extract text from the --save-crop images yolov5

[bachimanchiajay]

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Question

after executing the detect.py file --save-crop i can able to crop the class images but i need the text as well before image writing.
for eg : i tested with one image having text model predicted the text classes. i want to extract the text in the predicted coordinates before copping the class images

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[github-actions]

👋 Hello @bachimanchiajay, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[glenn-jocher]

@bachimanchiajay 👋 Hello! Thanks for asking about cropping results with YOLOv5 🚀. Cropping bounding box detections can be useful for training classification models on box contents for example. This feature was added in PR #2827. You can crop detections using either detect.py or YOLOv5 PyTorch Hub:

detect.py

Crops will be saved under runs/detect/exp/crops, with a directory for each class detected.

python detect.py --save-crop

YOLOv5 PyTorch Hub

Crops will be saved under runs/detect/exp/crops if save=True, and also returned as a dictionary with crops as numpy arrays.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, custom

# Images
img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list

# Inference
results = model(img)

# Results
crops = results.crop(save=True) 
# -- or --
crops = results.crop(save=True, save_dir='runs/detect/exp')  # specify save dir

Good luck 🍀 and let us know if you have any other questions!

[bachimanchiajay]

i want to extract the text from the predicted results, have to pass the predicted bounding box coordinates to the textract can you help me in that

[bachimanchiajay]

import cv2
import numpy as np
import os
import pytesseract as pt
import torch
from PIL import Image
import matplotlib.pyplot as plt
model = torch.hub.load('/home/pkumar/yolov5/', 'custom', path='/home/pkumar/Desktop/yolo_dataentry_models/best.pt',source='local')
def get_text(image_path, threshold):
img = cv2.imread(image_path)
Y, X,d = img.shape
result = model(img)
df = result.pandas().xyxy[0]
df = df[df['confidence']>=threshold]
df = df.drop(['class', 'name','confidence'], axis=1)
print(df.head())
output = []
i = 0
values = df.values.astype(int)
for xmin,ymin,xmax,ymax in df.values.astype(int):
xmin = max(0, xmin-3)
ymin = max(0, ymin-3)
xmax = min(X, xmax+3)
ymax = min(Y, ymax+3)
cv2.rectangle(img, (xmin,ymin),(xmax,ymax),(0,255,0),3)
crop_img = img[ymin:ymax, xmin:xmax]
crop_img = cv2.cvtColor(crop_img, cv2.COLOR_BGR2GRAY)
crop_img = cv2.resize(crop_img, None, fx=4,fy=4, interpolation=cv2.INTER_CUBIC)
text = pt.image_to_string(crop_img).strip()
output.append(text)
return output,img
path = '/home/pkumar/Downloads/UP_images/SAO1_0deblur.png'
text,image= get_text(path, 0.5)`

[bachimanchiajay]

This one is not extracting all the classes only getting two class values @glenn-jocher can you cross check the above method once

[glenn-jocher]

@bachimanchiajay I'm sorry we don't availability to debug custom code. If you find any issues with the official codebase please let us know though.

[Aayan37]

@glenn-jocher please can you let me know the code for YoloV5 with bounding boxes cropped, saved in some location. I have tried integrating method #2827! but its not working.. can you please share the latest code.

[glenn-jocher]

👋 Hello! Thanks for asking about cropping results with YOLOv5 🚀. Cropping bounding box detections can be useful for training classification models on box contents for example. This feature was added in PR #2827. You can crop detections using either detect.py or YOLOv5 PyTorch Hub:

detect.py

Crops will be saved under runs/detect/exp/crops, with a directory for each class detected.

python detect.py --save-crop

YOLOv5 PyTorch Hub

Crops will be saved under runs/detect/exp/crops if save=True, and also returned as a dictionary with crops as numpy arrays.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, custom

# Images
img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list

# Inference
results = model(img)

# Results
crops = results.crop(save=True) 
# -- or --
crops = results.crop(save=True, save_dir='runs/detect/exp')  # specify save dir

Good luck 🍀 and let us know if you have any other questions!

[Aayan37]

@glenn-jocher I have tried this but its not cropping the bounding boxes. can you please share the complete detect.py with cropping images code present in it. I have tried Abdurrahman, @burhr2 defined codes as well. May be I'm missing something, can you please share the detailed code for that. Thanks in Advance!!

[glenn-jocher]

@Aayan37 👋 hi, thanks for letting us know about this possible problem with YOLOv5 🚀. We've created a few short guidelines below to help users provide what we need in order to start investigating a possible problem.

How to create a Minimal, Reproducible Example

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• ✅ Current – Verify that your code is up-to-date with GitHub master, and if necessary git pull or git clone a new copy to ensure your problem has not already been solved in master.
• ✅ Unmodified – Your problem must be reproducible using official YOLOv5 code without changes. Ultralytics does not provide support for custom code ⚠️.

If you believe your problem meets all the above criteria, please close this issue and raise a new one using the 🐛 Bug Report template with a minimum reproducible example to help us better understand and diagnose your problem.

Thank you! 😃

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

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