Renamed to main_process_mating.py

process_mating.py → main_process_mating.py

-import os, time
-from core.images_processing import ImagesProcessing
-
-#import deeplabcut
-
-start = time.time()
-
-xyz_path = os.path.join(os.getcwd(), 'calib_files/xyz_calibration_device_small.csv')
-#xy_path = os.path.join(os.getcwd(), 'calib_files/20200615_xypts-rot.csv')
-xy_path = os.path.join(os.getcwd(), 'calib_files/20200615_xypts.csv')
-dlt_path = os.path.join(os.getcwd(), 'calib_files/20200615_DLTcoefs-py.csv')
-dlc_cfg_path = '/home/user/Desktop/Antoine/_DLC/config.yaml'
-
-img_size = (896, 896)
-
-# Generate dlt coefficients
-#gen_dlt(3, img_size, xyz_path, xy_path, dlt_path)
-
-img_process = ImagesProcessing(3, dlt_path)
-img_process.cam_paths = {1: '/media/user/MosquitoLanding_Photron3_Backup/Photron1/_MatingKinematics',
-                         2: '/media/user/MosquitoLanding_Photron3_Backup/Photron2/_MatingKinematics',
-                         3: '/media/user/MosquitoLanding_Photron3_Backup/Photron3/_MatingKinematics'}
-img_process.cam_save_paths = {1: '/media/user/MosquitoLanding_Photron3_Backup/Photron1/_MatingKinematics/_Process',
-                              2: '/media/user/MosquitoLanding_Photron3_Backup/Photron2/_MatingKinematics/_Process',
-                              3: '/media/user/MosquitoLanding_Photron3_Backup/Photron3/_MatingKinematics/_Process'}
-
-img_process.update_leading_zero = False
-
-img_process.init_paths_names()
-img_process.max_diff_date_s = 90
-
-img_process.show_plot = False
-
-# # Fill dict with name of recordings to process
-# # > either all recordings from a given date
-# date_to_process = '20200303'
-# rec_names_to_process = {}
-# for camn in range(1, img_process.nb_cam + 1):
-#     rec_names_to_process[camn] = [s for s in img_process.all_folders_rec_cam[camn] if date_to_process in s]
-
-# > all recordings
-rec_names_to_process = {}
-for camn in range(1, img_process.nb_cam + 1):
-    rec_names_to_process[camn] = img_process.all_folders_rec_cam[camn]
-
-
-# # Keep only rec_names that haven't been processed yet
-# non_processed_rec_names = {1: [], 2: [], 3: []}
-# for camn in range(1, img_process.nb_cam + 1):
-#     for i, rec_name in enumerate(rec_names_to_process[camn]):
-#         if not os.path.exists(os.path.join(img_process.cam_save_paths[camn], '_Avi', rec_name)):
-#             non_processed_rec_names[camn].append(rec_names_to_process[camn][i])
-#
-# rec_names_to_process = non_processed_rec_names
-
-
-# -----------------------------------------------------------------------------------------------------------------------
-# Will prepare recordings to be used by Deeplabcut
-
-# > do everything automatically use 'multi_processes'
-# Do cropping, rotating, stitching, etc at once and save as .avi (save much time and space)
-img_process.do_batch('multi_processes', rec_names=rec_names_to_process, yaml_path='processes.yaml', delete_previous=True)
-
-# > OR do each step separately
-# Convert image to 8 bits and reduce framerate (save in cam_save_paths/_Sample)
-# img_process.do_batch('sample', rec_names=rec_names_to_process, step_frame=50, delete_previous=True)
-#
-# # Do 2d tracking (blob detection) on images in cam_save_paths/_Sample
-# img_process.do_batch('track2d', rec_names=rec_names_to_process, from_fn_name='sample')
-#
-# # Do 3d reconstruction of tracks
-# img_process.do_batch('recon3d', rec_names=rec_names_to_process, from_fn_name='sample')
-#
-# # Crop all frames and save in cam_save_paths/_Cropped
-# img_process.do_batch('crop', rec_names=rec_names_to_process, from_fn_name='sample', height_crop=200, width_crop=200, delete_previous=True)
-#
-# # Rotate view 2 (to always have piston coming from right side)
-# img_process.do_batch('rotate', rec_names=rec_names_to_process, from_fn_name='crop', camns=[2], degrees=270)
