+        exit(ret)
+    print('done')
+
+    # Export RKNN model
+    print('--> Export rknn model')
+    ret = rknn.export_rknn(RKNN_MODEL)
+    if ret != 0:
+        print('Export rknn model failed!')
+        exit(ret)
+    print('done')
+
+    # Init runtime environment
+    print('--> Init runtime environment')
+    ret = rknn.init_runtime(target="rk3588")
+    # ret = rknn.init_runtime('rk3566')
+    if ret != 0:
+        print('Init runtime environment failed!')
+        exit(ret)
+    print('done')
+
+    # Set inputs
+    img = cv2.imread(IMG_PATH)
+    # img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
+
+    # Inference
+    print('--> Running model')
+    outputs = rknn.inference(inputs=[img])
+    np.save('./onnx_yolov5_0.npy', outputs[0])
+    np.save('./onnx_yolov5_1.npy', outputs[1])
+    np.save('./onnx_yolov5_2.npy', outputs[2])
+    print('done')
+
+    # post process
+    input0_data = outputs[0]
+    input1_data = outputs[1]
+    input2_data = outputs[2]
+
+    input0_data = input0_data.reshape([3, -1]+list(input0_data.shape[-2:]))
+    input1_data = input1_data.reshape([3, -1]+list(input1_data.shape[-2:]))
+    input2_data = input2_data.reshape([3, -1]+list(input2_data.shape[-2:]))
+
+    input_data = list()
+    input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
+    input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
+    input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))
+
+    boxes, classes, scores = yolov5_post_process(input_data)
+
+    img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    if boxes is not None:
+        draw(img_1, boxes, scores, classes)
+        cv2.imwrite('result.jpg', img_1)
+        print('Save results to result.jpg!')
+    # show output
+    # cv2.imshow("post process result", img_1)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+    rknn.release()

NpuYoloV5/04_yolov5/test_rknn.py

@@ -0,0 +1,292 @@
+import os
+import urllib
+import traceback
+import time
+import sys
+import numpy as np
+import cv2
+from rknn.api import RKNN
+
+ONNX_MODEL = 'yolov5s.onnx'
+RKNN_MODEL = 'yolov5s.rknn'
+IMG_PATH = './bus.jpg'
+DATASET = './dataset.txt'
+
+QUANTIZE_ON = True
+
+OBJ_THRESH = 0.25
+NMS_THRESH = 0.45
+IMG_SIZE = 640
+
+CLASSES = ("person", "bicycle", "car", "motorbike ", "aeroplane ", "bus ", "train", "truck ", "boat", "traffic light",
+           "fire hydrant", "stop sign ", "parking meter", "bench", "bird", "cat", "dog ", "horse ", "sheep", "cow", "elephant",
+           "bear", "zebra ", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
+           "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife ",
+           "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza ", "donut", "cake", "chair", "sofa",
+           "pottedplant", "bed", "diningtable", "toilet ", "tvmonitor", "laptop	", "mouse	", "remote ", "keyboard ", "cell phone", "microwave ",
+           "oven ", "toaster", "sink", "refrigerator ", "book", "clock", "vase", "scissors ", "teddy bear ", "hair drier", "toothbrush ")
+
+
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
+
+
+def xywh2xyxy(x):
+    # Convert [x, y, w, h] to [x1, y1, x2, y2]
+    y = np.copy(x)
+    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
+    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
+    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
+    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
+    return y
+
+
+def process(input, mask, anchors):
+
+    anchors = [anchors[i] for i in mask]
+    grid_h, grid_w = map(int, input.shape[0:2])
+
+    box_confidence = sigmoid(input[..., 4])
+    box_confidence = np.expand_dims(box_confidence, axis=-1)
+
+    box_class_probs = sigmoid(input[..., 5:])
+
+    box_xy = sigmoid(input[..., :2])*2 - 0.5
+
+    col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
+    row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
+    col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
+    row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
+    grid = np.concatenate((col, row), axis=-1)
+    box_xy += grid
+    box_xy *= int(IMG_SIZE/grid_h)
+
+    box_wh = pow(sigmoid(input[..., 2:4])*2, 2)
+    box_wh = box_wh * anchors
+
+    box = np.concatenate((box_xy, box_wh), axis=-1)
+
+    return box, box_confidence, box_class_probs
+
+
+def filter_boxes(boxes, box_confidences, box_class_probs):
+    """Filter boxes with box threshold. It's a bit different with origin yolov5 post process!
