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sgoldenlab · Dec 20, 2024 · 7f3cf06 · 7f3cf06
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diff --git a/docs/nb/Untitled2.ipynb b/docs/nb/Untitled2.ipynb
@@ -0,0 +1,23 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0cecda1-8604-44a4-9b9c-8e8bade04950",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "",
+   "name": ""
+  },
+  "language_info": {
+   "name": ""
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/simba/sandbox/CLAHE.py b/simba/sandbox/CLAHE.py
@@ -0,0 +1,89 @@
+import os
+from datetime import datetime
+import threading
+from tkinter import *
+from tkinter import Button
+from simba.utils.enums import Keys, Links, Options
+from simba.mixins.pop_up_mixin import PopUpMixin
+from simba.ui.tkinter_functions import CreateLabelFrameWithIcon, FileSelect, FolderSelect
+from simba.utils.checks import check_file_exist_and_readable, check_if_dir_exists
+from simba.video_processors.clahe_ui import interactive_clahe_ui
+from simba.video_processors.video_processing import clahe_enhance_video
+from simba.utils.read_write import find_files_of_filetypes_in_directory, get_fn_ext
+from simba.utils.printing import stdout_success, SimbaTimer
+
+class CLAHEPopUp(PopUpMixin):
+    def __init__(self):
+        super().__init__(title="CLAHE VIDEO CONVERSION")
+        single_video_frm = CreateLabelFrameWithIcon(parent=self.main_frm, header="SINGLE VIDEO - Contrast Limited Adaptive Histogram Equalization", icon_name=Keys.DOCUMENTATION.value, icon_link=Links.VIDEO_TOOLS.value)
+        self.selected_video = FileSelect(single_video_frm, "VIDEO PATH:", title="Select a video file", file_types=[("VIDEO", Options.ALL_VIDEO_FORMAT_STR_OPTIONS.value)])
+        run_single_video_btn = Button(single_video_frm, text="Apply CLAHE", command=lambda: self.run_single_video())
+
+        multiple_videos_frm = CreateLabelFrameWithIcon(parent=self.main_frm, header="MULTIPLE VIDEOs - Contrast Limited Adaptive Histogram Equalization", icon_name=Keys.DOCUMENTATION.value, icon_link=Links.VIDEO_TOOLS.value)
+        self.selected_dir = FolderSelect(multiple_videos_frm, "VIDEO DIRECTORY PATH:", lblwidth=25)
+        run_multiple_btn = Button(multiple_videos_frm, text="RUN VIDEO DIRECTORY", command=lambda: self.run_directory(), fg="blue")
+
+        single_video_frm.grid(row=0, column=0, sticky=NW)
+        self.selected_video.grid(row=0, column=0, sticky=NW)
+        run_single_video_btn.grid(row=1, column=0, sticky=NW)
+
+        multiple_videos_frm.grid(row=1, column=0, sticky=NW)
+        self.selected_dir.grid(row=0, column=0, sticky=NW)
+        run_multiple_btn.grid(row=1, column=0, sticky=NW)
+
+    def run_single_video(self):
+        selected_video = self.selected_video.file_path
+        check_file_exist_and_readable(file_path=selected_video)
+        threading.Thread(target=clahe_enhance_video(file_path=selected_video)).start()
+
+    def run_directory(self):
+        timer = SimbaTimer(start=True)
+        video_dir = self.selected_dir.folder_path
+        check_if_dir_exists(in_dir=video_dir, source=self.__class__.__name__)
+        self.video_paths = find_files_of_filetypes_in_directory(directory=video_dir, extensions=Options.ALL_VIDEO_FORMAT_OPTIONS.value, raise_error=True)
+        for file_path in self.video_paths:
+            threading.Thread(target=clahe_enhance_video(file_path=file_path)).start()
+        timer.stop_timer()
+        stdout_success(msg=f'CLAHE enhanced {len(self.video_paths)} video(s)', elapsed_time=timer.elapsed_time_str)
+
+
+
+
+
+
+
+
+
+
+
+# # Function to update CLAHE
+# def update_clahe(x):
+#     global img, clahe
