lobe-python-imagesfromfolder2labeledFolder

Batch image prediction + CSV output + sort copy of the images into labeled folders

Updated by Jerry Vermanen on Saturday May 15th

What this Python script does:

1. Batch process a folder with JPG-images
2. Label each image based on your Lobe-trained CV model and assign a confidence score (for that label)
3. Create separate folders based on the labels you used in your model
4. Copy every image to the labeled folder after classifying

For Mac users: before you run this script:

1. Create a folder called 'predictions': this is where the newly created/updated CSV-file goes.
2. make sure you have deleted the ".DS_Store" file in the 'imgs' folder You can do this by running this command in your Terminal: rm .DS_Store

You need this import in order to read the images from the folder AND to create the folders to add the labeled images to

import os 

to create the CSV file

import csv 

used for copying images to the designated predicted label-folders

import shutil 

this is the folder you have to create, where your testset images go you can change 'imgs' to any name you desire

path1 = "imgs"   

to sort the files into their label-destination folders

def copyFilesToDestLabelFolders(sourceFile, destination):
    # Copy file to another directory
    newPath = shutil.copy(sourceFile, destination)
    print("Path of copied file : ", newPath)

define the name of the directory to be created

def makeLabelDirs(whichLabel):
    print("whichLabel: " + whichLabel)
    realLabel = whichLabel.strip()
    try:
	os.mkdir(realLabel)
    except OSError:
	print ("Creation of the directory %s failed" % realLabel)
    else:
	print ("Successfully created the directory %s " % realLabel)

Every Lobe-project generates a 'labels.txt' file. You can read the txt file and create folders corresponding to the labelnames in that txt-file

def sortImagesToLabeledFolder():
    lines = []
    labelnames = []
    with open('labels.txt', encoding='utf8') as l:
	lines = l.readlines()

    count = 0
    for line in lines:
	count += 1
	#print(f'line {count}: {line}')
	labelnames.append(line)
	makeLabelDirs(line)

sortImagesToLabeledFolder()

the model you've trained using Lobe.ai and exported choosing 'TensorFlow, Use your model in a Python app'

from lobe import ImageModel #the model you've trained using Lobe.ai and exported choosing 'TensorFlow, Use your model in a Python app'

model = ImageModel.load('')

def createPredictionsCSV(theoutcome):

	#'a' is for append
	with open('predictions/predictions.csv', 'a') as file:
		writer = csv.writer(file)
		writer.writerow(theoutcome);

for opening the folder containing your images

# for opening the folder containing your images
listing = os.listdir(path1)  

for pics in listing:
    im = path1 + '/' + pics
    result = model.predict_from_file(im)

    predList = [pics, im, result.prediction]

    for label, confidence in result.labels:
	labelScores = f"{label}: {confidence*100}%"
	print(f"{label}: {confidence*100}%")
	labelName = (f"{label}")
	labelScore = (f"{confidence*100}")

	# Since I'm fairly new to Python, I haven't found a way to add all scores for each label to an image.
	# This 'if' statement  writes the scores pnly for the predicted label 
	# it copies the sourcefiles from the original source-folder ('imgs')  to the newly created  & predicted label-named folder:
	# e.g. all images predicted as "label1" are copied from the source folder into the newly created folder "label1"
	if label is result.prediction:
	    print("im" + im)
	    copyFilesToDestLabelFolders(im, label)
	    predList = [pics, im, result.prediction, labelScore]
	    createPredictionsCSV(predList)