+
+    # Arguments
+        boxes: ndarray, boxes of objects.
+        box_confidences: ndarray, confidences of objects.
+        box_class_probs: ndarray, class_probs of objects.
+
+    # Returns
+        boxes: ndarray, filtered boxes.
+        classes: ndarray, classes for boxes.
+        scores: ndarray, scores for boxes.
+    """
+    boxes = boxes.reshape(-1, 4)
+    box_confidences = box_confidences.reshape(-1)
+    box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])
+
+    _box_pos = np.where(box_confidences >= OBJ_THRESH)
+    boxes = boxes[_box_pos]
+    box_confidences = box_confidences[_box_pos]
+    box_class_probs = box_class_probs[_box_pos]
+
+    class_max_score = np.max(box_class_probs, axis=-1)
+    classes = np.argmax(box_class_probs, axis=-1)
+    _class_pos = np.where(class_max_score >= OBJ_THRESH)
+
+    boxes = boxes[_class_pos]
+    classes = classes[_class_pos]
+    scores = (class_max_score* box_confidences)[_class_pos]
+
+    return boxes, classes, scores
+
+
+def nms_boxes(boxes, scores):
+    """Suppress non-maximal boxes.
+
+    # Arguments
+        boxes: ndarray, boxes of objects.
+        scores: ndarray, scores of objects.
+
+    # Returns
+        keep: ndarray, index of effective boxes.
+    """
+    x = boxes[:, 0]
+    y = boxes[:, 1]
+    w = boxes[:, 2] - boxes[:, 0]
+    h = boxes[:, 3] - boxes[:, 1]
+
+    areas = w * h
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+
+        xx1 = np.maximum(x[i], x[order[1:]])
+        yy1 = np.maximum(y[i], y[order[1:]])
+        xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
+        yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
+
+        w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
+        h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
+        inter = w1 * h1
+
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+        inds = np.where(ovr <= NMS_THRESH)[0]
+        order = order[inds + 1]
+    keep = np.array(keep)
+    return keep
+
+
+def yolov5_post_process(input_data):
+    masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+    anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
+               [59, 119], [116, 90], [156, 198], [373, 326]]
+
+    boxes, classes, scores = [], [], []
+    for input, mask in zip(input_data, masks):
+        b, c, s = process(input, mask, anchors)
+        b, c, s = filter_boxes(b, c, s)
+        boxes.append(b)
+        classes.append(c)
+        scores.append(s)
+
+    boxes = np.concatenate(boxes)
+    boxes = xywh2xyxy(boxes)
+    classes = np.concatenate(classes)
+    scores = np.concatenate(scores)
+
+    nboxes, nclasses, nscores = [], [], []
+    for c in set(classes):
+        inds = np.where(classes == c)
+        b = boxes[inds]
+        c = classes[inds]
+        s = scores[inds]
+
+        keep = nms_boxes(b, s)
+
+        nboxes.append(b[keep])
+        nclasses.append(c[keep])
+        nscores.append(s[keep])
+
+    if not nclasses and not nscores:
+        return None, None, None
+
+    boxes = np.concatenate(nboxes)
+    classes = np.concatenate(nclasses)
+    scores = np.concatenate(nscores)
+
+    return boxes, classes, scores
+
+
+def draw(image, boxes, scores, classes):
+    """Draw the boxes on the image.
+
+    # Argument:
+        image: original image.
+        boxes: ndarray, boxes of objects.
+        classes: ndarray, classes of objects.
+        scores: ndarray, scores of objects.
+        all_classes: all classes name.
