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+from mdutils.mdutils import MdUtils
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+import os
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+import cv2
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+import numpy as np
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+import requests
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+
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+
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+# url = "http://192.168.199.249:4869"
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+
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+# 标签是经过归一化的，需要变回来
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+# xywh格式 ---> box四个顶点的坐标
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+def xywh2lrbt(img_w, img_h, box):
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+ c, x, y, w, h = float(box[0]), float(box[1]), float(box[2]), float(box[3]), float(box[4])
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+ x = x * img_w # 中心坐标x
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+ w = w * img_w # box的宽
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+ y = y * img_h # 中心坐标y
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+ h = h * img_h # box的高
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+
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+ lt_x, lt_y = x - w / 2, y - h / 2 # left_top_x, left_top_y
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+ lb_x, lb_y = x - w / 2, y + h / 2 # left_bottom_x, left_bottom_y
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+ rb_x, rb_y = x + w / 2, y + h / 2 # right_bottom_x, right_bottom_y
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+ rt_x, rt_y = x + w / 2, y - h / 2 # right_top_x, right_bottom_y
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+
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+ lrbt = [[lt_x, lt_y], [lb_x, lb_y], [rb_x, rb_y], [rt_x, rt_y]]
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+
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+ return lrbt, c
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+
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+
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+def IOU(rect1, rect2):
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+ xmin1, ymin1, xmax1, ymax1 = rect1
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+ xmin2, ymin2, xmax2, ymax2 = rect2
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+ s1 = (xmax1 - xmin1) * (ymax1 - ymin1)
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+ s2 = (xmax2 - xmin2) * (ymax2 - ymin2)
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+
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+ sum_area = s1 + s2
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+
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+ left = max(xmin2, xmin1)
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+ right = min(xmax2, xmax1)
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+ top = max(ymin2, ymin1)
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+ bottom = min(ymax2, ymax1)
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+
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+ if left >= right or top >= bottom:
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+ return 0
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+
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+ intersection = (right - left) * (bottom - top)
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+ return intersection / (sum_area - intersection) * 1.0
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+
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+
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+def send_requests():
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+ return result
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+
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+img_path = "./images/test1.jpg"
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+b_img_list = []
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+b_img_list.append(('file_list', ('image.jpeg', open(img_path, 'rb'), 'image/jpeg')))
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+payload = {'model_name': 'ocr-layout', 'img_size': 800, 'download_image': False}
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+response = requests.request("POST", "http://192.168.199.107:18089/detect", headers={}, data=payload, files=b_img_list)
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+result = eval(str(response.text))
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+result = eval(str(result['result']))
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+# print(len(result))
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+
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+mdFile = MdUtils(file_name='yolov5_yili_0818_1', title='Markdown File Example')
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+mdFile.new_header(level=1, title="yolov5预测结果")
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+list_item = ["ori_cla", "predict_cla", "cut_ori_img", "cut_predict_img", "isTrue"]
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+# list_item.extend(["ori_cla", "predict_cla", "cut_ori_img", "cut_predict_img", "isTrue"])
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+# for i in range(len(result[0])):
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+# # print(stencil_list[0][i])
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+#
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+# predict_cla = result[0][i]["class"]
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+
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+# 获取真实的标签
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+label = "./images/test1.txt"
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+with open(label, mode="r", encoding="utf-8") as f:
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+ lines = f.readlines()
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+
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+img = cv2.imread(img_path)
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+img_h, img_w, _ = img.shape
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+
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+arr_label = [] # labels的边界框，存储四个顶点坐标
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+ori_clas = [] # label的真实类别
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+for line in lines:
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+ line = line.split(" ")
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+ lrbt, c = xywh2lrbt(img_w, img_h, line)
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+ arr_label.append(lrbt)
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+ ori_clas.append(c)
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+
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+# cut_imgs = [] # 被剪切的图像
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+num = 0
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+for i in range(len(arr_label)):
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+ lt_x, lt_y = int(arr_label[i][0][0]), int(arr_label[i][0][1])
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+ rb_x, rb_y = int(arr_label[i][2][0]), int(arr_label[i][2][1])
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+ rect1 = [lt_x, lt_y, rb_x, rb_y]