+#     clip_limit = cv2.getTrackbarPos('Clip Limit', 'CLAHE') / 10.0  # Scale the trackbar value
+#     tile_size = cv2.getTrackbarPos('Tile Size', 'CLAHE')
+#     if tile_size % 2 == 0:
+#         tile_size += 1  # Ensure tile size is odd
+#     clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=(tile_size, tile_size))
+#     img_clahe = clahe.apply(img)
+#     cv2.imshow('CLAHE', img_clahe)
+#
+# # Load an image
+# img = cv2.imread('/Users/simon/Downloads/PXL_20240429_222923838.jpg', cv2.IMREAD_GRAYSCALE)
+#
+# # Create a window
+# cv2.namedWindow('CLAHE', cv2.WINDOW_NORMAL)
+#
+# # Initialize the clip limit trackbar
+# cv2.createTrackbar('Clip Limit', 'CLAHE', 10, 300, update_clahe)
+#
+# # Initialize the tile size trackbar
+# cv2.createTrackbar('Tile Size', 'CLAHE', 8, 64, update_clahe)
+#
+# # Apply CLAHE with initial parameters
+# clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(8, 8))
+# img_clahe = clahe.apply(img)
+# cv2.imshow('Original', img)
+# cv2.imshow('CLAHE', img_clahe)
+#
+# cv2.waitKey(0)
+# cv2.destroyAllWindows()
diff --git a/simba/sandbox/Geometry_7.py b/simba/sandbox/Geometry_7.py
@@ -0,0 +1,99 @@
+import os
+import numpy as np
+
+from simba.mixins.config_reader import ConfigReader
+from simba.mixins.geometry_mixin import GeometryMixin
+from simba.mixins.image_mixin import ImageMixin
+from simba.utils.read_write import read_df
+from simba.plotting.geometry_plotter import GeometryPlotter
+
+CONFIG_PATH = '/Users/simon/Desktop/envs/simba/troubleshooting/RAT_NOR/project_folder/project_config.ini'
+VIDEO_NAME = '2022-06-20_NOB_DOT_4'
+
+cfg = ConfigReader(config_path=CONFIG_PATH, read_video_info=False)
+data = read_df(os.path.join(cfg.outlier_corrected_dir, VIDEO_NAME + f'.{cfg.file_type}'), file_type=cfg.file_type)
+video_path = os.path.join(cfg.video_dir, VIDEO_NAME + '.mp4')
+animal_df = data[[x for x in data.columns if x in cfg.animal_bp_dict['Animal_1']['X_bps'] + cfg.animal_bp_dict['Animal_1']['Y_bps']]]
+animal_data = animal_df.drop(['Tail_end_x', 'Tail_end_y'], axis=1).values.reshape(-1, 7, 2).astype(np.int)
+
+animal_polygons = GeometryMixin.bodyparts_to_polygon(data=animal_data)[:300]
+geometry_plotter = GeometryPlotter(config_path=CONFIG_PATH,
+                                   geometries=[animal_polygons],
+                                   video_name=VIDEO_NAME,
+                                   thickness=10,
+                                   bg_opacity=0.2)
+geometry_plotter.run()
+
+#
+# animal_rectangles = GeometryMixin().multiframe_minimum_rotated_rectangle(shapes=animal_polygons)
+# geometry_plotter = GeometryPlotter(config_path=CONFIG_PATH,
+#                                    geometries=[animal_rectangles],
+#                                    video_name=VIDEO_NAME,
+#                                    thickness=10)
+# geometry_plotter.run()
+#
+# animal_polygons_buffered = GeometryMixin.bodyparts_to_polygon(data=animal_data[:300], parallel_offset=100, pixels_per_mm=1.88)
+# geometry_plotter = GeometryPlotter(config_path=CONFIG_PATH,
+#                                    geometries=[animal_polygons],
+#                                    video_name=VIDEO_NAME,
+#                                    thickness=10,
+#                                    bg_opacity=1.0)
+# geometry_plotter.run()
+
+imgs = ImageMixin().read_img_batch_from_video(video_path=video_path, start_frm=0, end_frm=299)
+imgs = np.stack(list(imgs.values()))
+# animal_polygons_buffered = np.array(animal_polygons_buffered).T.reshape(-1, 1)
+# sliced_images = ImageMixin().slice_shapes_in_imgs(imgs=imgs, shapes=animal_polygons_buffered)
+