+    """
+    for box, score, cl in zip(boxes, scores, classes):
+        top, left, right, bottom = box
+        print('class: {}, score: {}'.format(CLASSES[cl], score))
+        print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))
+        top = int(top)
+        left = int(left)
+        right = int(right)
+        bottom = int(bottom)
+
+        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
+        cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
+                    (top, left - 6),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.6, (0, 0, 255), 2)
+
+
+def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):
+    # Resize and pad image while meeting stride-multiple constraints
+    shape = im.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # Scale ratio (new / old)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+
+    # Compute padding
+    ratio = r, r  # width, height ratios
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+
+    dw /= 2  # divide padding into 2 sides
+    dh /= 2
+
+    if shape[::-1] != new_unpad:  # resize
+        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+    return im, ratio, (dw, dh)
+
+
+if __name__ == '__main__':
+
+    # Create RKNN object
+    rknn = RKNN(verbose=True)
+
+    rknn.load_rknn(path="./yolov5s.rknn")
+
+    # Init runtime environment
+    print('--> Init runtime environment')
+    ret = rknn.init_runtime(target="rk3588")
+    # ret = rknn.init_runtime('rk3566')
+    if ret != 0:
+        print('Init runtime environment failed!')
+        exit(ret)
+    print('done')
+
+    # Set inputs
+    img = cv2.imread(IMG_PATH)
+    # img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
+
+    # Inference
+    print('--> Running model')
+    outputs = rknn.inference(inputs=[img])
+    np.save('./onnx_yolov5_0.npy', outputs[0])
+    np.save('./onnx_yolov5_1.npy', outputs[1])
+    np.save('./onnx_yolov5_2.npy', outputs[2])
+    print('done')
+
+    # post process
+    input0_data = outputs[0]
+    input1_data = outputs[1]
+    input2_data = outputs[2]
+
+    input0_data = input0_data.reshape([3, -1]+list(input0_data.shape[-2:]))
+    input1_data = input1_data.reshape([3, -1]+list(input1_data.shape[-2:]))
+    input2_data = input2_data.reshape([3, -1]+list(input2_data.shape[-2:]))
+
+    input_data = list()
+    input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
+    input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
+    input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))
+
+    boxes, classes, scores = yolov5_post_process(input_data)
+
+    img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    if boxes is not None:
+        draw(img_1, boxes, scores, classes)
+        cv2.imwrite('result.jpg', img_1)
+        print('Save results to result.jpg!')
+    # show output
+    # cv2.imshow("post process result", img_1)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+    rknn.release()

NpuYoloV5/04_yolov5/test_rknn_lite.py

@@ -0,0 +1,292 @@
+import os
+import urllib
+import traceback
+import time
+import sys
+import numpy as np
+import cv2
+from rknnlite.api import RKNNLite
+
+ONNX_MODEL = 'yolov5s.onnx'
+RKNN_MODEL = 'yolov5s.rknn'
+IMG_PATH = './bus.jpg'
+DATASET = './dataset.txt'
+
+QUANTIZE_ON = True
+
+OBJ_THRESH = 0.25
+NMS_THRESH = 0.45
+IMG_SIZE = 640
+
+CLASSES = ("person", "bicycle", "car", "motorbike ", "aeroplane ", "bus ", "train", "truck ", "boat", "traffic light",
+           "fire hydrant", "stop sign ", "parking meter", "bench", "bird", "cat", "dog ", "horse ", "sheep", "cow", "elephant",
+           "bear", "zebra ", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
+           "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife ",
+           "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza ", "donut", "cake", "chair", "sofa",
+           "pottedplant", "bed", "diningtable", "toilet ", "tvmonitor", "laptop	", "mouse	", "remote ", "keyboard ", "cell phone", "microwave ",
+           "oven ", "toaster", "sink", "refrigerator ", "book", "clock", "vase", "scissors ", "teddy bear ", "hair drier", "toothbrush ")
+
+
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
+
+
+def xywh2xyxy(x):
+    # Convert [x, y, w, h] to [x1, y1, x2, y2]
+    y = np.copy(x)
+    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
+    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
+    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
+    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
+    return y
+
+
+def process(input, mask, anchors):
+
+    anchors = [anchors[i] for i in mask]
+    grid_h, grid_w = map(int, input.shape[0:2])
+
+    box_confidence = sigmoid(input[..., 4])
+    box_confidence = np.expand_dims(box_confidence, axis=-1)
+
+    box_class_probs = sigmoid(input[..., 5:])
+
+    box_xy = sigmoid(input[..., :2])*2 - 0.5
+
+    col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
+    row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
+    col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
+    row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
+    grid = np.concatenate((col, row), axis=-1)
+    box_xy += grid
+    box_xy *= int(IMG_SIZE/grid_h)
+
+    box_wh = pow(sigmoid(input[..., 2:4])*2, 2)
+    box_wh = box_wh * anchors
+
+    box = np.concatenate((box_xy, box_wh), axis=-1)
+
+    return box, box_confidence, box_class_probs
+
+
+def filter_boxes(boxes, box_confidences, box_class_probs):
+    """Filter boxes with box threshold. It's a bit different with origin yolov5 post process!
