更新到HyperLPR3版本
This commit is contained in:
tunmx
@@ -0,0 +1,37 @@
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import numpy as np
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import MNN
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from loguru import logger
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class MNNAdapter(object):
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def __init__(self, model_path: str, input_shape: tuple,
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dim_type: int = MNN.Tensor_DimensionType_Caffe, outputs_name=None, outputs_shape=None):
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self.interpreter = MNN.Interpreter(model_path)
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self.session = self.interpreter.createSession()
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self.input_tensor = self.interpreter.getSessionInput(self.session)
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self.input_shape = input_shape
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self.dim_type = dim_type
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self.outputs_name = outputs_name
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self.outputs_shape = outputs_shape
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def inference(self, tensor: np.ndarray) -> np.ndarray:
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tensor = tensor.astype(np.float32)
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tmp_input = MNN.Tensor(self.input_shape, MNN.Halide_Type_Float, tensor, self.dim_type)
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self.input_tensor.copyFrom(tmp_input)
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self.interpreter.runSession(self.session)
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output_tensor = list()
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if self.outputs_name:
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if self.outputs_shape:
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for idx, shape in enumerate(self.outputs_shape):
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tmp_output = MNN.Tensor(shape, MNN.Halide_Type_Float, np.ones(shape).astype(np.float32), self.dim_type)
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tmp_tensor = self.interpreter.getSessionOutput(self.session, self.outputs_name[idx])
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tmp_tensor.copyToHostTensor(tmp_output)
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output_tensor.append(np.asarray(tmp_output.getData()))
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else:
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output_tensor = [np.asarray(self.interpreter.getSessionOutput(self.session, name).getData()) for name in self.outputs_name]
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else:
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output_tensor.append(self.interpreter.getSessionOutput(self.session).getData())
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res = np.asarray(output_tensor)
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return res
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@@ -0,0 +1,4 @@
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token = ["blank", "'", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "J",
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"K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z", "云", "京", "冀", "吉", "学", "宁",
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"川", "挂", "新", "晋", "桂", "民", "沪", "津", "浙", "渝", "港", "湘", "琼", "甘", "皖", "粤", "航", "苏", "蒙", "藏", "警", "豫",
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"贵", "赣", "辽", "鄂", "闽", "陕", "青", "鲁", "黑", '领', '使', '澳', ]
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@@ -0,0 +1,300 @@
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import numpy as np
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import cv2
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import time
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from functools import wraps
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def find_the_adjacent_boxes(boxes: list):
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list_index = list()
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for i, a in enumerate(boxes):
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for j, b in enumerate(boxes):
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if i == j:
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continue
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if j in list_index:
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continue
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ax, ay, aw, ah = single_xyxy2cxcywh(a)
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bx, by, bw, bh = single_xyxy2cxcywh(b)
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dis = l2((ax, ay), (bx, by))
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if dis < 2 * aw or dis < 2 * bw:
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list_index.append(i)
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list_index.append(j)
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list_index = set(list_index)
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return list(list_index)
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def l2(p1, p2):
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x0, y0 = p1
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x1, y1 = p2
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return np.sqrt((x0 - x1) ** 2 + (y0 - y1) ** 2)
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def single_xyxy2cxcywh(box):
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x1, y1, x2, y2 = box
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w = x2 - x1
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h = y2 - y1
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x = x1 + w / 2
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y = y1 + h / 2
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return x, y, w, h
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def sigmoid(x):
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return 1 / (1 + np.exp(-x))
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def xywh2xyxy(x):
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# Convert [x, y, w, h] to [x1, y1, x2, y2]
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y = np.copy(x)
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y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
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y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
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y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
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y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
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return y
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def process(input, mask, anchors, size):
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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 = sigmoid(input[..., 4])
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box_confidence = np.expand_dims(box_confidence, axis=-1)
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box_class_probs = sigmoid(input[..., 5:])
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box_xy = sigmoid(input[..., :2]) * 2 - 0.5
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col = np.tile(np.arange(0, grid_w), grid_h).reshape(-1, grid_w)
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row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_w)
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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(size[1] / grid_h), int(size[0] / grid_w))
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box_wh = pow(sigmoid(input[..., 2:4]) * 2, 2)
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box_wh = box_wh * anchors
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box = np.concatenate((box_xy, box_wh), axis=-1)
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return box, box_confidence, box_class_probs
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def filter_boxes(boxes, box_confidences, box_class_probs, box_threshold, nms_threshold):
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"""Filter boxes with box threshold. It's a bit different with origin yolov5 post process!
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# Arguments
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boxes: ndarray, boxes of objects.
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box_confidences: ndarray, confidences of objects.
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box_class_probs: ndarray, class_probs of objects.
