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utils.py
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import copy
import logging
import cv2
import gspread as gs
import imageio
import numpy as np
import pandas as pd
import mediapipe as mp
from numpy.core.numeric import count_nonzero
NUMBER_OF_SHEETS = 1
left_hand = ["LEFT_THUMB.x", "LEFT_INDEX.x", "LEFT_PINKY.x", "LEFT_WRIST.x", "LEFT_THUMB.y", "LEFT_INDEX.y", "LEFT_PINKY.y", "LEFT_WRIST.y", "LEFT_THUMB.v", "LEFT_INDEX.v", "LEFT_PINKY.v", "LEFT_WRIST.v"]
right_hand = ["RIGHT_THUMB.x", "RIGHT_INDEX.x", "RIGHT_PINKY.x", "RIGHT_WRIST.x", "RIGHT_THUMB.y", "RIGHT_INDEX.y", "RIGHT_PINKY.y", "RIGHT_WRIST.y", "RIGHT_THUMB.v", "RIGHT_INDEX.v", "RIGHT_PINKY.v", "RIGHT_WRIST.v"]
left_foot = ["LEFT_FOOT_INDEX.x", "LEFT_ANKLE.x", "LEFT_HEEL.x", "LEFT_FOOT_INDEX.y", "LEFT_ANKLE.y", "LEFT_HEEL.y", "LEFT_FOOT_INDEX.v", "LEFT_ANKLE.v", "LEFT_HEEL.v"]
right_foot = ["RIGHT_FOOT_INDEX.x", "RIGHT_ANKLE.x", "RIGHT_HEEL.x", "RIGHT_FOOT_INDEX.y", "RIGHT_ANKLE.y", "RIGHT_HEEL.y", "RIGHT_FOOT_INDEX.v", "RIGHT_ANKLE.v", "RIGHT_HEEL.v"]
extremities = {"left_hand" : left_hand, "right_hand": right_hand, "left_foot" : left_foot, "right_foot" : right_foot}
# Used for reading the excel sheet
def get_sheet(i, path=None):
# With google colab
if path is None:
gc = gs.authorize(GoogleCredentials.get_application_default())
worksheet = gc.open('boulder_problems').get_worksheet(i)
# get_all_values gives a list of rows.
rows = worksheet.get_all_values()
# Convert to a DataFrame and render.
df = pd.DataFrame.from_records(rows)
df.columns = df.iloc[0]
df = df[1:]
# Without google colab
else:
df = pd.read_excel(path, sheet_name=i)
df['Folder'] = df['Folder'].astype('string')
df['File'] = df['File'].astype('string')
df['Boulder'] = df['Boulder'].astype('string')
df['time_screenshot'] = df['time_screenshot'].astype('string')
df['time_start'] = df['time_start'].astype('string')
df['time_end'] = df['time_end'].astype('string')
return df
def run_all(func, args, table_path=None, n_sheet=1):
for i in range(n_sheet):
df = get_sheet(i, table_path)
func(df, args)
def coord(height, width, side_h, frac_h, side_w, frac_w):
frac_h = frac_h / 100
frac_w = frac_w / 100
left = 0
right = width - 1
top = 0
bottom = height - 1
if(side_h == 'center'):
top = round((1 - frac_h) * height / 2)
bottom = height - 1 - top
elif(side_h == 'top'):
bottom = height - 1 - round((1 - frac_h) * height)
elif(side_h == 'bottom'):
top = round((1 - frac_h) * height)
if(side_w == 'center'):
left = round((1 - frac_w) * width / 2)
right = width - 1 - left
elif(side_w == 'left'):
right = width - 1 - round((1 - frac_w) * width)
elif(side_w == 'right'):
left = round((1 - frac_w) * width)
return top, bottom, left, right
def crop_image(img, left, right, bottom, top):
return img[top:bottom, left:right]
def weighted_m(x, w):
sum_w = w.sum()
num = np.multiply(x,w).sum()
return (num/sum_w)
def create_gif_arrow(img, centers):
frames = []
timeframe = []
frames.append(img)
frame_width = img.shape[1]
frame_height = img.shape[0]
scaled_centers = copy.deepcopy(centers)
move_order = []
for key, coord in scaled_centers.items():
for elem in coord:
elem[0][0] *= frame_width
elem[0][1] *= frame_height
move_order.append((key, elem[1]))
move_order.sort(key=lambda y: y[1])
members = ['left_hand', 'right_hand', 'left_foot', 'right_foot']
colors = {'left_hand': (0, 204, 204), 'right_hand':(0, 204, 0), 'left_foot': (255, 102, 102), 'right_foot':(76, 0, 153)}
img = add_legend(img, colors, members)
size = 30
thickness = 4
img_base = img.copy()
count = dict(zip(members, [[0,0], [0,0], [0,0], [0,0]]))
for id, move in enumerate(move_order):
img = img_base.copy()
for member in members:
start = count[member][0]
end = count[member][1]
draw_last_moves(img, scaled_centers[member][start:end], colors[member])
member = move[0]
count[member][1] += 1 #add 1 to the end
start = count[member][0] #get start and end
end = count[member][1]
draw_last_moves(img, scaled_centers[member][end-1:end], colors[member])
if (count[member][1] > 1):
draw_arrow(img, scaled_centers[member][start:end], colors[member])
count[member][0] = count[member][1] - 1 #
frames.append(img)
timeframe.append(move[1])
return frames, timeframe
def add_legend(img, colors, members):
text_pos = [(20, 30), (20, 80), (20, 130), (20, 180)]
for i in range(4):
cv2.putText(img, members[i], text_pos[i], cv2.FONT_HERSHEY_SIMPLEX, 0.9, colors[members[i]], 3)
return img
def draw_arrow(img, last_move, color):
size = 30
thickness = 4
x1 = int(last_move[0][0][0])
y1 = int(last_move[0][0][1] - size)
x2 = int(last_move[1][0][0])
y2 = int(last_move[1][0][1] + size)
cv2.arrowedLine(img, (x1, y1), (x2, y2), color, thickness)
return img
def draw_last_moves(img, last_move, color):
size = 30
thickness = 4
for id, elem in enumerate(last_move):
x_bg = int(elem[0][0] - size)
y_bg = int(elem[0][1] - size)
x_ed = int(elem[0][0] + size)
y_ed = int(elem[0][1] + size)
cv2.rectangle(img, (x_bg, y_bg), (x_ed, y_ed), color, thickness)