+
+    # Arguments
+        boxes: ndarray, boxes of objects.
+        box_confidences: ndarray, confidences of objects.
+        box_class_probs: ndarray, class_probs of objects.
+
+    # Returns
+        boxes: ndarray, filtered boxes.
+        classes: ndarray, classes for boxes.
+        scores: ndarray, scores for boxes.
+    """
+    boxes = boxes.reshape(-1, 4)
+    box_confidences = box_confidences.reshape(-1)
+    box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])
+
+    _box_pos = np.where(box_confidences >= OBJ_THRESH)
+    boxes = boxes[_box_pos]
+    box_confidences = box_confidences[_box_pos]
+    box_class_probs = box_class_probs[_box_pos]
+
+    class_max_score = np.max(box_class_probs, axis=-1)
+    classes = np.argmax(box_class_probs, axis=-1)
+    _class_pos = np.where(class_max_score >= OBJ_THRESH)
+
+    boxes = boxes[_class_pos]
+    classes = classes[_class_pos]
+    scores = (class_max_score* box_confidences)[_class_pos]
+
+    return boxes, classes, scores
+
+
+def nms_boxes(boxes, scores):
+    """Suppress non-maximal boxes.
+
+    # Arguments
+        boxes: ndarray, boxes of objects.
+        scores: ndarray, scores of objects.
+
+    # Returns
+        keep: ndarray, index of effective boxes.
+    """
+    x = boxes[:, 0]
+    y = boxes[:, 1]
+    w = boxes[:, 2] - boxes[:, 0]
+    h = boxes[:, 3] - boxes[:, 1]
+
+    areas = w * h
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+
+        xx1 = np.maximum(x[i], x[order[1:]])
+        yy1 = np.maximum(y[i], y[order[1:]])
+        xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
+        yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
+
+        w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
+        h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
+        inter = w1 * h1
+
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+        inds = np.where(ovr <= NMS_THRESH)[0]
+        order = order[inds + 1]
+    keep = np.array(keep)
+    return keep
+
+
+def yolov5_post_process(input_data):
+    masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+    anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
+               [59, 119], [116, 90], [156, 198], [373, 326]]
+
+    boxes, classes, scores = [], [], []
+    for input, mask in zip(input_data, masks):
+        b, c, s = process(input, mask, anchors)
+        b, c, s = filter_boxes(b, c, s)
+        boxes.append(b)
+        classes.append(c)
+        scores.append(s)
+
+    boxes = np.concatenate(boxes)
+    boxes = xywh2xyxy(boxes)
+    classes = np.concatenate(classes)
+    scores = np.concatenate(scores)
+
+    nboxes, nclasses, nscores = [], [], []
+    for c in set(classes):
+        inds = np.where(classes == c)
+        b = boxes[inds]
+        c = classes[inds]
+        s = scores[inds]
+
+        keep = nms_boxes(b, s)
+
+        nboxes.append(b[keep])
+        nclasses.append(c[keep])
+        nscores.append(s[keep])
+
+    if not nclasses and not nscores:
+        return None, None, None
+
+    boxes = np.concatenate(nboxes)
+    classes = np.concatenate(nclasses)
+    scores = np.concatenate(nscores)
+
+    return boxes, classes, scores
+
+
+def draw(image, boxes, scores, classes):
+    """Draw the boxes on the image.
+
+    # Argument:
+        image: original image.
+        boxes: ndarray, boxes of objects.
+        classes: ndarray, classes of objects.
+        scores: ndarray, scores of objects.
+        all_classes: all classes name.
