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Fixed display problem. Was getting bad colors. Enabled ability to run using either PSG or PSG-Qt by only changing import.

This commit is contained in:
MikeTheWatchGuy 2018-12-03 12:00:45 -05:00
parent ccfd556ab6
commit 76d34616a6
1 changed files with 14 additions and 23 deletions
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37
YoloObjectDetection/yolo.py
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@ -1,28 +1,28 @@
# USAGE
# python yolo.py --image images/baggage_claim.jpg --yolo yolo-coco
"""
usage: yolo_video.py [-h] -i INPUT -o OUTPUT -y YOLO [-c CONFIDENCE]
A Yolo image processor with a GUI front-end
The original code was command line driven. Now these parameters are collected via a GUI
old usage: yolo_video.py [-h] -i INPUT -o OUTPUT -y YOLO [-c CONFIDENCE]
[-t THRESHOLD]
"""
# import the necessary packages
import numpy as np
import argparse
import time
import cv2
import os
import PySimpleGUI as sg
from PIL import Image
import io
import PySimpleGUIQt as sg
layout = [
[sg.Text('YOLO')],
[sg.Text('Path to image'), sg.In(r'A:\Dropbox\Camera Uploads\2018-11-16 17.35.15.jpg',size=(40,1), key='image'), sg.FileBrowse()],
[sg.Text('Yolo base path'), sg.In(r'C:\Python\PycharmProjects\yolo-object-detection\yolo-coco',size=(40,1), key='yolo'), sg.FolderBrowse()],
[sg.Text('Confidence'), sg.Slider(range=(0,1),orientation='h', resolution=.1, default_value=.5, size=(15,15), key='confidence')],
[sg.Text('Threshold'), sg.Slider(range=(0,1), orientation='h', resolution=.1, default_value=.3, size=(15,15), key='threshold')],
[sg.OK(), sg.Cancel()]
[sg.Text('Path to image'), sg.In(r'C:/Python/PycharmProjects/YoloObjectDetection/images/baggage_claim.jpg',size=(40,1), key='image'), sg.FileBrowse()],
[sg.Text('Yolo base path'), sg.In(r'yolo-coco',size=(40,1), key='yolo'), sg.FolderBrowse()],
[sg.Text('Confidence'), sg.Slider(range=(0,10),orientation='h', resolution=1, default_value=5, size=(15,15), key='confidence')],
[sg.Text('Threshold'), sg.Slider(range=(0,10), orientation='h', resolution=1, default_value=3, size=(15,15), key='threshold')],
[sg.OK(), sg.Cancel(), sg.Stretch()]
]
win = sg.Window('YOLO',
@ -45,8 +45,8 @@ win.Close()
# args = vars(ap.parse_args())
# load the COCO class labels our YOLO model was trained on
args['threshold'] = float(args['threshold'])
args['confidence'] = float(args['confidence'])
args['threshold'] = float(args['threshold']/10)
args['confidence'] = float(args['confidence']/10)
labelsPath = os.path.sep.join([args["yolo"], "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")
@ -66,6 +66,7 @@ net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
# load our input image and grab its spatial dimensions
image = cv2.imread(args["image"])
(H, W) = image.shape[:2]
# determine only the *output* layer names that we need from YOLO
@ -143,19 +144,9 @@ if len(idxs) > 0:
0.5, color, 2)
# show the output image
imgbytes = cv2.imencode('.png', image)[1].tobytes() # ditto
# let img be the PIL image
img = Image.fromarray(image) # create PIL image from frame
size = img.size
size = (size[0]//4, size[1]//4)
img = img.resize(size)
bio = io.BytesIO() # a binary memory resident stream
img.save(bio, format='PNG') # save image as png to it
imgbytes = bio.getvalue() # this can be used by OpenCV hopefully
# imgbytes = cv2.imencode('.png', image)[1].tobytes() # ditto
layout = [
[sg.Text('Yolo Output')],
[sg.Image(data=imgbytes)],