主要算法：目标检测网络RetinaNet
本方案需要完成前置模型转换工作采取的方案为：pt—onnx—tflite（tflite为了完成部署到移动端）
完成转换后将模型部署至aidlux平台，完成实时视频检测。部分代码如下：
代码语言：javascript复制def process_img(img, target_size=640, max_size=2000, multiple=32, keep_ratio=True, NCHW=True, ToTensor=True):
img = img128:512, 0:480
img = cv2.resize(img, (640, 512), interpolation=cv2.INTER_LINEAR)
im_shape = img.shape
im_size_min = np.min(im_shape0:2)
im_size_max = np.max(im_shape0:2)resize with keep_ratio
代码语言：javascript复制if keep_ratio:
im_scale = float(target_size) / float(im_size_min)
if np.round(im_scale * im_size_max) > max_size:
im_scale = float(max_size) / float(im_size_max)
im_scale_x = np.floor(img.shape1 * im_scale / multiple) * multiple / img.shape1
im_scale_y = np.floor(img.shape0 * im_scale / multiple) * multiple / img.shape0
image_resized = cv2.resize(img, None, None, fx=im_scale_x, fy=im_scale_y, interpolation=cv2.INTER_LINEAR)
im_scales = np.array(im_scale_x, im_scale_y, im_scale_x, im_scale_y)
im = image_resized / 255.0 # np.float64
im = im.astype(np.float32)
PIXEL_MEANS =(0.485, 0.456, 0.406) # RGB format mean and variances
PIXEL_STDS = (0.229, 0.224, 0.225)
im -= np.array(PIXEL_MEANS)
im /= np.array(PIXEL_STDS)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) # BGR2RGB
if NCHW:
im = np.transpose(im, (2, 0, 1)).astype(np.float32) # SAI-KEY TensorFlow use input with NHWC.
im = imnp.newaxis, ...
if ToTensor:
im = torch.from_numpy(im)
return im, im_scales
else:
return None 此代码用于对输入的图片进行预处理，使其能够被神经网络处理。具体来说，输入的图片首先被裁剪和缩放到指定大小，然后通过减去 RGB 归一化值的均值和标准差进行归一化，接着将 RGB 通道转为 BGR 通道，最后改变图像的输入格式，将其转为 NCHW 格式的张量（N 代表 Batch size，C 代表通道数，H 代表高度，W 代表宽度）并转为 PyTorch 的 Tensor 类型。如果 keep_ratio=True，则图像的宽高比被保持不变。返回处理后的图像以及缩放比例。如果 keep_ratio=False，则返回 None。
代码语言：javascript复制if __name__=="__main__":
tflite_model = '/home/R-RetinaNet/models/r-retinanet.tflite'
# 定义输入输出shape
in_shape = [1 * 640 * 800 * 3 * 4] # HWC, float32
out_shape = [1 * 53325 * 8 * 4] # 8400: total cells, 52 = 48(num_classes) 4(xywh), float32
# AidLite初始化
aidlite = aidlite_gpu.aidlite()
# 加载R-RetinaNet模型
res = aidlite.ANNModel(tflite_model, in_shape, out_shape, 4, -1) # Infer on -1: cpu, 0: gpu, 1: mixed, 2: dsp
# print(res)
'''
读取手机实时摄像头数据
'''
cap = cvs.VideoCapture(0)
frame_id = 0
while True:
frame = cap.read()
if frame is None:
continue
im, im_scales = process_img(frame, NCHW=False, ToTensor=False) # im: NHWC
frame_id = 1
if frame_id % 3 != 0:
continue
aidlite.setInput_Float32(im, 800, 640)
# 推理
aidlite.invoke()
preds = aidlite.getOutput_Float32(0)
preds = preds.reshape(1, 8, (int)(preds.shape[0]/8))
# 后解算
output = np.transpose(preds, (0, 2, 1))
# 创建Anchor
im_anchor = np.transpose(im, (0, 3, 1, 2)).astype(np.float32)
anchors_list = []
anchor_generator = Anchors(ratios = np.array([0.2, 0.5, 1, 2, 5]))
original_anchors = anchor_generator(im_anchor) # (bs, num_all_achors, 5)
anchors_list.append(original_anchors)
# 解算输出
decode_output = decoder(im_anchor, anchors_list[-1], output[..., 5:8], output[..., 0:5], thresh=0.2, nms_thresh=0.2, test_conf=None)
# 重构输出
scores = decode_output[0].reshape(-1, 1)
classes = decode_output[1].reshape(-1, 1)
boxes = decode_output[2]
boxes[:, :4] = boxes[:, :4] / im_scales
if boxes.shape[1] > 5:
boxes[:, 5:9] = boxes[:, 5:9] / im_scales
dets = np.concatenate([classes, scores, boxes], axis=1)
# 过滤类别
keep = np.where(classes > 0)[0]
dets = dets[keep, :]
# 转换坐标('xywha'->'xyxyxyxy')
res = sort_corners(rbox_2_quad(dets[:, 2:]))
# cv绘图.
for k in range(dets.shape[0]):
cv2.line(frame, (int(res[k, 0]), int(res[k, 1])), (int(res[k, 2]), int(res[k, 3])), (0, 255, 0), 3)
cv2.line(frame, (int(res[k, 2]), int(res[k, 3])), (int(res[k, 4]), int(res[k, 5])), (0, 255, 0), 3)
cv2.line(frame, (int(res[k, 4]), int(res[k, 5])), (int(res[k, 6]), int(res[k, 7])), (0, 255, 0), 3)
cv2.line(frame, (int(res[k, 6]), int(res[k, 7])), (int(res[k, 0]), int(res[k, 1])), (0, 255, 0), 3)
cvs.imshow(frame)此代码实现了通过手机摄像头实时检测图像中的文本行，首先初始化 AidLite，并加载 R-RetinaNet 模型。然后进入摄像头读取和处理的循环中，先调用 process_img 对图像进行预处理，然后将预处理后的图像输入给模型进行推理，再根据模型输出进行解析、过滤和转换坐标，最后在原图上绘制文本行框并显示出来。其中使用了 opencv 绘制框和显示图片。
