mmpose/k-fold_change_person.py at main · ChinoEst/mmpose · GitHub

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import os
import pandas as pd
import json
import shutil
import cv2


class body_part:
    def __init__(self, x, y, v):
        self.x = x
        self.y = y
        self.v = v


def df_process(part):
    if "NAN" in part:
        return 0, 0, 0
    else:
        part = part.replace("'","")
        part = part.replace("(","")
        part = part.replace(")","")
        part_x, part_y = part.split(',')

        return int(float(part_x)), int(float(part_y)), 2


who = ""
"""
test_fold = os.listdir("data/test/images")

for test_img in test_fold:
    shutil.move(f"data/test/images/{test_img}", "data/train/images/{test_img}")
"""

df = pd.read_csv("label/label.csv")


image_train = []
annotation_train=[]


image_test = []
annotation_test = []

count = 0

img_fold = os.listdir("data/train/images")

for img in img_fold:
    n_jpg = img.split('.')[0]

    mode = "train"
    """
    if who in img_fold:
        shutil.move(f"data/train/images/{img}", "data/test/images/{img}")
        mode = "test"
    """
    target = df[df['file_name']== n_jpg]

    img2 = cv2.imread(f"data/{mode}/images/{img}")
    h, w, _ = img2.shape

    image_temp = {
        'file_name': f'{img}',
        'height': h,
        'width': w,
        'id': count
                  }

    if mode == "train":
        image_train.append(image_temp)
    else:
        image_test.append(image_temp)

    if "front" in img:
        front = True
    else:
        front = False

    with open(f'label/predictions/{n_jpg}.json', 'r') as file:
        json_data = json.load(file)

    j_data = json_data[0]
    keypoint_temp = []

    pose = img.split('_')[1]

    for i in range(17):
        for j in range(3):
            if j != 2:
                keypoint_temp.append(j_data["keypoints"][i][j])

            else:
                if (int(j_data["keypoints"][i][0])== 0) and (int(j_data["keypoints"][i][1])== 0):
                    keypoint_temp.append(0)
                elif not front:
                    if i in [2, 4, 6, 12]:
                        keypoint_temp.append(1)
                    else:
                        if (pose in ["1", "2", "3", "8"]) and (i in [14, 16]):
                            keypoint_temp.append(1)
                        elif (pose in ["5", "6", "9"] and i == 16):
                            keypoint_temp.append(1)
                        else:
                            keypoint_temp.append(2)
                else:
                    keypoint_temp.append(2)

    x, y, v = df_process(target['l_finger'].iloc[0])
    left_finger = body_part(x, y, v)
    x, y, v = df_process(target['r_finger'].iloc[0])
    right_finger = body_part(x, y, v)
    x, y, v = df_process(target['l_toe'].iloc[0])
    left_toe = body_part(x, y, v)
    x, y, v = df_process(target['r_toe'].iloc[0])
    right_toe = body_part(x, y, v)
    x, y, v = df_process(target['l_rib'].iloc[0])
    left_rib = body_part(x, y, v)
    x, y, v = df_process(target['r_rib'].iloc[0])
    right_rib = body_part(x, y, v)

    keypoint_temp.append(left_finger.x)
    keypoint_temp.append(left_finger.y)
    keypoint_temp.append(left_finger.v)
    keypoint_temp.append(right_finger.x)
    keypoint_temp.append(right_finger.y)
    keypoint_temp.append(right_finger.v)
    keypoint_temp.append(left_rib.x)
    keypoint_temp.append(left_rib.y)
    keypoint_temp.append(left_rib.v)
    keypoint_temp.append(right_rib.x)
    keypoint_temp.append(right_rib.y)
    keypoint_temp.append(right_rib.v)
    keypoint_temp.append(left_toe.x)
    keypoint_temp.append(left_toe.y)
    keypoint_temp.append(left_toe.v)
    keypoint_temp.append(right_toe.x)
    keypoint_temp.append(right_toe.y)
    keypoint_temp.append(right_toe.v)


    annotation_temp ={
        'keypoints': keypoint_temp,
        'num_keypoints': 23,
        'area': (j_data["bbox"][0][2]-j_data["bbox"][0][0])*(j_data["bbox"][0][3]*j_data["bbox"][0][1]),
        'iscrowed': 0,
        'image_id': count,
        'bbox': j_data["bbox"][0],
        'category_id': 0,
        'id': count
    }
    if mode == "train":
        annotation_train.append(annotation_temp)
    else:
        annotation_test.append(annotation_temp)

    count = count + 1
    print(f"{' ' * 30}\r{count}/{len(img_fold)}", end='\r', flush=True)


data_train = {'images': image_train,
        'annotations': annotation_train,
        'categories': [{'id': 1, 'name': 'person'}]}

data_test = {'images': image_test,
        'annotations': annotation_test,
        'categories': [{'id': 1, 'name': 'person'}]}


with open ("data/annotations/annotation_train.json", 'w') as json_file:
    json.dump(data_train, json_file)

with open ("data/annotations/annotation_test.json", 'w') as json_file:
    json.dump(data_test, json_file)