Updated the code that allows to save the cropped image in result_img directory

README.md

@@ -361,10 +361,12 @@ If you customize build with CMake GUI, darknet executable will be installed in y
     `./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -dont_show -ext_output < data/train.txt > result.txt`
 - To process a video and output results to a json file use: `darknet.exe detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights file.mp4 -dont_show -json_file_output results.json`
 - Pseudo-labelling - to process a list of images `data/new_train.txt` and save results of detection in Yolo training format for each image as label `<image_name>.txt` (in this way you can increase the amount of training data) use:
-    `./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt`
-- To calculate anchors: `./darknet detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416`
-- To check accuracy mAP@IoU=50: `./darknet detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights`
-- To check accuracy mAP@IoU=75: `./darknet detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75`
+    `darknet.exe detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 -dont_show -save_labels < data/new_train.txt`
+* To calculate anchors: `darknet.exe detector calc_anchors data/obj.data -num_of_clusters 9 -width 416 -height 416`
+* To check accuracy mAP@IoU=50: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights`
+* To check accuracy mAP@IoU=75: `darknet.exe detector map data/obj.data yolo-obj.cfg backup\yolo-obj_7000.weights -iou_thresh 0.75`
+* To save the cropped image of the object detected: [`uncomment the following lines of code`](https://github.com/AlexeyAB/darknet/blob/master/src/image_opencv.cpp#L980-L996)
+* To run inference on all the images in a folder: `./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights -i 0 -thresh 0.25 --folder_inference ./result_img/` here `./result_img/` is the path of the folder in which the inference has to be done. the folder name can be anything, as long as it exists. After the inference the images will be moved to `./processed_files/`. To change the folder name or path, the changes cane be done [`here`](https://github.com/AlexeyAB/darknet/blob/master/src/detector.c#L1894).
 
 ##### For using network video-camera mjpeg-stream with any Android smartphone
 

include/darknet.h

@@ -1054,7 +1054,7 @@ LIB_API float *network_predict_image_letterbox(network *net, image im);
 LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, const int map_points, int letter_box, network *existing_net);
 LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map, float thresh, float iou_thresh, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path, int mAP_epochs);
 LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
-    float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers);
+    float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers, char *folder_inference);
 LIB_API int network_width(network *net);
 LIB_API int network_height(network *net);
 LIB_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm);

src/darknet.c

@@ -490,7 +490,8 @@ int main(int argc, char **argv)
         float thresh = find_float_arg(argc, argv, "-thresh", .24);
         int ext_output = find_arg(argc, argv, "-ext_output");
         char *filename = (argc > 4) ? argv[4]: 0;
-        test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, ext_output, 0, NULL, 0, 0);
+        char *folder_inference = find_char_arg(argc, argv, "-folder_inference", 0);
+        test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, ext_output, 0, NULL, 0, 0, folder_inference);
     } else if (0 == strcmp(argv[1], "cifar")){
         run_cifar(argc, argv);
     } else if (0 == strcmp(argv[1], "go")){