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# Returns
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boxes: ndarray, filtered boxes.
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classes: ndarray, classes for boxes.
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scores: ndarray, scores for boxes.
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"""
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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 >= box_threshold)
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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 * box_confidences >= box_threshold)
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boxes = boxes[_class_pos]
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classes = classes[_class_pos]
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scores = (class_max_score * box_confidences)[_class_pos]
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return boxes, classes, scores
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def nms_boxes(boxes, scores, nms_threshold):
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"""Suppress non-maximal boxes.
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# Arguments
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boxes: ndarray, boxes of objects.
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scores: ndarray, scores of objects.
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# Returns
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keep: ndarray, index of effective boxes.
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"""
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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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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 + 0.00001)
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h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)
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inter = w1 * h1
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ovr = inter / (areas[i] + areas[order[1:]] - inter)
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inds = np.where(ovr <= nms_threshold)[0]
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order = order[inds + 1]
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keep = np.array(keep)
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return keep
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def restore_bound_box(boxes: list, ratio: tuple, pad_size: tuple):
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if len(boxes) > 0:
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pad_width, pad_height = pad_size
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boxes_array = np.asarray(boxes)
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# print(boxes_array)
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boxes_array[:, 0] = (boxes_array[:, 0] - pad_width) / ratio[0]
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boxes_array[:, 1] = (boxes_array[:, 1] - pad_height) / ratio[1]
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boxes_array[:, 2] = (boxes_array[:, 2] - pad_width) / ratio[0]
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boxes_array[:, 3] = (boxes_array[:, 3] - pad_height) / ratio[1]
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# print(boxes_array)
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boxes = boxes_array.tolist()
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return boxes
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def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):
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# Resize and pad image while meeting stride-multiple constraints
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shape = im.shape[:2] # current shape [height, width]
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if isinstance(new_shape, int):
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new_shape = (new_shape, new_shape)
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# Scale ratio (new / old)
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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# Compute padding
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ratio = r, r # width, height ratios
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
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dw /= 2 # divide padding into 2 sides
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dh /= 2
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if shape[::-1] != new_unpad: # resize
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im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
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im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
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return im, ratio, (dw, dh)
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def cost(tag=''):
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try:
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'''
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:param tag: 装饰器的参数
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'''
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from loguru import logger
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def wrapper(fn):
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@wraps(fn)
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def wrapper_use_time(*args, **kw):
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'''
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函数传参方式 fn(arg1, arg2, param1=a, param2=b)
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:param args: 位置参数(arg1, arg2, ....) 传参方式 fn(arg1, arg2, ...)
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:param kw: 字典参数{param1=a, param2=b, .....} 传参方式 fn(param1=a, param2=b, ....)
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:return:
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'''
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t1 = time.time()
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try:
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res = fn(*args, **kw)
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except Exception as e:
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logger.error(f"@use_time %s(%s) execute error" % (fn.__name__, tag))
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return None
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else:
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t2 = time.time()
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logger.info(f"{tag}@UseTime: {t2 - t1}")
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return res
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return wrapper_use_time
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return wrapper
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except Exception as err:
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print(err)
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def align_box(imgs, bbox, size=96, scale_factor=1.0, center_bias=0, borderValue=(0, 0, 0)):
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bias_x = (-1 + 2 * np.random.sample()) * center_bias
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bias_y = (-1 + 2 * np.random.sample()) * center_bias
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b_x1, b_y1, b_x2, b_y2 = bbox
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cx, cy = (b_x1 + b_x2) // 2, (b_y1 + b_y2) // 2
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w = b_x2 - b_x1
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h = b_y2 - b_y1
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cx += w * bias_x
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cy += h * bias_y
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base_r = max(w, h)
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j_x = 0
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j_y = 0
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j_r = 0
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base_r += j_r
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r = int(base_r / 2 * scale_factor)
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cy -= int(base_r * 0)
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cx += j_x
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cy += j_y
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x1, y1, x2, y2 = cx - r, cy - r, cx + r, cy + r
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x3, y3 = cx - r, cy + r
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_x1, _y1, _x2, _y2, _x3, _y3 = [0, 0, size, size, 0, size]
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src = np.array([x1, y1, x2, y2, x3, y3], dtype=np.float32).reshape(3, 2)
