Reading and detecting images, and save in folder

Question

Firstly, I am simply reading images from the folder that contains different image formats. Secondly, the YOLO model detects the class, draws a rectangle and fills it with color only detected part, and saves it into another folder with the same name. Second Case, If the model didn't detect anything in an image then it will save the same image with the same name but in a different folder.

My codebase is stuck on the first image and never moves on to the second image. I have no idea what is the problem happening.

Code

import torch
import cv2
from matplotlib import pyplot as plt
from utils.plots import Annotator, colors, save_one_box
import os
import glob
# Load Ours Custom Model
model = torch.hub.load('.', 'custom', path='/media/bmvc/CM_1/yolov5/runs/train/exp4/weights/last.pt', source='local')
# Files extension
img_Extension = ['jpg', 'jpeg', 'png']
# Load all testing images
my_path = "/home/bmvc/Documents/hide_info_test_dataset/testing_images/"
# Save images into array
files = []
[files.extend(glob.glob(my_path + '*.' + e)) for e in img_Extension]
# Iteration on all images
images = [cv2.imread(file) for file in files]
total_images = 1
# Taking only image name to save with save name
image_file_name = ''
for file in files:
    for im in images:
        detections = model(im[..., ::-1])
        results = detections.pandas().xyxy[0].to_dict(orient="records")
        if len(results) == 0:
            cv2.imwrite(os.path.join("/home/bmvc/Documents/hide_info_test_dataset/detected/", file), im)
        else:
            for result in results:
                print(result['class'])
                con = result['confidence']
                cs = result['class']
                x1 = int(result['xmin'])
                y1 = int(result['ymin'])
                x2 = int(result['xmax'])
                y2 = int(result['ymax'])
                imagee = cv2.rectangle(im, (x1, y1), (x2, y2), (0, 255, 0), -1)
                cv2.imwrite(os.path.join("/home/bmvc/Documents/hide_info_test_dataset/detected/", file), im)
            total_images += 1

Answer 1

I have put a lot of loops that are completely useless for example reading different extension files, reading only images. I have improved the overall implementation and used only one loop to fix the above problem.

import torch
import cv2
from PIL import Image

from utils.plots import Annotator, colors, save_one_box
import os
import glob
import numpy as np
# Load Ours Custom Model
model = torch.hub.load('.', 'custom', path='/media/bmvc/CM_1/yolov5/runs/train/exp4/weights/last.pt', source='local')

# Files extension
img_Extension = ['jpg', 'jpeg', 'png']

# Load all testing images
my_path = "/home/bmvc/Documents/hide_info_test_dataset/testing_images/"

# Save images into array
files = []
[files.extend(glob.glob(my_path + '*.' + e)) for e in img_Extension]

# Iteration on all images
images = [cv2.imread(file) for file in files]

total_images = 1

# Taking only image name to save with save name
image_file_name = ''

for img in glob.glob(my_path + '*.*'):
    img_bgr_rgb = cv2.imread(img)
    file_Name = os.path.basename(img)
    detections = model(img_bgr_rgb[:, :, ::-1])
    results = detections.pandas().xyxy[0].to_dict(orient="records")
    if len(results) == 0:
        cv2.imwrite(os.path.join("/home/bmvc/Documents/hide_info_test_dataset/detected/", file_Name), img_bgr_rgb)
    else:
        for result in results:
            print(result['class'])
            con = result['confidence']
            cs = result['class']
            x1 = int(result['xmin'])
            y1 = int(result['ymin'])
            x2 = int(result['xmax'])
            y2 = int(result['ymax'])
            imagee = cv2.rectangle(img_bgr_rgb, (x1, y1), (x2, y2), (255, 87, 51), -1)
            cv2.imwrite(os.path.join("/home/bmvc/Documents/hide_info_test_dataset/detected/", file_Name), img_bgr_rgb)