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+
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+ cut_ori_img = img[lt_y:rb_y, lt_x:rb_x, :]
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+ save_cut_ori_img = "./images/cut_imgs/" + str(i) + "_" + str(num) + ".jpg"
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+ cv2.imwrite(save_cut_ori_img, cut_ori_img)
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+ save_cut_ori_img = mdFile.new_inline_image(text='', path=save_cut_ori_img)
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+ num += 1
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+ ori_cla = ori_clas[i]
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+ for j in range(len(result[0])):
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+ res = []
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+ predict_cla = result[0][j]['class']
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+ if ori_cla == predict_cla:
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+ rect2 = result[0][j]['bbox']
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+ iou = IOU(rect1, rect2)
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+ if iou >= 0.5:
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+ cut_predict_img = img[rect2[1]:rect2[3], rect2[0]:rect2[2], :]
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+ save_cut_predict_img = "./images/cut_imgs/" + str(j) + ".jpg"
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+ cv2.imwrite(save_cut_predict_img, cut_predict_img)
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+ save_cut_predict_img = mdFile.new_inline_image(text='', path=save_cut_predict_img)
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+ isTrue = True
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+ list_item.extend([ori_cla, predict_cla, save_cut_ori_img, save_cut_predict_img, isTrue])
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+ # list_item.extend(res)
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+ break
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+ elif j == len(result[0]) - 1:
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+ isTrue = False
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+ list_item.extend([ori_cla, predict_cla, save_cut_ori_img, "", isTrue])
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+ # res.append([ori_cla, predict_cla, "", "", isTrue])
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+ list_item.extend(res)
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+#
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+# print(list_item)
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+# print(len(arr_label))
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+print(len(list_item))
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+# print(len(result[0]))
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+mdFile.new_line()
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+mdFile.new_table(columns=5, rows=len(list_item)//5, text=list_item, text_align='center')
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+mdFile.create_md_file()
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+
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+if __name__ == "__main__":
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+ images_path = "./test_imgs/images/"
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+ labels_path = "./test_imgs/labels/"
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+ list_images = os.listdir(images_path)
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+ list_labels = os.listdir(labels_path)
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+ for image in list_images:
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+ img_name = image.split(".")[0]
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+ mdFile = MdUtils(file_name='yq_0819_' + str(img_name), title='yq_0819_' + str(img_name) + '_iou>=0.5')
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+
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+
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+
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+
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+#
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+# images = os.listdir(images_path)
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+# labels = os.listdir(labels_path)
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+#
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+# assert len(images) == len(labels)
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+#
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+# for i in range(len(images)):
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+# image = images[i]
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+# label = labels[i]
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+# with open(labels_path + label, mode="r", encoding="utf-8") as f:
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+# lines = f.readlines()
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+#
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+# img = images_path + images[i]
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+# img = cv2.imread(img)
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+# img_h, img_w, _ = img.shape
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+#
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+# arr_label = [] # labels的边界框，存储四个顶点坐标
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+# ori_cla = [] # label的真实类别
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+# for line in lines:
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+# line = line.split(" ")
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+# lrbt, c = xywh2lrbt(img_w, img_h, line)
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+# arr_label.append(lrbt)
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+# ori_cla.append(c)
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+#
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+# cut_path = "./valid_img/cut/"
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+#
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+# for i in range(len(arr_label)):
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+# lt_x, lt_y = int(arr_label[i][0][0]), int(arr_label[i][0][1])
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+# rb_x, rb_y = int(arr_label[i][2][0]), int(arr_label[i][2][1])
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+# cut_img = img[lt_y:rb_y, lt_x:rb_x, :]
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+# cv2.imwrite(cut_path + str(i) + ".jpg", cut_img)
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+#
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+# box = np.reshape(np.array(arr_label[i]), [-1, 1, 2]).astype(np.int32)
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+# img = cv2.polylines(np.array(img), [box], True, (255, 0, 0), 5)
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+#
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+#
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+#
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+#
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+# mdFile.create_md_file()
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+#
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+#
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Raychar