initial_commit

musetalk/utils/preprocessing.py

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+import sys
+from face_detection import FaceAlignment,LandmarksType
+from os import listdir, path
+import subprocess
+import numpy as np
+import cv2
+import pickle
+import os
+import json
+from mmpose.apis import inference_topdown, init_model
+from mmpose.structures import merge_data_samples
+import torch
+from tqdm import tqdm
+
+# initialize the mmpose model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+config_file = './musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py'
+checkpoint_file = './models/dwpose/dw-ll_ucoco_384.pth'
+model = init_model(config_file, checkpoint_file, device=device)
+
+# initialize the face detection model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+fa = FaceAlignment(LandmarksType._2D, flip_input=False,device=device)
+
+# maker if the bbox is not sufficient 
+coord_placeholder = (0.0,0.0,0.0,0.0)
+
+def resize_landmark(landmark, w, h, new_w, new_h):
+    w_ratio = new_w / w
+    h_ratio = new_h / h
+    landmark_norm = landmark / [w, h]
+    landmark_resized = landmark_norm * [new_w, new_h]
+    return landmark_resized
+
+def read_imgs(img_list):
+    frames = []
+    print('reading images...')
+    for img_path in tqdm(img_list):
+        frame = cv2.imread(img_path)
+        frames.append(frame)
+    return frames
+
+def get_landmark_and_bbox(img_list,upperbondrange =0):
+    frames = read_imgs(img_list)
+    batch_size_fa = 1
+    batches = [frames[i:i + batch_size_fa] for i in range(0, len(frames), batch_size_fa)]
+    coords_list = []
+    landmarks = []
+    if upperbondrange != 0:
+        print('get key_landmark and face bounding boxes with the bbox_shift:',upperbondrange)
+    else:
+        print('get key_landmark and face bounding boxes with the default value')
+    average_range_minus = []
+    average_range_plus = []
+    for fb in tqdm(batches):
+        results = inference_topdown(model, np.asarray(fb)[0])
+        results = merge_data_samples(results)
+        keypoints = results.pred_instances.keypoints
+        face_land_mark= keypoints[0][23:91]
+        face_land_mark = face_land_mark.astype(np.int32)
+        
+        # get bounding boxes by face detetion
+        bbox = fa.get_detections_for_batch(np.asarray(fb))
+        
+        # adjust the bounding box refer to landmark
+        # Add the bounding box to a tuple and append it to the coordinates list
+        for j, f in enumerate(bbox):
+            if f is None: # no face in the image
+                coords_list += [coord_placeholder]
+                continue
+            
+            half_face_coord =  face_land_mark[29]#np.mean([face_land_mark[28], face_land_mark[29]], axis=0)
+            range_minus = (face_land_mark[30]- face_land_mark[29])[1]
+            range_plus = (face_land_mark[29]- face_land_mark[28])[1]
+            average_range_minus.append(range_minus)
+            average_range_plus.append(range_plus)
+            if upperbondrange != 0:
+                half_face_coord[1] = upperbondrange+half_face_coord[1] #手动调整  + 向下（偏29）  - 向上（偏28）
+            half_face_dist = np.max(face_land_mark[:,1]) - half_face_coord[1]
+            upper_bond = half_face_coord[1]-half_face_dist
+            
+            f_landmark = (np.min(face_land_mark[:, 0]),int(upper_bond),np.max(face_land_mark[:, 0]),np.max(face_land_mark[:,1]))
+            x1, y1, x2, y2 = f_landmark
+            
+            if y2-y1<=0 or x2-x1<=0 or x1<0: # if the landmark bbox is not suitable, reuse the bbox
+                coords_list += [f]
+                w,h = f[2]-f[0], f[3]-f[1]
+                print("error bbox:",f)
+            else:
+                coords_list += [f_landmark]
+    
+    print("********************************************bbox_shift parameter adjustment**********************************************************")
+    print(f"Total frame:「{len(frames)}」 Manually adjust range : [ -{int(sum(average_range_minus) / len(average_range_minus))}~{int(sum(average_range_plus) / len(average_range_plus))} ] , the current value: {upperbondrange}")
+    print("*************************************************************************************************************************************")
+    return coords_list,frames
+    
+
+if __name__ == "__main__":
+    img_list = ["./results/lyria/00000.png","./results/lyria/00001.png","./results/lyria/00002.png","./results/lyria/00003.png"]
+    crop_coord_path = "./coord_face.pkl"
+    coords_list,full_frames = get_landmark_and_bbox(img_list)
+    with open(crop_coord_path, 'wb') as f:
+        pickle.dump(coords_list, f)
+        
+    for bbox, frame in zip(coords_list,full_frames):
+        if bbox == coord_placeholder:
+            continue
+        x1, y1, x2, y2 = bbox
+        crop_frame = frame[y1:y2, x1:x2]
+        print('Cropped shape', crop_frame.shape)
+        
+        #cv2.imwrite(path.join(save_dir, '{}.png'.format(i)),full_frames[i][0][y1:y2, x1:x2])
+    print(coords_list)