+    """
+    for box, score, cl in zip(boxes, scores, classes):
+        top, left, right, bottom = box
+        print('class: {}, score: {}'.format(CLASSES[cl], score))
+        print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))
+        top = int(top)
+        left = int(left)
+        right = int(right)
+        bottom = int(bottom)
+
+        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
+        cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
+                    (top, left - 6),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.6, (0, 0, 255), 2)
+
+
+def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):
+    # Resize and pad image while meeting stride-multiple constraints
+    shape = im.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # Scale ratio (new / old)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+
+    # Compute padding
+    ratio = r, r  # width, height ratios
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+
+    dw /= 2  # divide padding into 2 sides
+    dh /= 2
+
+    if shape[::-1] != new_unpad:  # resize
+        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+    return im, ratio, (dw, dh)
+
+
+if __name__ == '__main__':
+
+    # Create RKNN object
+    rknn = RKNNLite(verbose=True)
+
+    rknn.load_rknn(path="./yolov5s.rknn")
+
+    # Init runtime environment
+    print('--> Init runtime environment')
+    ret = rknn.init_runtime()
+    # ret = rknn.init_runtime('rk3566')
+    if ret != 0:
+        print('Init runtime environment failed!')
+        exit(ret)
+    print('done')
+
+    # Set inputs
+    img = cv2.imread(IMG_PATH)
+    # img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
+
+    # Inference
+    print('--> Running model')
+    outputs = rknn.inference(inputs=[img])
+    np.save('./onnx_yolov5_0.npy', outputs[0])
+    np.save('./onnx_yolov5_1.npy', outputs[1])
+    np.save('./onnx_yolov5_2.npy', outputs[2])
+    print('done')
+
+    # post process
+    input0_data = outputs[0]
+    input1_data = outputs[1]
+    input2_data = outputs[2]
+
+    input0_data = input0_data.reshape([3, -1]+list(input0_data.shape[-2:]))
+    input1_data = input1_data.reshape([3, -1]+list(input1_data.shape[-2:]))
+    input2_data = input2_data.reshape([3, -1]+list(input2_data.shape[-2:]))
+
+    input_data = list()
+    input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
+    input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
+    input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))
+
+    boxes, classes, scores = yolov5_post_process(input_data)
+
+    img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    if boxes is not None:
+        draw(img_1, boxes, scores, classes)
+        cv2.imwrite('result.jpg', img_1)
+        print('Save results to result.jpg!')
+    # show output
+    # cv2.imshow("post process result", img_1)
+    # cv2.waitKey(0)
+    # cv2.destroyAllWindows()
+
+    rknn.release()

NpuYoloV5/04_yolov5/test_rknn_lite_hdmi_in.py

@@ -0,0 +1,312 @@
+import os
+import urllib
+import traceback
+import time
+import sys
+import numpy as np
+import cv2
+from rknnlite.api import RKNNLite
+
+ONNX_MODEL = 'yolov5s.onnx'
+RKNN_MODEL = 'yolov5s.rknn'
+IMG_PATH = './bus.jpg'
+DATASET = './dataset.txt'
+
+QUANTIZE_ON = True
+
+OBJ_THRESH = 0.25
+NMS_THRESH = 0.45
+IMG_SIZE = 640
+
+CLASSES = ("person", "bicycle", "car", "motorbike ", "aeroplane ", "bus ", "train", "truck ", "boat", "traffic light",
+           "fire hydrant", "stop sign ", "parking meter", "bench", "bird", "cat", "dog ", "horse ", "sheep", "cow", "elephant",
+           "bear", "zebra ", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite",
+           "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife ",
+           "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza ", "donut", "cake", "chair", "sofa",
+           "pottedplant", "bed", "diningtable", "toilet ", "tvmonitor", "laptop	", "mouse	", "remote ", "keyboard ", "cell phone", "microwave ",
+           "oven ", "toaster", "sink", "refrigerator ", "book", "clock", "vase", "scissors ", "teddy bear ", "hair drier", "toothbrush ")
+
+
+def sigmoid(x):
+    return 1 / (1 + np.exp(-x))
+
+
+def xywh2xyxy(x):
+    # Convert [x, y, w, h] to [x1, y1, x2, y2]
+    y = np.copy(x)
+    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
+    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
+    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
+    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
+    return y
+
+
+def process(input, mask, anchors):
+
+    anchors = [anchors[i] for i in mask]
+    grid_h, grid_w = map(int, input.shape[0:2])
+
+    box_confidence = sigmoid(input[..., 4])
+    box_confidence = np.expand_dims(box_confidence, axis=-1)
+
+    box_class_probs = sigmoid(input[..., 5:])
+
+    box_xy = sigmoid(input[..., :2])*2 - 0.5
+
+    col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)
+    row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)
+    col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
+    row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)
+    grid = np.concatenate((col, row), axis=-1)
+    box_xy += grid
+    box_xy *= int(IMG_SIZE/grid_h)
+
+    box_wh = pow(sigmoid(input[..., 2:4])*2, 2)
+    box_wh = box_wh * anchors
+
+    box = np.concatenate((box_xy, box_wh), axis=-1)
+
+    return box, box_confidence, box_class_probs
+
+
+def filter_boxes(boxes, box_confidences, box_class_probs):
+    """Filter boxes with box threshold. It's a bit different with origin yolov5 post process!
