Add style suffix

README.md

@@ -20,7 +20,7 @@ python text2pose.py --gloss "good morning afternoon" --scale-y 1.4 --scale-x 1.3
 
 or
 
-python text2pose.py --gloss "where is my cat" --scale-y 1.5 --scale-x 1.5 --npz-interpolation 20 --width 512 --height 512 --fps 30 --hide-torso-lines false
+python text2pose.py --gloss "where is my cat" --scale-y 1.5 --scale-x 1.5 --npz-interpolation 20 --width 512 --height 512 --fps 30 --hide-torso-lines false --draw-style openpose
 ```
 
 second, change `generated_pose_video/GOOD_MORNING_AFTERNOON_25073_36863_01435_20250804_160832.mp4` & `ref_img_dict/03.jpeg`, and run:

text2pose.py

@@ -30,10 +30,11 @@ OUTPUT_DIR = "generated_pose_video"  # Output video directory
 
 
 class SignLanguageQA:
-    def __init__(self):
+    def __init__(self, max_candidates=10):
         self.gloss_data = None
         self.load_json_mapping()
         self.detector = None  # Lazy load DWpose detector when needed
+        self.max_candidates = max_candidates
 
     def load_json_mapping(self):
         """Load JSON mapping file"""
@@ -760,12 +761,18 @@ class SignLanguageQA:
         
         for gloss in gloss_list:
             matched_videos = self.find_gloss_videos(gloss)
+            total_matches = len(matched_videos)
             
             if not matched_videos:
                 print(f"❌ '{gloss}' -> No matching Pose unit found")
                 continue
             
-            print(f"🎯 '{gloss}' -> Found {len(matched_videos)} Unit in Pose Dict")
+            if self.max_candidates and total_matches > self.max_candidates:
+                print(f"   ℹ️ Limiting candidates to first {self.max_candidates} (out of {total_matches}) for efficiency")
+                matched_videos = matched_videos[:self.max_candidates]
+            
+            print(f"🎯 '{gloss}' -> Found {total_matches} Unit in Pose Dict")
+            print(f"   🔍 Evaluating {len(matched_videos)} candidate(s)")
 
             # Evaluate all videos and select the best one based on quality and centering
             candidate_videos = []
@@ -1176,7 +1183,11 @@ class SignLanguageQA:
                 suffix = f"_{smoothing_method}" if smoothing_method != 'none' else ""
                 if npz_interpolation_frames > 0:
                     suffix = f"_npz{npz_interpolation_frames}"
-                output_file = Path(OUTPUT_DIR) / f"{gloss_str}_{timestamp}{suffix}.mp4"
+                
+                draw_style = 'controlnext'
+                print(f"🎨 Using {draw_style} drawing style")
+                style_suffix = f"_{draw_style}_style"
+                output_file = Path(OUTPUT_DIR) / f"{gloss_str}_{timestamp}{suffix}{style_suffix}.mp4"
                 
                 # Generate video
                 success = self.generate_video_from_glosses(
@@ -1184,7 +1195,8 @@ class SignLanguageQA:
                     smoothing_frames=smoothing_frames,
                     smoothing_method=smoothing_method,
                     npz_interpolation_frames=npz_interpolation_frames,
-                    hide_torso_lines=False
+                    hide_torso_lines=False,
+                    draw_style=draw_style
                 )
 
             except KeyboardInterrupt:
@@ -1227,10 +1239,10 @@ def main():
                         help='Smoothing frames (default: 5)')
     parser.add_argument('--npz-interpolation', type=int, default=0,
                         help='Number of NPZ interpolation frames between glosses (default: 0)')
-    parser.add_argument('--scale-x', type=float, default=1.0,
-                        help='Progressive X-axis scaling factor (default: 1.0)')
-    parser.add_argument('--scale-y', type=float, default=1.0,
-                        help='Progressive Y-axis scaling factor (default: 1.0)')
+    parser.add_argument('--scale-x', type=float, default=1.5,
+                        help='Progressive X-axis scaling factor (default: 1.5)')
+    parser.add_argument('--scale-y', type=float, default=1.5,
+                        help='Progressive Y-axis scaling factor (default: 1.5)')
     parser.add_argument('--hide-torso-lines', type=str, choices=['true', 'false'], default='false',
                         help='Hide torso lines from neck to hips (set to black). Choices: true/false (default: false)')
     parser.add_argument('--ref-image-path', type=str, default=None,
@@ -1239,11 +1251,13 @@ def main():
                         help='Drawing style for pose visualization. controlnext: advanced features with confidence-based coloring. openpose: simpler classic style. (default: controlnext)')
     parser.add_argument('--normalize-pose', type=str, choices=['true', 'false'], default='true',
                         help='Normalize pose data to standard coordinate system. This ensures different videos are in the same coordinate space, reducing inconsistency from different video sources. (default: true)')
+    parser.add_argument('--max-candidates', type=int, default=10,
+                        help='Maximum number of NPZ candidates evaluated per gloss (default: 10, set 0 for unlimited)')
 
     args = parser.parse_args()
 
     # Initialize QA system
-    qa_system = SignLanguageQA()
+    qa_system = SignLanguageQA(max_candidates=max(0, args.max_candidates))
 
     # If gloss parameter provided, process directly
     if args.gloss:
@@ -1262,7 +1276,8 @@ def main():
                 video_ids.append(videos[0])  # Take first video
         
         video_id_str = "_".join(video_ids) if video_ids else "novideo"
-        output_file = Path(OUTPUT_DIR) / f"{gloss_str}_{video_id_str}_{timestamp}{suffix}.mp4"
+        style_suffix = f"_{args.draw_style}_style"
+        output_file = Path(OUTPUT_DIR) / f"{gloss_str}_{video_id_str}_{timestamp}{suffix}{style_suffix}.mp4"
 
         if args.smoothing != 'none':
             print(f"🌊 Using {args.smoothing} smoothing ({args.smoothing_frames} frames)")