+# ImageMixin.img_stack_to_video(imgs=sliced_images,
+#                               save_path='/Users/simon/Desktop/envs/simba/troubleshooting/RAT_NOR/project_folder/frames/output/geometry_visualization/sliced.mp4',
+#                               fps=30,
+#                               verbose=True)
+
+# animal_polygons_tighter = np.array(animal_polygons).T.reshape(-1, 1)
+# sliced_images = ImageMixin().slice_shapes_in_imgs(imgs=imgs, shapes=animal_polygons_tighter)
+# ImageMixin.img_stack_to_video(imgs=sliced_images,
+#                               save_path='/Users/simon/Desktop/envs/simba/troubleshooting/RAT_NOR/project_folder/frames/output/geometry_visualization/sliced_tighter.mp4',
+#                               fps=30,
+#                               verbose=True)
+
+animal_head = animal_df[['Nose_x', 'Nose_y', 'Ear_left_x', 'Ear_left_y', 'Ear_right_x', 'Ear_right_y']].values.reshape(-1, 3, 2).astype(np.int)
+#animal_head_polygons = GeometryMixin.bodyparts_to_polygon(data=animal_head, parallel_offset=100, pixels_per_mm=1.88)[:300]
+# animal_head_polygons = np.array(animal_head_polygons).T.reshape(-1, 1)
+# sliced_images = ImageMixin().slice_shapes_in_imgs(imgs=imgs, shapes=animal_head_polygons)
+# ImageMixin.img_stack_to_video(imgs=sliced_images,
+#                               save_path='/Users/simon/Desktop/envs/simba/troubleshooting/RAT_NOR/project_folder/frames/output/geometry_visualization/sliced_head.mp4',
+#                               fps=30,
+#                               verbose=True)
+
+# animal_head_polygons = GeometryMixin.bodyparts_to_polygon(data=animal_head, parallel_offset=25, pixels_per_mm=1.88)[:300]
+# animal_head_polygons = np.array(animal_head_polygons).T.reshape(-1, 1)
+# sliced_images = ImageMixin().slice_shapes_in_imgs(imgs=imgs, shapes=animal_head_polygons)
+# ImageMixin.img_stack_to_video(imgs=sliced_images,
+#                               save_path='/Users/simon/Desktop/envs/simba/troubleshooting/RAT_NOR/project_folder/frames/output/geometry_visualization/sliced_head_tighter_even.mp4',
+#                               fps=30,
+#                               verbose=True)
+
+
+
+# animal_head_polygons = GeometryMixin.bodyparts_to_polygon(data=animal_head)[:300]
+# head_centers = GeometryMixin.get_center(shape=animal_head_polygons)
+# head_circles = GeometryMixin.bodyparts_to_circle(data=head_centers, parallel_offset=100, pixels_per_mm=1.88)
+# head_circles = np.array(head_circles).T.reshape(-1, 1)
+# sliced_images = ImageMixin().slice_shapes_in_imgs(imgs=imgs, shapes=head_circles)
+# ImageMixin.img_stack_to_video(imgs=sliced_images,
+#                               save_path='/Users/simon/Desktop/envs/simba/troubleshooting/RAT_NOR/project_folder/frames/output/geometry_visualization/sliced_head_circles.mp4',
+#                               fps=30,
+#                               verbose=True)
+
+
+# head_circles = GeometryMixin.bodyparts_to_circle(data=head_centers, parallel_offset=50, pixels_per_mm=1.88)
+# head_circles = np.array(head_circles).T.reshape(-1, 1)
+# sliced_images = ImageMixin().slice_shapes_in_imgs(imgs=imgs, shapes=head_circles)
+# ImageMixin.img_stack_to_video(imgs=sliced_images,
+#                               save_path='/Users/simon/Desktop/envs/simba/troubleshooting/RAT_NOR/project_folder/frames/output/geometry_visualization/sliced_head_circles_tighter.mp4',
+#                               fps=30,
+#                               verbose=True)
+
+
+#video_bg_substraction_mp(video_path=video_path, save_path='/Users/simon/Desktop/envs/simba/troubleshooting/RAT_NOR/project_folder/frames/output/geometry_visualization/bg_removed.mp4')