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detect.py
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from __future__ import division
from models import *
from utils.utils import *
from utils.datasets import *
from utils.augmentations import *
from utils.transforms import *
from utils.model_dump import *
from model_wrapper import Yolo
import os
import sys
import time
import datetime
import argparse
from PIL import Image
import torch
import torchvision.transforms as transforms
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
def set_weights(model, parameters):
for i, param in enumerate(model.parameters()):
print(torch.cuda.device_count())
print(torch.cuda.is_available())
param_ = torch.from_numpy(parameters[i]).cuda()
param.data.copy_(param_)
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/custom/classes.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=0.4, 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)
import json
def load_json(filename):
with open(filename) as f:
return json.load(f)
task_config = load_json('data/task_configs/yolo/clients_data/yolo_task1.json')
model = Yolo(task_config)
# 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))
weights = pickle_string_to_obj("yolo_model.pkl")
model.set_weights(weights)
#model.eval() # Set in evaluation mode
dataloader = DataLoader(
ImageFolder(opt.image_folder, transform= \
transforms.Compose([DEFAULT_TRANSFORMS, Resize(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
print(classes)
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()
# testset = ListDataset(task_config['test'], img_size=416,
# multiscale=False, transform=DEFAULT_TRANSFORMS)
# test_dataloader = DataLoader(
# testset,
# batch_size=task_config['batch_size'],
# num_workers=1,
# shuffle=False,
# collate_fn=testset.collate_fn
# )
labels = []
sample_metrics = [] # List of tuples (TP, confs, pred)
total_losses = list()
Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
# for batch_i, (img_paths, input_imgs, targets) in enumerate(tqdm.tqdm(test_dataloader, desc="Detecting objects")):
# # Extract labels
# labels += targets[:, 1].tolist()
# # Rescale target
# targets = Variable(targets.to(device), requires_grad=False)
#
# input_imgs = Variable(input_imgs.type(Tensor), requires_grad=False)
# with torch.no_grad():
# loss, detections = model.yolo(input_imgs, targets)
#
# detections = non_max_suppression(detections, conf_thres=0.8, nms_thres=0.4)
#
#model.yolo.eval()
for batch_i, (img_paths, input_imgs) in enumerate(dataloader):
# Configure input
print(type(input_imgs),input_imgs)
input_imgs = Variable(input_imgs.type(Tensor))
# Get detections
with torch.no_grad():
detections = model.yolo(input_imgs)
print(detections)
detections = non_max_suppression(detections, opt.conf_thres, opt.nms_thres)
print(detections)
# 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
print(type(detections),detections)
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 = os.path.basename(path).split(".")[0]
output_path = os.path.join("output", f"{filename}.png")
#print(output_path)
plt.savefig(output_path, bbox_inches="tight", pad_inches=0.0)
plt.close()