return img
def save_gif(img, dict_centers, p_out):
frames, timeframe = create_gif_arrow(img, dict_centers)
timeframe_scaled = [timeframe[i] - timeframe[i-1] for i in range(1,len(timeframe))] #get number of frame btween two moves
timeframe_scaled = [timeframe_scaled[i] if timeframe_scaled[i] < 60 else 60 for i in range(len(timeframe_scaled))]#cap the maximum duration bteween two move to two seconds
timeframe_scaled.insert(0, 30) #add boulder image without any move, at the beginning, for 1 sec
timeframe_scaled.append(60) #stay on last image for 2 sec
timeframe_scaled = list(np.divide(timeframe_scaled,30)) #divide by 30 to have speed of 0.5 (original 60fps)
logging.debug(f"Saving GIF file for {p_out}")
with imageio.get_writer(p_out, mode="I", duration = timeframe_scaled) as writer:
for idx, frame in enumerate(frames):
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
writer.append_data(rgb_frame)
logging.debug(f"Adding frame {idx+1} to GIF for {p_out}: ")
def landmark_to_dict(mp_pose, results):
#To output landmark object to a dict to be then outputed as json file
dict_ = { "LEFT_FOOT_INDEX": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_FOOT_INDEX].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_FOOT_INDEX].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_FOOT_INDEX].visibility},
"LEFT_ANKLE": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ANKLE].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ANKLE].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ANKLE].visibility},
"LEFT_HEEL": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HEEL].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HEEL].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HEEL].visibility},
"RIGHT_FOOT_INDEX": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX].visibility},
"RIGHT_ANKLE": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ANKLE].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ANKLE].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ANKLE].visibility},
"RIGHT_HEEL": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HEEL].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HEEL].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HEEL].visibility},
"LEFT_THUMB": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_THUMB].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_THUMB].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_THUMB].visibility},
"LEFT_INDEX": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_INDEX].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_INDEX].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_INDEX].visibility},
"LEFT_PINKY": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_PINKY].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_PINKY].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_PINKY].visibility},
"LEFT_WRIST": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_WRIST].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_WRIST].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_WRIST].visibility},
"RIGHT_THUMB": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_THUMB].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_THUMB].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_THUMB].visibility},
"RIGHT_INDEX": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_INDEX].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_INDEX].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_INDEX].visibility},
"RIGHT_PINKY": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_PINKY].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_PINKY].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_PINKY].visibility},
"RIGHT_WRIST": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_WRIST].visibility},
"NOSE": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].visibility},
"RIGHT_ELBOW": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_ELBOW].visibility},
"LEFT_ELBOW": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ELBOW].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ELBOW].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_ELBOW].visibility},
"RIGHT_KNEE": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_KNEE].visibility},
"LEFT_KNEE": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_KNEE].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_KNEE].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_KNEE].visibility},
"RIGHT_HIP": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_HIP].visibility},
"LEFT_HIP": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_HIP].visibility},
"RIGHT_SHOULDER": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].visibility},
"LEFT_SHOULDER": {"x": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].x,
"y": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].y,
"v": results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].visibility},
}
return dict_