我已经创建了一个模型来识别图像中的对象，它对我来说很好，我有根据已经训练的权重检测对象的代码等等，但我只需要用我检测到的来创建一个新图像，例如，如果我有一张公园里的猫的图像，我怎么能那样做？我给你我的当前代码，我在其中检测对象：
from __future__ import division
from models import *
from utils.utils import *
from utils.datasets import *
import os
import sys
import time
import datetime
import argparse
from PIL import Image
import torch
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.ticker import NullLocator
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--image_folder", type=str, default="data/samples", help="path to dataset")
parser.add_argument("--model_def", type=str, default="config/yolov3.cfg", help="path to model definition file")
parser.add_argument("--weights_path", type=str, default="weights/yolov3.weights", help="path to weights file")
parser.add_argument("--class_path", type=str, default="data/coco.names", help="path to class label file")
parser.add_argument("--conf_thres", type=float, default=0.8, help="object confidence threshold")
parser.add_argument("--nms_thres", type=float, default=0.5, help="iou thresshold for non-maximum suppression")
parser.add_argument("--batch_size", type=int, default=1, help="size of the batches")
parser.add_argument("--n_cpu", type=int, default=0, help="number of cpu threads to use during batch generation")
parser.add_argument("--img_size", type=int, default=416, help="size of each image dimension")
parser.add_argument("--checkpoint_model", type=str, help="path to checkpoint model")
opt = parser.parse_args()
print(opt)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs("output", exist_ok=True)
# Set up model
model = Darknet(opt.model_def, img_size=opt.img_size).to(device)
if opt.weights_path.endswith(".weights"):
# Load darknet weights
model.load_darknet_weights(opt.weights_path)
else:
# Load checkpoint weights
model.load_state_dict(torch.load(opt.weights_path))
model.eval() # Set in evaluation mode
dataloader = DataLoader(
ImageFolder(opt.image_folder, img_size=opt.img_size),
batch_size=opt.batch_size,
shuffle=False,
num_workers=opt.n_cpu,
)
classes = load_classes(opt.class_path) # Extracts class labels from file
Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
imgs = [] # Stores image paths
img_detections = [] # Stores detections for each image index
print("nPerforming object detection:")
prev_time = time.time()
for batch_i, (img_paths, input_imgs) in enumerate(dataloader):
# Configure input
input_imgs = Variable(input_imgs.type(Tensor))
# Get detections
with torch.no_grad():
detections = model(input_imgs)
detections = non_max_suppression(detections, opt.conf_thres, opt.nms_thres)
# Log progress
current_time = time.time()
inference_time = datetime.timedelta(seconds=current_time - prev_time)
prev_time = current_time
print("t+ Batch %d, Inference Time: %s" % (batch_i, inference_time))
# Save image and detections
imgs.extend(img_paths)
img_detections.extend(detections)
# Bounding-box colors
cmap = plt.get_cmap("tab20b")
colors = [cmap(i) for i in np.linspace(0, 1, 20)]
print("nSaving images:")
# Iterate through images and save plot of detections
for img_i, (path, detections) in enumerate(zip(imgs, img_detections)):
print("(%d) Image: '%s'" % (img_i, path))
# Create plot
img = np.array(Image.open(path))
plt.figure()
fig, ax = plt.subplots(1)
ax.imshow(img)
# Draw bounding boxes and labels of detections
if detections is not None:
# Rescale boxes to original image
detections = rescale_boxes(detections, opt.img_size, img.shape[:2])
unique_labels = detections[:, -1].cpu().unique()
n_cls_preds = len(unique_labels)
bbox_colors = random.sample(colors, n_cls_preds)
for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:
print("t+ Label: %s, Conf: %.5f" % (classes[int(cls_pred)], cls_conf.item()))
box_w = x2 - x1
box_h = y2 - y1
color = bbox_colors[int(np.where(unique_labels == int(cls_pred))[0])]
# Create a Rectangle patch
bbox = patches.Rectangle((x1, y1), box_w, box_h, linewidth=2, edgecolor=color, facecolor="none")
# Add the bbox to the plot
ax.add_patch(bbox)
# Add label
plt.text(
x1,
y1,
s=classes[int(cls_pred)],
color="white",
verticalalignment="top",
bbox={"color": color, "pad": 0},
)
# Save generated image with detections
plt.axis("off")
plt.gca().xaxis.set_major_locator(NullLocator())
plt.gca().yaxis.set_major_locator(NullLocator())
filename = path.split("/")[-1].split(".")[0]
plt.savefig(f"output/{filename}.png", bbox_inches="tight", pad_inches=0.0)
plt.close()
我如何在这个代码中添加一个片段来剪切我检测到的对象？非常感谢，如果这是解决方案所必需的，我正在使用pytho3和tensorflow 2，再次感谢
您可以使用以下切片表示法裁剪图像：
crop=img[y1:y1+box_height，x1:x1+box_width，：]；
只要numpy数组是numpy.uint8，它就应该被straigtforward保存：
pimg = Image.fromarray(img);
pimg.save("test.png");
使用解决
img = cv2.imread('./202.jpg')
crop_img = img[y:y+h, x:x+w]
#cv2.imshow('img', crop_img)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
cv2.imwrite('./pruebas/resultado.png', crop_img)