.

Result_yolo3_fault_4/.gitignore

@@ -0,0 +1 @@
+result_otros/

predict_yolo3.py

@@ -16,18 +16,19 @@ import numpy as np
 
 
 def _main_(args):
+    
     config_path  = args.conf
     input_path   = args.input
     output_path  = args.output
 
-    with open(config_path) as config_buffer:    
+    with open(config_path) as config_buffer:
         config = json.load(config_buffer)
 
     makedirs(output_path)
 
     ###############################
     #   Set some parameter
-    ###############################       
+    ###############################
     net_h, net_w = 416, 416 # a multiple of 32, the smaller the faster
     obj_thresh, nms_thresh = 0.5, 0.45
 
@@ -38,7 +39,7 @@ def _main_(args):
     infer_model = load_model(config['train']['saved_weights_name'])
 
     ###############################
-    #   Predict bounding boxes 
+    #   Predict bounding boxes
     ###############################
     if 'webcam' in input_path: # do detection on the first webcam
         video_reader = cv2.VideoCapture(0)
@@ -54,13 +55,13 @@ def _main_(args):
                 batch_boxes = get_yolo_boxes(infer_model, images, net_h, net_w, config['model']['anchors'], obj_thresh, nms_thresh)
 
                 for i in range(len(images)):
-                    draw_boxes(images[i], batch_boxes[i], config['model']['labels'], obj_thresh) 
+                    draw_boxes(images[i], batch_boxes[i], config['model']['labels'], obj_thresh)
                     cv2.imshow('video with bboxes', images[i])
                 images = []
-            if cv2.waitKey(1) == 27: 
+            if cv2.waitKey(1) == 27:
                 break  # esc to quit
-        cv2.destroyAllWindows()        
-    elif input_path[-4:] == '.mp4': # do detection on a video  
+        cv2.destroyAllWindows()
+    elif input_path[-4:] == '.mp4': # do detection on a video
         video_out = output_path + input_path.split('/')[-1]
         video_reader = cv2.VideoCapture(input_path)
 
@@ -69,8 +70,8 @@ def _main_(args):
         frame_w = int(video_reader.get(cv2.CAP_PROP_FRAME_WIDTH))
 
         video_writer = cv2.VideoWriter(video_out,
-                               cv2.VideoWriter_fourcc(*'MPEG'), 
-                               50.0, 
+                               cv2.VideoWriter_fourcc(*'MPEG'),
+                               50.0,
                                (frame_w, frame_h))
         # the main loop
         batch_size  = 1
@@ -89,27 +90,27 @@ def _main_(args):
 
                     for i in range(len(images)):
                         # draw bounding boxes on the image using labels
-                        draw_boxes(images[i], batch_boxes[i], config['model']['labels'], obj_thresh)   
+                        draw_boxes(images[i], batch_boxes[i], config['model']['labels'], obj_thresh)
 
-                        # show the video with detection bounding boxes          
-                        if show_window: cv2.imshow('video with bboxes', images[i])  
+                        # show the video with detection bounding boxes
+                        if show_window: cv2.imshow('video with bboxes', images[i])
 
                         # write result to the output video
-                        video_writer.write(images[i]) 
+                        video_writer.write(images[i])
                     images = []
 
                 if show_window and cv2.waitKey(1) == 27: break  # esc to quit
 
         if show_window: cv2.destroyAllWindows()
         video_reader.release()
-        video_writer.release()       
+        video_writer.release()
     else: # do detection on an image or a set of images
 
 
 
         image_paths = []
 
-        if os.path.isdir(input_path): 
+        if os.path.isdir(input_path):
             for inp_file in os.listdir(input_path):
                 image_paths += [input_path + inp_file]
         else:
@@ -129,20 +130,20 @@ def _main_(args):
             print('Elapsed time = {}'.format(time.time() - start))
             times.append(time.time() - start)
             # draw bounding boxes on the image using labels
-            draw_boxes(image, boxes, config['model']['labels'], obj_thresh) 
-     
+            draw_boxes(image, boxes, config['model']['labels'], obj_thresh)
+
             # write the image with bounding boxes to file
-            cv2.imwrite(output_path + image_path.split('/')[-1], np.uint8(image))    
-     
+            cv2.imwrite(output_path + image_path.split('/')[-1], np.uint8(image))
+
         file = open(args.output + '/time.txt','w')
         file.write('Tiempo promedio:' + str(np.mean(times)))
-        file.close() 
+        file.close()
 
 if __name__ == '__main__':
     argparser = argparse.ArgumentParser(description='Predict with a trained yolo model')
     argparser.add_argument('-c', '--conf', help='path to configuration file')
-    argparser.add_argument('-i', '--input', help='path to an image, a directory of images, a video, or webcam')    
-    argparser.add_argument('-o', '--output', default='output/', help='path to output directory')   
-    
+    argparser.add_argument('-i', '--input', help='path to an image, a directory of images, a video, or webcam')
+    argparser.add_argument('-o', '--output', default='output/', help='path to output directory')
+
     args = argparser.parse_args()
     _main_(args)