-#
-# # Stitch all views together and save in cam_save_paths/_Stitched
-# img_process.do_batch('stitch', rec_names=rec_names_to_process, from_fn_name='crop', delete_previous=True)
-
-
-# -----------------------------------------------------------------------------------------------------------------------
-# Run Deeplabcut analysis + Fit 3D skeleton to 2D coordinate from Deeplabcut
-
-dlc_cfg_path = '/home/user/Desktop/Antoine/_maDLC/config.yaml'
-
-shuffle = 31
-iteration = 70000
-model_name = 'DLC_resnet50_MatingKinematicsshuffle{0}_{1}'.format(shuffle, iteration)
-
-res_method = '3d'  # '2d', '3d' or '2d_geo'
-opt_method = 'leastsq'  # 'powell' or 'leastsq' or 'least_squares'
-body_param_names = ['yaw_a', 'pitch_a', 'roll_a', 'x_com', 'y_com', 'z_com']
-wing_param_names = ['stroke_a', 'deviation_a', 'rotation_a']
-
-
-# # Track features using DeepLabCut
-# img_process.do_batch('analyse_dlc', rec_names=rec_names_to_process, from_fn_name='save_avi', cfg_path=dlc_cfg_path,
-#                     shuffle=shuffle, trainingsetindex=0, batchsize=5, save_avi=False, model_name=model_name, delete_previous=True)
-
-# # Load (+ filtering and unscrambling) 2d coords from DLC + Reverse processes (unstitch, rotate back, uncrop) + Reconstruct 3d coord
-# img_process.do_batch('load_dlc', rec_names=rec_names_to_process, from_fn_name='analyse_dlc', model_name=model_name,
-#                       tracker_method='skeleton')
-
-# # Optimize fit of skeleton to find body and wings angles
-# img_process.multiprocessing = True
-# img_process.threshold_likelihood = 0.85
-# img_process.do_batch('fit_skeleton', rec_names=rec_names_to_process, from_fn_name='load_dlc', model_name=model_name,
-#                      csv_path=csv_wings_geo_path, body_param_names=body_param_names, wing_param_names=wing_param_names,
-#                      res_method=res_method, opt_method=opt_method)
-
+import os, time
+from core.images_processing import ImagesProcessing
+
+#import deeplabcut
+
+start = time.time()
+
+xyz_path = os.path.join(os.getcwd(), 'calib_files/xyz_calibration_device_small.csv')
+#xy_path = os.path.join(os.getcwd(), 'calib_files/20200615_xypts-rot.csv')
+xy_path = os.path.join(os.getcwd(), 'calib_files/20200615_xypts.csv')
+dlt_path = os.path.join(os.getcwd(), 'calib_files/20200615_DLTcoefs-py.csv')
+dlc_cfg_path = '/home/user/Desktop/Antoine/_DLC/config.yaml'
+
+img_size = (896, 896)
+
+# Generate dlt coefficients
+#gen_dlt(3, img_size, xyz_path, xy_path, dlt_path)
+
+img_process = ImagesProcessing(3, dlt_path)
+img_process.cam_paths = {1: '/media/user/MosquitoLanding_Photron3_Backup/Photron1/_MatingKinematics',
+                         2: '/media/user/MosquitoLanding_Photron3_Backup/Photron2/_MatingKinematics',
+                         3: '/media/user/MosquitoLanding_Photron3_Backup/Photron3/_MatingKinematics'}
+img_process.cam_save_paths = {1: '/media/user/MosquitoLanding_Photron3_Backup/Photron1/_MatingKinematics/_Process',
+                              2: '/media/user/MosquitoLanding_Photron3_Backup/Photron2/_MatingKinematics/_Process',
+                              3: '/media/user/MosquitoLanding_Photron3_Backup/Photron3/_MatingKinematics/_Process'}
+
+img_process.update_leading_zero = False
+
+img_process.init_paths_names()
+img_process.max_diff_date_s = 90
+
+img_process.show_plot = False
+
+# # Fill dict with name of recordings to process
+# # > either all recordings from a given date
+# date_to_process = '20200303'
+# rec_names_to_process = {}
+# for camn in range(1, img_process.nb_cam + 1):
+#     rec_names_to_process[camn] = [s for s in img_process.all_folders_rec_cam[camn] if date_to_process in s]
+
+# > all recordings
+rec_names_to_process = {}
+for camn in range(1, img_process.nb_cam + 1):
+    rec_names_to_process[camn] = img_process.all_folders_rec_cam[camn]
+