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sv = np.asarray([[b_x1, b_y1, 1], [b_x2, b_y2, 1]])
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dst = np.array([_x1, _y1, _x2, _y2, _x3, _y3], dtype=np.float32).reshape(3, 2)
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assert src.dtype == np.float32
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assert dst.dtype == np.float32
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assert src.shape == (3, 2)
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assert dst.shape == (3, 2)
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mat = cv2.getAffineTransform(src, dst)
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p = sv.dot(mat.T).reshape(-1)
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if type(imgs) == list:
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imgs = [cv2.warpAffine(img, mat, (size, size), borderValue=borderValue) for img in imgs]
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else:
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imgs = cv2.warpAffine(imgs, mat, (size, size), borderValue=borderValue)
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return imgs, p, mat
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def get_rotate_crop_image(img, points):
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'''
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img_height, img_width = img.shape[0:2]
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left = int(np.min(points[:, 0]))
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right = int(np.max(points[:, 0]))
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top = int(np.min(points[:, 1]))
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bottom = int(np.max(points[:, 1]))
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img_crop = img[top:bottom, left:right, :].copy()
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points[:, 0] = points[:, 0] - left
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points[:, 1] = points[:, 1] - top
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'''
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assert len(points) == 4, "shape of points must be 4*2"
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img_crop_width = int(
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max(
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np.linalg.norm(points[0] - points[1]),
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np.linalg.norm(points[2] - points[3])))
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img_crop_height = int(
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max(
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np.linalg.norm(points[0] - points[3]),
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np.linalg.norm(points[1] - points[2])))
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pts_std = np.float32([[0, 0], [img_crop_width, 0],
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[img_crop_width, img_crop_height],
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[0, img_crop_height]])
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points = points.astype(np.float32)
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# print(points.shape, pts_std.shape)
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M = cv2.getPerspectiveTransform(points, pts_std)
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dst_img = cv2.warpPerspective(
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img,
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M, (img_crop_width, img_crop_height),
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borderMode=cv2.BORDER_REPLICATE,
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flags=cv2.INTER_CUBIC)
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dst_img_height, dst_img_width = dst_img.shape[0:2]
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if dst_img_height * 1.0 / dst_img_width >= 1.5:
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dst_img = np.rot90(dst_img)
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return dst_img
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@@ -0,0 +1,79 @@
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import numpy as np
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PLATE_TYPE_BLUE = 0
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PLATE_TYPE_GREEN = 1
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PLATE_TYPE_YELLOW = 2
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INFER_ONNX_RUNTIME = 0
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INFER_MNN = 1
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DETECT_LEVEL_LOW = 0
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DETECT_LEVEL_HIGH = 1
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MONO = 0 # 单层车牌
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DOUBLE = 1 # 双层车牌
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UNKNOWN = -1 # 未知车牌
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BLUE = 0 # 蓝牌
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YELLOW_SINGLE = 1 # 黄牌单层
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WHILE_SINGLE = 2 # 白牌单层
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GREEN = 3 # 绿牌新能源
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BLACK_HK_MACAO = 4 # 黑牌港澳
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HK_SINGLE = 5 # 香港单层
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HK_DOUBLE = 6 # 香港双层
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MACAO_SINGLE = 7 # 澳门单层
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MACAO_DOUBLE = 8 # 澳门双层
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YELLOW_DOUBLE = 9 # 黄牌双层
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def code_filter(code: str) -> int:
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plate_type = UNKNOWN
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if code[0] == 'W' and code[1] == 'J':
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plate_type = WHILE_SINGLE
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elif len(code) == 8:
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plate_type = GREEN
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elif '学' in code:
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plate_type = BLUE
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elif '港' in code:
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plate_type = BLACK_HK_MACAO
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elif '澳' in code:
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plate_type = BLACK_HK_MACAO
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elif '警' in code:
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plate_type = WHILE_SINGLE
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elif '粤Z' in code:
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plate_type = BLACK_HK_MACAO
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return plate_type
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class Plate(object):
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def __init__(self,
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vertex: np.ndarray,
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plate_code: str,
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rec_confidence: float,
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det_bound_box,
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dex_bound_confidence: float,
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plate_type: int):
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assert vertex.shape == (4, 2)
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self.vertex = vertex
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self.det_bound_box = det_bound_box
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self.plate_code = plate_code
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self.rec_confidence = rec_confidence
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self.dex_bound_confidence = dex_bound_confidence
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self.left_top, self.right_top, self.right_bottom, self.left_bottom = vertex
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self.plate_type = plate_type
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def to_dict(self):
|
||||
return dict(plate_code=self.plate_code, rec_confidence=self.rec_confidence,
|
||||
det_bound_box=self.det_bound_box, plate_type=self.plate_type)
|
||||
|
||||
def to_result(self):
|
||||
return [self.plate_code, self.rec_confidence, self.plate_type, self.det_bound_box.tolist(),]
|
||||
|
||||
def __dict__(self):
|
||||
return self.to_dict()
|
||||
|
||||
def __str__(self):
|
||||
return str(self.to_dict())
|
||||
Reference in New Issue
Block a user