191 lines
7.6 KiB
Python
191 lines
7.6 KiB
Python
# coding: utf-8
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# 2019-12-10
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"""
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YOlo相关的预处理api；
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"""
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import cv2
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import time
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import numpy as np
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# 加载label names；
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def get_labels(names_file):
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names = list()
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with open(names_file, 'r') as f:
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lines = f.read()
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for name in lines.splitlines():
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names.append(name)
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f.close()
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return names
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# 照片预处理
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def process_img(img_path, input_shape):
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ori_img = cv2.imread(img_path)
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img = cv2.resize(ori_img, input_shape)
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image = img[:, :, ::-1].transpose((2, 0, 1))
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image = image[np.newaxis, :, :, :] / 255
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image = np.array(image, dtype=np.float32)
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return ori_img, ori_img.shape, image
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# 视频预处理
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def frame_process(frame, input_shape):
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image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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image = cv2.resize(image, input_shape)
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# image = cv2.resize(image, (640, 480))
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image_mean = np.array([127, 127, 127])
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image = (image - image_mean) / 128
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image = np.transpose(image, [2, 0, 1])
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image = np.expand_dims(image, axis=0)
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image = image.astype(np.float32)
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return image
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# sigmoid函数
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def sigmoid(x):
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s = 1 / (1 + np.exp(-1 * x))
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return s
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# 获取预测正确的类别，以及概率和索引;
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def get_result(class_scores):
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class_score = 0
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class_index = 0
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for i in range(len(class_scores)):
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if class_scores[i] > class_score:
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class_index += 1
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class_score = class_scores[i]
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return class_score, class_index
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# 通过置信度筛选得到bboxs
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def get_bbox(feat, anchors, image_shape, confidence_threshold=0.25):
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box = list()
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for i in range(len(anchors)):
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for cx in range(feat.shape[0]):
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for cy in range(feat.shape[1]):
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tx = feat[cx][cy][0 + 85 * i]
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ty = feat[cx][cy][1 + 85 * i]
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tw = feat[cx][cy][2 + 85 * i]
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th = feat[cx][cy][3 + 85 * i]
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cf = feat[cx][cy][4 + 85 * i]
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cp = feat[cx][cy][5 + 85 * i:85 + 85 * i]
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bx = (sigmoid(tx) + cx) / feat.shape[0]
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by = (sigmoid(ty) + cy) / feat.shape[1]
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bw = anchors[i][0] * np.exp(tw) / image_shape[0]
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bh = anchors[i][1] * np.exp(th) / image_shape[1]
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b_confidence = sigmoid(cf)
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b_class_prob = sigmoid(cp)
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b_scores = b_confidence * b_class_prob
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b_class_score, b_class_index = get_result(b_scores)
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if b_class_score >= confidence_threshold:
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box.append([bx, by, bw, bh, b_class_score, b_class_index])
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return box
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# 采用nms算法筛选获取到的bbox
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def nms(boxes, nms_threshold=0.6):
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l = len(boxes)
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if l == 0:
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return []
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else:
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b_x = boxes[:, 0]
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b_y = boxes[:, 1]
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b_w = boxes[:, 2]
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b_h = boxes[:, 3]
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scores = boxes[:, 4]
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areas = (b_w + 1) * (b_h + 1)
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order = scores.argsort()[::-1]
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keep = list()
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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(b_x[i], b_x[order[1:]])
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yy1 = np.maximum(b_y[i], b_y[order[1:]])
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xx2 = np.minimum(b_x[i] + b_w[i], b_x[order[1:]] + b_w[order[1:]])
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yy2 = np.minimum(b_y[i] + b_h[i], b_y[order[1:]] + b_h[order[1:]])
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# 相交面积,不重叠时面积为0
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w = np.maximum(0.0, xx2 - xx1 + 1)
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h = np.maximum(0.0, yy2 - yy1 + 1)
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inter = w * h
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# 相并面积,面积1+面积2-相交面积
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union = areas[i] + areas[order[1:]] - inter
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# 计算IoU：交 /（面积1+面积2-交）
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IoU = inter / union
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# 保留IoU小于阈值的box
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inds = np.where(IoU <= nms_threshold)[0]
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order = order[inds + 1] # 因为IoU数组的长度比order数组少一个,所以这里要将所有下标后移一位
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final_boxes = [boxes[i] for i in keep]
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return final_boxes
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# 绘制预测框
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def draw_box(boxes, img, img_shape):
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label = ["background", "person",
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"bicycle", "car", "motorbike", "aeroplane",
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"bus", "train", "truck", "boat", "traffic light",
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"fire hydrant", "stop sign", "parking meter", "bench",
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"bird", "cat", "dog", "horse", "sheep", "cow", "elephant",
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"bear", "zebra", "giraffe", "backpack", "umbrella", "handbag",
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"tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball",
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"kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
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"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon",
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"bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog",
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"pizza", "donut", "cake", "chair", "sofa", "potted plant", "bed", "dining table",
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"toilet", "TV monitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
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"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
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"scissors", "teddy bear", "hair drier", "toothbrush"]
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for box in boxes:
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x1 = int((box[0] - box[2] / 2) * img_shape[1])
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y1 = int((box[1] - box[3] / 2) * img_shape[0])
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x2 = int((box[0] + box[2] / 2) * img_shape[1])
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y2 = int((box[1] + box[3] / 2) * img_shape[0])
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cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
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cv2.putText(img, label[int(box[5])] + ":" + str(round(box[4], 3)), (x1 + 5, y1 + 10), cv2.FONT_HERSHEY_SIMPLEX,
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0.5, (0, 0, 255), 1)
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print(label[int(box[5])] + ":" + "概率值：%.3f" % box[4])
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cv2.imshow('image', img)
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cv2.waitKey(10)
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cv2.destroyAllWindows()
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def draw_box_save(boxes,img,img_shape,img_path):
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label = ["background", "person",
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"bicycle", "car", "motorbike", "aeroplane",
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"bus", "train", "truck", "boat", "traffic light",
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"fire hydrant", "stop sign", "parking meter", "bench",
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"bird", "cat", "dog", "horse", "sheep", "cow", "elephant",
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"bear", "zebra", "giraffe", "backpack", "umbrella", "handbag",
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"tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball",
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"kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
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"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon",
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"bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog",
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"pizza", "donut", "cake", "chair", "sofa", "potted plant", "bed", "dining table",
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"toilet", "TV monitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
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"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase",
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"scissors", "teddy bear", "hair drier", "toothbrush"]
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for box in boxes:
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x1 = int((box[0] - box[2] / 2) * img_shape[1])
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y1 = int((box[1] - box[3] / 2) * img_shape[0])
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x2 = int((box[0] + box[2] / 2) * img_shape[1])
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y2 = int((box[1] + box[3] / 2) * img_shape[0])
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cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
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cv2.putText(img, label[int(box[5])] + ":" + str(round(box[4], 3)), (x1 + 5, y1 + 10), cv2.FONT_HERSHEY_SIMPLEX,
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0.5, (0, 0, 255), 1)
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print(label[int(box[5])] + ":" + "概率值：%.3f" % box[4])
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cv2.imwrite(img_path,img)
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# 获取预测框
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def get_boxes(prediction, anchors, img_shape, confidence_threshold=0.25, nms_threshold=0.6):
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boxes = []
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for i in range(len(prediction)):
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feature_map = prediction[i][0].transpose((2, 1, 0))
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box = get_bbox(feature_map, anchors[i], img_shape, confidence_threshold)
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boxes.extend(box)
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Boxes = nms(np.array(boxes), nms_threshold)
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return Boxes
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