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detect2.py
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detect2.py
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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
import cv2
import numpy as np
from PIL import Image
from IPython.display import clear_output
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
def detect_image(img):
# scale and pad image
ratio = min(img_size/img.size[0], img_size/img.size[1])
imw = round(img.size[0] * ratio)
imh = round(img.size[1] * ratio)
img_transforms=transforms.Compose([transforms.Resize((imh,imw)),
transforms.Pad((max(int((imh-imw)/2),0), max(int((imw-imh)/2),0), max(int((imh-imw)/2),0), max(int((imw-imh)/2),0)), (128,128,128)), transforms.ToTensor(),
])
# convert image to Tensor
image_tensor = img_transforms(img).float()
image_tensor = image_tensor.unsqueeze_(0)
input_img = Variable(image_tensor.type(Tensor))
# run inference on the model and get detections
with torch.no_grad():
detections = model(input_img)
detections = non_max_suppression(detections, opt.conf_thres, opt.nms_thres)
return detections[0]
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=0.4, 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)
#f = open(sys.argv[1], 'w')
videopath = 'data/samples/test.mp4'
vid = cv2.VideoCapture(videopath)
out = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc(*'MJPG'), 10, (int(vid.get(3)), int(vid.get(4))), False)
# 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,
# )
img_size = opt.img_size
classes = load_classes(opt.class_path) # Extracts class labels from file
Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
# Bounding-box colors
cmap = plt.get_cmap("tab20b")
colors = [cmap(i) for i in np.linspace(0, 1, 20)]
while True:
ret, frame = vid.read()
if not(ret):
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
pilimg = Image.fromarray(frame)
detections = detect_image(pilimg)
img = np.array(pilimg)
plt.figure()
fig, ax = plt.subplots(1)
ax.imshow(img)
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},
)
out.write(frame)
# Save generated image with detections
plt.axis("off")
plt.gca().xaxis.set_major_locator(NullLocator())
plt.gca().yaxis.set_major_locator(NullLocator())
plt.savefig(f"output/output.jpg", bbox_inches="tight", pad_inches=0.0)
plt.close()
#cv2.destroyAllWindows()
out.release()
clear_output(wait=True)