main.py

import yolov5

# load model
model = yolov5.load('keremberke/yolov5m-garbage')

# set model parameters
model.conf = 0.25  # NMS confidence threshold
model.iou = 0.45  # NMS IoU threshold
model.agnostic = False  # NMS class-agnostic
model.multi_label = False  # NMS multiple labels per box
model.max_det = 1000  # maximum number of detections per image

# set image
img = 'sample.jpg'

# perform inference
results = model(img, size=640)

# inference with test time augmentation
results = model(img, augment=True)

# parse results
predictions = results.pred[0]
boxes = predictions[:, :4]  # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]

# show detection bounding boxes on image
results.show()

print(results)  # results output image 1/1: 720x1280 2 biodegradables, 1 paper
 
sample =  str(results).split()


# print(type(results))

# regex to match the particular things 

ct =0 
for words in sample:
    if(words=='paper'  or words =='plastic' or words =='rubber'):
        ct+=1
    


if(ct >5):
    print("Image contains garbage")
else:
    print("Image does not contain garbage")

    

# save results into "results/" folder
# results.save(save_dir='results/')