+
+# # Keep only rec_names that haven't been processed yet
+# non_processed_rec_names = {1: [], 2: [], 3: []}
+# for camn in range(1, img_process.nb_cam + 1):
+#     for i, rec_name in enumerate(rec_names_to_process[camn]):
+#         if not os.path.exists(os.path.join(img_process.cam_save_paths[camn], '_Avi', rec_name)):
+#             non_processed_rec_names[camn].append(rec_names_to_process[camn][i])
+#
+# rec_names_to_process = non_processed_rec_names
+
+
+# -----------------------------------------------------------------------------------------------------------------------
+# Will prepare recordings to be used by Deeplabcut
+
+# > do everything automatically use 'multi_processes'
+# Do cropping, rotating, stitching, etc at once and save as .avi (save much time and space)
+img_process.do_batch('multi_processes', rec_names=rec_names_to_process, yaml_path='processes.yaml', delete_previous=True)
+
+# > OR do each step separately
+# Convert image to 8 bits and reduce framerate (save in cam_save_paths/_Sample)
+# img_process.do_batch('sample', rec_names=rec_names_to_process, step_frame=50, delete_previous=True)
+#
+# # Do 2d tracking (blob detection) on images in cam_save_paths/_Sample
+# img_process.do_batch('track2d', rec_names=rec_names_to_process, from_fn_name='sample')
+#
+# # Do 3d reconstruction of tracks
+# img_process.do_batch('recon3d', rec_names=rec_names_to_process, from_fn_name='sample')
+#
+# # Crop all frames and save in cam_save_paths/_Cropped
+# img_process.do_batch('crop', rec_names=rec_names_to_process, from_fn_name='sample', height_crop=200, width_crop=200, delete_previous=True)
+#
+# # Rotate view 2 (to always have piston coming from right side)
+# img_process.do_batch('rotate', rec_names=rec_names_to_process, from_fn_name='crop', camns=[2], degrees=270)
+#
+# # Stitch all views together and save in cam_save_paths/_Stitched
+# img_process.do_batch('stitch', rec_names=rec_names_to_process, from_fn_name='crop', delete_previous=True)
+
+
+# -----------------------------------------------------------------------------------------------------------------------
+# Run Deeplabcut analysis + Fit 3D skeleton to 2D coordinate from Deeplabcut
+
+dlc_cfg_path = '/home/user/Desktop/Antoine/_maDLC/config.yaml'
+
+shuffle = 31
+iteration = 70000
+model_name = 'DLC_resnet50_MatingKinematicsshuffle{0}_{1}'.format(shuffle, iteration)
+
+res_method = '3d'  # '2d', '3d' or '2d_geo'
+opt_method = 'leastsq'  # 'powell' or 'leastsq' or 'least_squares'
+body_param_names = ['yaw_a', 'pitch_a', 'roll_a', 'x_com', 'y_com', 'z_com']
+wing_param_names = ['stroke_a', 'deviation_a', 'rotation_a']
+
+
+# # Track features using DeepLabCut
+# img_process.do_batch('analyse_dlc', rec_names=rec_names_to_process, from_fn_name='save_avi', cfg_path=dlc_cfg_path,
+#                     shuffle=shuffle, trainingsetindex=0, batchsize=5, save_avi=False, model_name=model_name, delete_previous=True)
+
+# # Load (+ filtering and unscrambling) 2d coords from DLC + Reverse processes (unstitch, rotate back, uncrop) + Reconstruct 3d coord
+# img_process.do_batch('load_dlc', rec_names=rec_names_to_process, from_fn_name='analyse_dlc', model_name=model_name,
+#                       tracker_method='skeleton')
+
+# # Optimize fit of skeleton to find body and wings angles
+# img_process.multiprocessing = True
+# img_process.threshold_likelihood = 0.85
+# img_process.do_batch('fit_skeleton', rec_names=rec_names_to_process, from_fn_name='load_dlc', model_name=model_name,
+#                      csv_path=csv_wings_geo_path, body_param_names=body_param_names, wing_param_names=wing_param_names,
+#                      res_method=res_method, opt_method=opt_method)
+
 print('> All processes as been processed (total time elapsed: {0:.4f} s)'.format(time.time() - start))
\ No newline at end of file