File size: 4,609 Bytes
5f3229a bd0a1f8 5f3229a bd0a1f8 6e9f976 bd0a1f8 6e9f976 bd0a1f8 6e9f976 55b849d 6e9f976 55b849d 6e9f976 bd0a1f8 e6267a7 16c9a25 27ce824 bd0a1f8 1cd4e94 d85ad2a 39d901b 9483426 3d4105d 1cd4e94 9a0ab6b 1cd4e94 0680486 527a9fe 0680486 527a9fe 1cd4e94 6e9f976 0680486 6e9f976 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 |
import streamlit as st
import numpy as np
import PIL
from PIL import Image
from streamlit_image_select import image_select
from ultralytics import YOLO
import cv2
import matplotlib.pyplot as plt
import os
import pathlib
import PIL
import PIL.Image
import xml.etree.ElementTree as ET
import pybboxes as pbx
from pybboxes import BoundingBox
from pathlib import Path
import colorsys
import random
####################################################
# Support functions
####################################################
# helper function to generate random colors for class boxes
def generate_label_colors(count):
colors = []
for c in range(count):
h,s,l = random.random(), 0.5 + random.random()/2.0, 0.4 + random.random()/5.0
r,g,b = [int(256*i) for i in colorsys.hls_to_rgb(h,l,s)]
colors.append(tuple([int(r), int(g), int(b)]))
return colors
# helper function to run model inference
def run_inference(model, img_paths):
return model.predict(img_paths)
# helper function to process result and return image with bbox overlays
def process_inference_result(result, class_colors):
# setup label counts
label_counts = {'class': [], 'count': []}
# extract result objects
img = result.orig_img
dh, dw, _ = img.shape
boxes = result.boxes.xywhn.tolist()
labels = [int(label) for label in result.boxes.cls]
conf = [float(label) for label in result.boxes.conf]
# create image
for i, bbox in enumerate(boxes):
x = bbox[0]
y = bbox[1]
w = bbox[2]
h = bbox[3]
voc_box = pbx.convert_bbox([x, y, w, h], from_type="yolo", to_type="voc", image_size=(dw, dh))
voc_x1 = voc_box[0]
voc_y1 = voc_box[1]
voc_x2 = voc_box[2]
voc_y2 = voc_box[3]
class_name = aircraft_lookup[classes[labels[i]]]
cv2.rectangle(img, (voc_x1, voc_y1), (voc_x2, voc_y2), class_colors[labels[i]], 2)
cv2.putText(img, class_name + ' ' + str(round(conf[i], 2)), (voc_x1, voc_y1-5), cv2.FONT_HERSHEY_SIMPLEX, 1, class_colors[labels[i]], 2)
if class_name not in label_counts['class']:
label_counts['class'].append(class_name)
label_counts['count'].append(1)
else:
label_counts['count'][label_counts['class'].index(class_name)] += 1
return img, label_counts
def get_detection_count_display(classes):
class_names = classes["class"]
counts = classes["count"]
if(len(classes)):
disp_str = "[Aircraft detected] "
for i, c in enumerate(class_names):
disp_str += c + ": " + str(counts[i]) + " "
else:
disp_str = "[No aircraft detected]"
return disp_str
def run_process_show(img_path):
results = model(img_path)
processed_image = process_inference_result(results[0], rand_class_colors)
return processed_image
####################################################
# Setup model and class parameters
####################################################
# init model
model = YOLO("weights/best.pt")
# setup label classes
classes = ['A6', 'A17', 'A16', 'A15', 'A5', 'A20', 'A14', 'A12', 'A8', 'A2', 'A7', 'A18', 'A13', 'A4', 'A19', 'A1', 'A3', 'A10', 'A11', 'A9']
# setup mapping of class labels to real aircraft names
aircraft_names = ['SU-35', 'C-130', 'C-17', 'C-5', 'F-16', 'TU-160', 'E-3', 'B-52', 'P-3C', 'B-1B', 'E-8', 'TU-22', 'F-15', 'KC-135', 'F-22', 'FA-18', 'TU-95', 'KC-10', 'SU-34', 'SU-24']
aircraft_lookup = {}
for i in range(len(classes)):
aircraft_lookup['A' + str(i+1)] = aircraft_names[i]
# generate bbox colors for each class
rand_class_colors = generate_label_colors(len(classes))
# logo
logo = cv2.imread("images/oneeye2.jpg")
logo_img = st.image(logo)
####################################################
# Main UX Loop
####################################################
img = image_select(
label="Select an airbase",
images=[
cv2.imread("images/edwards.jpg"),
cv2.imread("images/edwards2.jpg"),
cv2.imread("images/buturlinovka2.jpg"),
cv2.imread("images/engels.jpg"),
cv2.imread("images/nellis1.jpg"),
cv2.imread("images/littlerock.jpg"),
cv2.imread("images/hmeimim.jpg"),
cv2.imread("images/hill.jpg"),
],
captions=["Edwards AFB 1", "Edwards AFB 2", "Buturlinovka District", "Engels", "Nellis AFB", "Little Rock AFB", "Hmiemim Syria", "Hill AFB"],
)
# process image through detector
status = st.empty()
# show un-classified image
big_img = st.image(img)
# show status message
status.write("Running OneEye detector...")
# process image through detector
img2, detection_labels = run_process_show(img)
big_img.image(img2)
status.write(get_detection_count_display(detection_labels)) |