vehicle_and_person_detection: A Python repository from posi2

YOLO project

Model darknet yolov3 416

directories:

Code:

project/vehicle_and_person_detection/code


for image :
            file: yolo_image.py

            command: python yolo_image.py 

            for save the detected image: python yolo_image.py --save True
           
            Dependent folder: code/input:- handlabel(40 images) , coco(validation2017) specific to vehicle and person

            Takes images from handlabel_images or coco_validation_images 
            save detected bounding box text files handlabel_detected-results or coco_detected-results
            save detected images detected-images

for video : 
            file: yolo_video.py

            command: python yolo_output.py --input <file-name> --output 

            for save the text (detected bounding) file separately python yolo_output.py --input <file-name> --output --save_file_separately

            for save the text (detected bounding) file python yolo_output.py --input <file-name> --output --save_file

            video save in out_video folder
            input video path : video (todo) presently code

for mAP :
           file: calculate_map.py
          
           command: python calculate_map.py

           folder requirement: coco_ground-truth, coco_detected-results , coco_validation_images(optional)
           result: results/<filename>

train :

 file : train.py
 
 command: pthon train.py

 Note: change weights, anchor and classes path in train.py as per the need

credit : https://github.com/qqwweee/keras-yolo3

posi2/vehicle_and_person_detection