1 Pytorch中的目标检测内置模型
在torchvision库下的modelsldetecton目录中，找到__int__.py文件。该文件中存放着可以导出的PyTorch内置的目标检测模型。
2 MaskR-CNN内置模型实现目标检测
2.1 代码逻辑简述
将COCO2017数据集上的预训练模型maskrcnm_resnet50_fpn_coco加载到内存，并使用该模型对图片进行目标检测。
2.2 代码实战 ：MaskR-CNN内置模型实现目标检测
Maskrcnn_resent_Object Detection.py
from PIL import Image import matplotlib.pyplot as plt import torchvision.transforms as T import torchvision import numpy as np import cv2 import random import os os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' # 加载模型 model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True) model.eval() # 标签 COCO_INSTANCE_CATEGORY_NAMES = [ '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', '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', 'sink', 'refrigerator', 'N/A', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'] def get_prediction(img_path, threshold): # 定义模型，并根据阈值过滤结果 img = Image.open(img_path).convert('RGB') # 需要转化：RuntimeError: The size of tensor a (4) must match the size of tensor b (3) at non-singleton transform = T.Compose([T.ToTensor()]) img = transform(img) # MaskR - CNN模型会返回一个字典对象，该字典对象中包含如下key值： # boxes∶每个目标的边框信息。 # labels：每个目标的分类信息。 # scores：每个目标的分类分值。 # masks：每个目标的像素掩码（Mask)。 pred = model([img]) # 调用模型 print('pred') print(pred) pred_score = list(pred[0]['scores'].detach().numpy()) pred_t = [pred_score.index(x) for x in pred_score if x > threshold][-1] print("masks>0.5") print(pred[0]['masks'] > 0.5) masks = (pred[0]['masks'] > 0.5).squeeze().detach().cpu().numpy() print("this is masks") print(masks) pred_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(pred[0]['labels'].numpy())] pred_boxes = [[(i[0], i[1]), (i[2], i[3])] for i in list(pred[0]['boxes'].detach().numpy())] masks = masks[:pred_t + 1] pred_boxes = pred_boxes[:pred_t + 1] pred_class = pred_class[:pred_t + 1] return masks, pred_boxes, pred_class def random_colour_masks(image): colours = [[0, 255, 0], [0, 0, 255], [255, 0, 0], [0, 255, 255], [255, 255, 0], [255, 0, 255], [80, 70, 180],[250, 80, 190], [245, 145, 50], [70, 150, 250], [50, 190, 190]] r = np.zeros_like(image).astype(np.uint8) g = np.zeros_like(image).astype(np.uint8) b = np.zeros_like(image).astype(np.uint8) randcol = colours[random.randrange(0, 10)] r[image == 1] = randcol[0] g[image == 1] = randcol[1] b[image == 1] = randcol[2] coloured_mask = np.stack([r, g, b], axis=2) print("randcol",randcol) return coloured_mask, randcol def instance_segmentation_api(img_path, threshold=0.5, rect_th=3, text_size=3, text_th=5): # 进行目标检测 masks, boxes, pred_cls = get_prediction(img_path, threshold) # 调用模型 print("已加载COCO标签类数：", len(COCO_INSTANCE_CATEGORY_NAMES)) img = cv2.imread(img_path) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for i in range(len(masks)): rgb_mask, randcol = random_colour_masks(masks[i]) # 使用随机颜色为模型的掩码区进行填充。 img = cv2.addWeighted(img, 1, rgb_mask, 0.5, 0) # 元组里面有小数，需要转化为整数 否则报错 T1,T2 = boxes[i][0],boxes[i][1] x1 = int(T1[0]) y1 = int(T1[1]) x2 = int(T2[0]) y2 = int(T2[1]) cv2.rectangle(img, (x1,y1), (x2,y2), color=randcol, thickness=rect_th) # # putText各参数依次是：图片，添加的文字，左上角坐标，字体，字体大小，颜色黑，字体粗细 cv2.putText(img, pred_cls[i], (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, text_size, randcol, thickness=text_th) plt.figure(figsize=(20, 30)) plt.imshow(img) plt.xticks([]) plt.yticks([]) plt.show() # 显示模型结果 instance_segmentation_api('./models_2/mask.jpg')