+
+    # Arguments
+        boxes: ndarray, boxes of objects.
+        box_confidences: ndarray, confidences of objects.
+        box_class_probs: ndarray, class_probs of objects.
+
+    # Returns
+        boxes: ndarray, filtered boxes.
+        classes: ndarray, classes for boxes.
+        scores: ndarray, scores for boxes.
+    """
+    boxes = boxes.reshape(-1, 4)
+    box_confidences = box_confidences.reshape(-1)
+    box_class_probs = box_class_probs.reshape(-1, box_class_probs.shape[-1])
+
+    _box_pos = np.where(box_confidences >= OBJ_THRESH)
+    boxes = boxes[_box_pos]
+    box_confidences = box_confidences[_box_pos]
+    box_class_probs = box_class_probs[_box_pos]
+
+    class_max_score = np.max(box_class_probs, axis=-1)
+    classes = np.argmax(box_class_probs, axis=-1)
+    _class_pos = np.where(class_max_score >= OBJ_THRESH)
+
+    boxes = boxes[_class_pos]
+    classes = classes[_class_pos]
+    scores = (class_max_score* box_confidences)[_class_pos]
+
+    return boxes, classes, scores
+
+
+def nms_boxes(boxes, scores):
+    """Suppress non-maximal boxes.
+
+    # Arguments
+        boxes: ndarray, boxes of objects.
+        scores: ndarray, scores of objects.
+
+    # Returns
+        keep: ndarray, index of effective boxes.
+    """
+    x = boxes[:, 0]
+    y = boxes[:, 1]
+    w = boxes[:, 2] - boxes[:, 0]
+    h = boxes[:, 3] - boxes[:, 1]
+
+    areas = w * h
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+
+        xx1 = np.maximum(x[i], x[order[1:]])
+        yy1 = np.maximum(y[i], y[order[1:]])
+        xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])
+        yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])
+
+        w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)
+        h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
+        inter = w1 * h1
+
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+        inds = np.where(ovr <= NMS_THRESH)[0]
+        order = order[inds + 1]
+    keep = np.array(keep)
+    return keep
+
+
+def yolov5_post_process(input_data):
+    masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+    anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
+               [59, 119], [116, 90], [156, 198], [373, 326]]
+
+    boxes, classes, scores = [], [], []
+    for input, mask in zip(input_data, masks):
+        b, c, s = process(input, mask, anchors)
+        b, c, s = filter_boxes(b, c, s)
+        boxes.append(b)
+        classes.append(c)
+        scores.append(s)
+
+    boxes = np.concatenate(boxes)
+    boxes = xywh2xyxy(boxes)
+    classes = np.concatenate(classes)
+    scores = np.concatenate(scores)
+
+    nboxes, nclasses, nscores = [], [], []
+    for c in set(classes):
+        inds = np.where(classes == c)
+        b = boxes[inds]
+        c = classes[inds]
+        s = scores[inds]
+
+        keep = nms_boxes(b, s)
+
+        nboxes.append(b[keep])
+        nclasses.append(c[keep])
+        nscores.append(s[keep])
+
+    if not nclasses and not nscores:
+        return None, None, None
+
+    boxes = np.concatenate(nboxes)
+    classes = np.concatenate(nclasses)
+    scores = np.concatenate(nscores)
+
+    return boxes, classes, scores
+
+
+def draw(image, boxes, scores, classes):
+    """Draw the boxes on the image.
