基于pytorch预训练模型使用Faster RCNN调用摄像头进行目标检测【无敌详细！简单！超少代码！】

详细完整项目链接：https://download.csdn.net/download/weixin_46570668/86954697?spm=1001.2014.3001.5503

使用 Pytorch 自带的预训练模型 fasterrcnn_resnet50_fpn，该模型是在COCO 数据集上进行预训练。
COCO 数据集是一个大型的、丰富的用于物体检测、分割的数据集，提供的图片类别有80 类，有超过 33 万张图片，其中 20 万张图片有标注，整个数据集中个体的数目超过 150万个。COCO2017 数据共 80 个类别，类别的 id 号不是连续的，最大为 90。

记载预训练模型代码如下：

import torch
import torchvision
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True, progress=True, num_classes=91, pretrained_backbone=True)

使用OpenCV调用摄像头，代码如下：

import cv2
capture=cv2.VideoCapture(0)

获得标签及对应类别（这个文件好像可以去COCO还是Pytorch的什么网站上下载，我有点记不清了，不过我也把它和代码一起打包上传资源）

classes_path = 'classes.txt'
classes_path = os.path.expanduser(classes_path)
with open(classes_path) as f:class_names = f.readlines()
class_names = [c.strip() for c in class_names]
num_classes = len(class_names)

进行目标检测，并画图函数

def detect_image(image):start_time = time.time()image_shape = np.array(np.shape(image)[0:2])old_width = image_shape[1]old_height = image_shape[0]old_image = copy.deepcopy(image)width, height = get_new_img_size(old_width, old_height)image = image.resize([width, height])photo = np.array(image, dtype=np.float32) / 255photo = np.transpose(photo, (2, 0, 1))with torch.no_grad():model.eval()# Using RGB conversion to convert image pixel-wise into a numpy arraynp_sample_image = np.array(image.convert("RGB"))# Converting the numpy array to a tensortransformed_img = torchvision.transforms.transforms.ToTensor()(torchvision.transforms.ToPILImage()(np_sample_image))result = model([transformed_img])bbox = []label = []conf = []for i in range(len(result[0]['scores'])):if result[0]['scores'][i] > confidence:bbox.append(result[0]['boxes'][i].tolist())label.append(result[0]['labels'][i].tolist())conf.append(result[0]['scores'][i].tolist())bbox = np.array(bbox)label = np.array(label)conf = np.array(conf)bbox[:, 0::2] = (bbox[:, 0::2]) / width * old_widthbbox[:, 1::2] = (bbox[:, 1::2]) / height * old_heightbbox = np.array(bbox, np.int32)image = old_imagethickness = (np.shape(old_image)[0] + np.shape(old_image)[1]) // old_width * 2font = ImageFont.truetype(font='simhei.ttf',size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32'))for i, c in enumerate(label):predicted_class = class_names[int(c)]score = conf[i]left, top, right, bottom = bbox[i]top = top - 5left = left - 5bottom = bottom + 5right = right + 5top = max(0, np.floor(top + 0.5).astype('int32'))left = max(0, np.floor(left + 0.5).astype('int32'))bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32'))right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32'))# 画框框label = '{} {:.2f}'.format(predicted_class, score)draw = ImageDraw.Draw(image)label_size = draw.textsize(label, font)label = label.encode('utf-8')print(label)if top - label_size[1] >= 0:text_origin = np.array([left, top - label_size[1]])else:text_origin = np.array([left, top + 1])for i in range(thickness):draw.rectangle([left + i, top + i, right - i, bottom - i],outline=colors[int(c)])draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)],fill=colors[int(c)])draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font)del drawprint("time:", time.time() - start_time)return image

依次读入帧，转变格式，并送入模型进行检测

t1 = time.time()# 读取某一帧ref,frame=capture.read()# 格式转变，BGRtoRGBframe = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)# 转变成Imageframe = Image.fromarray(np.uint8(frame))frame = np.array(detect_image(frame))frame = cv2.cvtColor(frame,cv2.COLOR_RGB2BGR)fps  = ( fps + (1./(time.time()-t1)) ) / 2print("fps= %.2f"%(fps))cv2.imshow("video",frame)c= cv2.waitKey(30) & 0xffif c==27:capture.release()break

