细说PyTorch深度学习：理论、算法、模型与编程实现 02

# # IoU计算。 # def cal_iou_xyxy(box1,box2): # x1min, y1min, x1max, y1max = box1[0], box1[1], box1[2], box1[3] # x2min, y2min, x2max, y2max = box2[0], box2[1], box2[2], box2[3] # #计算两个框的面积 # s1 = (y1max - y1min + 1.) * (x1max - x1min + 1.) # s2 = (y2max - y2min + 1.) * (x2max - x2min + 1.) # # #计算相交部分的坐标 # xmin = max(x1min,x2min) # ymin = max(y1min,y2min) # xmax = min(x1max,x2max) # ymax = min(y1max,y2max) # # inter_h = max(ymax - ymin + 1, 0) # inter_w = max(xmax - xmin + 1, 0) # # intersection = inter_h * inter_w # union = s1 + s2 - intersection # # #计算iou # iou = intersection / union # return iou # # box1 = [100,100,200,200] # box2 = [120,120,220,220] # iou = cal_iou_xyxy(box1,box2) # print(iou) # NMS计算 import cv2 import numpy as np import matplotlib.pyplot as plt # iou = np.array([0.57184716, 0.76505679]) # order = np.array([2, 1, 4, 5, 6, 8]) # inds = np.where(iou <= 0.8)[0] # 将重叠度大于给定阈值的边框剔除掉，仅保留剩下的边框，返回相应的下标 [1,2] # print('inds:', inds) # 得到分数小于阈值的框索引 [0 1] # order = order[inds + 1] # 从剩余的候选框中继续筛选[1 2] # print('order:', order) # [1 4 ] # # NMS计算实例。 # import numpy as np # boxes = np.array([[100, 100, 210, 210, 0.72], # [250, 250, 420, 420, 0.8], # [220, 220, 320, 330, 0.92], # [100, 100, 210, 210, 0.72], # [230, 240, 325, 330, 0.81], # [220, 230, 315, 340, 0.9]]) # # # def py_cpu_nms(dets, thresh): # # x1 = dets[:, 0] # y1 = dets[:, 1] # x2 = dets[:, 2] # y2 = dets[:, 3] # # areas = (y2 - y1 + 1) * (x2 - x1 + 1) # scores = dets[:, 4] # keep = [] # index = scores.argsort()[::-1] # # while index.size > 0: # print("sorted index of boxes according to scores", index) # # i = index[0] # keep.append(i) # # x11 = np.maximum(x1[i], x1[index[1:]]) # y11 = np.maximum(y1[i], y1[index[1:]]) # x22 = np.minimum(x2[i], x2[index[1:]]) # y22 = np.minimum(y2[i], y2[index[1:]]) # # print("x1 values by original order:", x1) # print("x1 value by scores:", x1[index[:]]) # print("x11 value means replacing the less value compared"\ # " with the value by the largest score :" , x11) # # w = np.maximum(0, x22 - x11 + 1) # h = np.maximum(0, y22 - y11 + 1) # overlaps = w * h # # ious = overlaps / (areas[i] + areas[index[1:]] - overlaps) # # idx = np.where(ious <= thresh)[0] # # index = index[idx + 1] # # return keep # # # import matplotlib.pyplot as plt # # # def plot_bbox(dets, c='k', title_name="title"): # x1 = dets[:, 0] # y1 = dets[:, 1] # x2 = dets[:, 2] # y2 = dets[:, 3] # # plt.plot([x1, x2], [y1, y1], c) # plt.plot([x1, x1], [y1, y2], c) # plt.plot([x1, x2], [y2, y2], c) # plt.plot([x2, x2], [y1, y2], c) # plt.title(title_name) # # if __name__ == '__main__': # plot_bbox(boxes, 'k', title_name="before NMS") # plt.show() # # keep = py_cpu_nms(boxes, thresh=0.7) # # plot_bbox(boxes[keep], 'r', title_name="after NMS") # plt.show() # #fasterrcnn_resnet50网络目标检测。 # ## 导入相关模块 # import numpy as np # import torchvision # import torch # import torchvision.transforms as transforms # from PIL import Image, ImageDraw, ImageFont # import matplotlib.pyplot as plt # # model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True) # model.eval() # # # ## 准备需要检测的图像 # image = Image.open("./MSCOCO2017/val2017/000000005477.jpg") # transform_d = transforms.Compose([transforms.ToTensor()]) # image_t = transform_d(image) # pred = model([image_t]) # print(pred) # # # COCO_INSTANCE_CATEGORY_NAMES = [ # '__BACKGROUND__', 'person', 'bicycle', 'car', 'motorcycle', # 'airplane', 'bus', 'train', 'trunk', 'boat', 'traffic light', # 'fire hydrant', 'N/A', 'stop sign', 'parking meter', 'bench', # 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', # 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', # 'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', # 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', # 'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass', # 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', # 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', # 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', # 'dining table', 'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', # 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', # 'toaster', 'toaster', 'sink', 'refrigerator', 'N/A', 'book', 'clock', # 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush' # ] # # ## 检测出目标的类别和得分 # pred_class = [COCO_INSTANCE_CATEGORY_NAMES[ii] for ii in list(pred[0]['labels'].numpy())] # pred_score = list(pred[0]['scores'].detach().numpy()) # # ## 检测出目标的边界框 # pred_boxes = [[ii[0], ii[1], ii[2], ii[3]] for ii in list(pred[0]['boxes'].detach().numpy())] # # ## 只保留识别的概率大约 0.5 的结果。 # pred_index = [pred_score.index(x) for x in pred_score if x > 0.5] # # ## 设置图像显示的字体 # fontsize = np.int16(image.size[1] / 20) # # ## 可视化对象 # draw = ImageDraw.Draw(image) # for index in pred_index: # box = pred_boxes[index] # draw.rectangle(box, outline="blue") # texts = pred_class[index]+":"+str(np.round(pred_score[index], 2)) # draw.text((box[0], box[1]), texts, fill="blue") # # ## 显示图像 # plt.imshow(image) # plt.show() # 随机选择文件夹中3张图片使用fasterrcnn_resnet50网络目标检测。 import numpy as np import torchvision import torch import torchvision.transforms as transforms from PIL import Image, ImageDraw, ImageFont import matplotlib.pyplot as plt import os COCO_INSTANCE_CATEGORY_NAMES = [ '__BACKGROUND__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'trunk', 'boat', 'traffic light', 'fire hydrant', 'N/A', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack', 'umbrella', 'N/A', 'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table', 'N/A', 'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'toaster', 'sink', 'refrigerator', 'N/A', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush' ] # 加载预训练的模型 model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True) model.eval() # 准备需要检测的图像文件夹 image_folder_path = "./MSCOCO2017/val2017/" number = 3 transform_d = transforms.Compose([transforms.ToTensor()]) # 随机选择10张图片 dirs = os.listdir(image_folder_path) idx = np.random.randint(0, len(dirs), number) for i in idx: image_path = os.path.join(image_folder_path, dirs[i]) image = Image.open(image_path) image_t = transform_d(image) pred = model([image_t]) print(pred) # 检测出