+
+    # Argument:
+        image: original image.
+        boxes: ndarray, boxes of objects.
+        classes: ndarray, classes of objects.
+        scores: ndarray, scores of objects.
+        all_classes: all classes name.
+    """
+    for box, score, cl in zip(boxes, scores, classes):
+        top, left, right, bottom = box
+        # print('class: {}, score: {}'.format(CLASSES[cl], score))
+        # print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))
+        top = int(top)
+        left = int(left)
+        right = int(right)
+        bottom = int(bottom)
+
+        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)
+        cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),
+                    (top, left - 6),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.6, (0, 0, 255), 2)
+
+
+def letterbox(im, new_shape=(IMG_SIZE, IMG_SIZE), color=(0, 0, 0)):
+    # Resize and pad image while meeting stride-multiple constraints
+    shape = im.shape[:2]  # current shape [height, width]
+    if isinstance(new_shape, int):
+        new_shape = (new_shape, new_shape)
+
+    # Scale ratio (new / old)
+    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+
+    # Compute padding
+    ratio = r, r  # width, height ratios
+    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
+
+    dw /= 2  # divide padding into 2 sides
+    dh /= 2
+
+    if shape[::-1] != new_unpad:  # resize
+        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
+    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
+    return im, ratio, (dw, dh)
+
+
+if __name__ == '__main__':
+
+    # Create RKNN object
+    rknn = RKNNLite(verbose=False)
+
+    rknn.load_rknn(path="./yolov5s.rknn")
+
+    # Init runtime environment
+    print('--> Init runtime environment')
+    ret = rknn.init_runtime(core_mask=7)
+    # ret = rknn.init_runtime('rk3566')
+    if ret != 0:
+        print('Init runtime environment failed!')
+        exit(ret)
+    print('done')
+
+    # # Set inputs
+    # img = cv2.imread(IMG_PATH)
+    # # img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))
+    # img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    # img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
+    cap = cv2.VideoCapture('test.mp4')  
+    frames, loopTime, initTime = 0, time.time(), time.time()
+    fps = 0
+    while True:
+        frames += 1
+        # 从摄像头捕获帧
+        ret, img = cap.read()
+        # 重塑视频帧，只有自己虚拟的才需要
+        # img = img.reshape((720, 1280, 3)).astype(np.uint8)
+        # 如果捕获到帧，则显示它
+        if ret:
+            # Inference
+            # print('--> Running model')
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+            img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
+            outputs = rknn.inference(inputs=[img])
+            # np.save('./onnx_yolov5_0.npy', outputs[0])
+            # np.save('./onnx_yolov5_1.npy', outputs[1])
+            # np.save('./onnx_yolov5_2.npy', outputs[2])
+            # print('done')
+
+            # post process
+            input0_data = outputs[0]
+            input1_data = outputs[1]
+            input2_data = outputs[2]
+
+            input0_data = input0_data.reshape([3, -1]+list(input0_data.shape[-2:]))
+            input1_data = input1_data.reshape([3, -1]+list(input1_data.shape[-2:]))
+            input2_data = input2_data.reshape([3, -1]+list(input2_data.shape[-2:]))
+
+            input_data = list()
+            input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))
+            input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))
+            input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))
+
+            boxes, classes, scores = yolov5_post_process(input_data)
+
+            img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+            if boxes is not None:
+                draw(img_1, boxes, scores, classes)
+            if frames % 30 == 0:
+                print("30帧平均帧率:\t", 30 / (time.time() - loopTime), "帧")
+                fps = 30 / (time.time() - loopTime)
+                loopTime = time.time()
+            cv2.putText(img_1, "FPS: {:.2f}".format(fps), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255),
+                        2)  # 在图像上显示帧率
+            cv2.imshow("MIPI Camera", img_1)
+        # 按下'q'键退出循环
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+    # show output
+    # cv2.imshow("post process result", img_1)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+    rknn.release()