完整代码如下！赶紧试试看！

import numpy as np
import time
import torch
import torchvision
import os
import cv2
import copy
import colorsys
from PIL import Image, ImageFont, ImageDraw
# 调用摄像头
capture=cv2.VideoCapture(0)fps = 0.0model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True, progress=True, num_classes=91, pretrained_backbone=True)def get_new_img_size(width, height, img_min_side=600):if width <= height:f = float(img_min_side) / widthresized_height = int(f * height)resized_width = int(img_min_side)else:f = float(img_min_side) / heightresized_width = int(f * width)resized_height = int(img_min_side)return resized_width, resized_height# classes_path = 'voc_classes.txt'
classes_path = 'classes.txt'
classes_path = os.path.expanduser(classes_path)
with open(classes_path) as f:class_names = f.readlines()
class_names = [c.strip() for c in class_names]
num_classes = len(class_names)
mean = torch.Tensor([0,0,0,0]).cuda().repeat(num_classes+1)[None]
std = torch.Tensor([0.1, 0.1, 0.2, 0.2]).cuda().repeat(num_classes+1)[None]
# 画框设置不同的颜色
hsv_tuples = [(x / len(class_names), 1., 1.)for x in range(len(class_names))]
colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),colors))
#---------------------------------------------------#
#   获得所有的分类
#---------------------------------------------------#confidence = 0.5
def detect_image(image):start_time = time.time()image_shape = np.array(np.shape(image)[0:2])old_width = image_shape[1]old_height = image_shape[0]old_image = copy.deepcopy(image)width, height = get_new_img_size(old_width, old_height)image = image.resize([width, height])photo = np.array(image, dtype=np.float32) / 255photo = np.transpose(photo, (2, 0, 1))with torch.no_grad():model.eval()# Using RGB conversion to convert image pixel-wise into a numpy arraynp_sample_image = np.array(image.convert("RGB"))# Converting the numpy array to a tensortransformed_img = torchvision.transforms.transforms.ToTensor()(torchvision.transforms.ToPILImage()(np_sample_image))result = model([transformed_img])bbox = []label = []conf = []for i in range(len(result[0]['scores'])):if result[0]['scores'][i] > confidence:bbox.append(result[0]['boxes'][i].tolist())label.append(result[0]['labels'][i].tolist())conf.append(result[0]['scores'][i].tolist())bbox = np.array(bbox)label = np.array(label)conf = np.array(conf)bbox[:, 0::2] = (bbox[:, 0::2]) / width * old_widthbbox[:, 1::2] = (bbox[:, 1::2]) / height * old_heightbbox = np.array(bbox, np.int32)image = old_imagethickness = (np.shape(old_image)[0] + np.shape(old_image)[1]) // old_width * 2font = ImageFont.truetype(font='simhei.ttf',size=np.floor(3e-2 * np.shape(image)[1] + 0.5).astype('int32'))for i, c in enumerate(label):predicted_class = class_names[int(c)]score = conf[i]left, top, right, bottom = bbox[i]top = top - 5left = left - 5bottom = bottom + 5right = right + 5top = max(0, np.floor(top + 0.5).astype('int32'))left = max(0, np.floor(left + 0.5).astype('int32'))bottom = min(np.shape(image)[0], np.floor(bottom + 0.5).astype('int32'))right = min(np.shape(image)[1], np.floor(right + 0.5).astype('int32'))# 画框框label = '{} {:.2f}'.format(predicted_class, score)draw = ImageDraw.Draw(image)label_size = draw.textsize(label, font)label = label.encode('utf-8')print(label)if top - label_size[1] >= 0:text_origin = np.array([left, top - label_size[1]])else:text_origin = np.array([left, top + 1])for i in range(thickness):draw.rectangle([left + i, top + i, right - i, bottom - i],outline=colors[int(c)])draw.rectangle([tuple(text_origin), tuple(text_origin + label_size)],fill=colors[int(c)])draw.text(text_origin, str(label, 'UTF-8'), fill=(0, 0, 0), font=font)del drawprint("time:", time.time() - start_time)return imagewhile(True):t1 = time.time()# 读取某一帧ref,frame=capture.read()# 格式转变，BGRtoRGBframe = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)# 转变成Imageframe = Image.fromarray(np.uint8(frame))frame = np.array(detect_image(frame))frame = cv2.cvtColor(frame,cv2.COLOR_RGB2BGR)fps  = ( fps + (1./(time.time()-t1)) ) / 2print("fps= %.2f"%(fps))cv2.imshow("video",frame)c= cv2.waitKey(30) & 0xffif c==27:capture.release()break