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pose.py
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pose.py
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import cv2
import sys
import math
import torch
import threading
import numpy as np
import tkinter as tk
import mediapipe as mp
import matplotlib.pyplot as plt
from mediapipe import solutions
from tkinter.simpledialog import askstring
from tkinter.filedialog import askopenfilename
from mediapipe.framework.formats import landmark_pb2
from segment_anything import sam_model_registry, SamPredictor
from scan import *
from merge import *
body_coord = [
"NOSE",
"RIGHT_EYE",
"LEFT_EYE",
"RIGHT_EAR",
"LEFT_EAR",
"RIGHT_SHOULDER",
"LEFT_SHOULDER",
"RIGHT_ELBOW",
"LEFT_ELBOW",
"RIGHT_WRIST",
"LEFT_WRIST",
"RIGHT_HIP",
"LEFT_HIP",
"RIGHT_KNEE",
"LEFT_KNEE",
"RIGHT_ANKLE",
"LEFT_ANKLE",
"RIGHT_HEEL",
"LEFT_HEEL",
]
res = []
# p = "./"
def input_from_bro(p):
mp_pose = mp.solutions.pose
mp_holistic = mp.solutions.holistic
mp_drawing = mp.solutions.drawing_utils
def get_body_coord(str, image_width, image_height, results):
return np.array([results.pose_landmarks.landmark[mp_holistic.PoseLandmark[str]].x * image_width,
results.pose_landmarks.landmark[mp_holistic.PoseLandmark[str]].y * image_height])
with mp_pose.Pose(
static_image_mode=True, min_detection_confidence=0.5) as pose:
print(p)
image = cv2.imread(p)
image_height, image_width, _ = image.shape
# Convert the BGR image to RGB before processing.
results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
print(
f'Nose coordinates: ('
f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.RIGHT_SHOULDER].x * image_width}, '
f'{results.pose_landmarks.landmark[mp_holistic.PoseLandmark.NOSE].y * image_height})'
)
for e in body_coord:
res.append(get_body_coord(e, image_width, image_height, results))
# Draw pose landmarks on the image.
annotated_image = image.copy()
# Use mp_pose.UPPER_BODY_POSE_CONNECTIONS for drawing below when
# upper_body_only is set to True.
mp_drawing.draw_landmarks(
annotated_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
cv2.imwrite('./clothes.png', annotated_image)
## model implementation
print(type(res))
def distance_calculator(arr, coord):
# arr: two dimension array contain tuples (start, end)
res = []
for p1, p2 in arr:
diff = coord[p1] - coord[p2]
distance = np.sqrt(np.sum(np.square(diff)))
res.append(distance)
return res
# Calculating angle opposite to egde b
def angle(a,b,c):
'''
@param:
- a: edge that opposite to the angle that we looking for
- b, c: edges that beside the angle that we looking for
'''
angle_rad = math.acos((b**2 + c**2 - a**2) / (2 * b * c))
return angle_rad
# Comment out when needed
# angle_deg = math.degrees(angle_rad)
# return angle_deg
def make_angles(coords):
'''
A function that generate angles between joints
@param:
@return:
an array that contains the following angles
- left-shoulder
- left-elbow
- right-shoulder
- right-elbow
- left-hip
- right-hip
'''
# get first person
person = coords
# get points for angle calculation
points = [[(6, 7), (5, 7), (5, 6)] ,
[(5, 8), (5, 6), (6, 8)] ,
[(5, 9), (5, 7), (7, 9)] ,
[(6, 10), (6, 8), (8, 10)] ,
[(12, 13), (11, 12), (11, 13)],
[(11, 14), (11, 12), (12, 14)]]
keys = ["left_shoulder" ,
"right_shoulder",
"left_elbow" ,
"right_elbow" ,
"left_hip" ,
"right_hip"]
# angle calculation
res = {}
for i, e in enumerate(points):
ans = angle(*distance_calculator(e, person))
res[keys[i]] = ans
return res
# Calculating length of joints of the person (dictionary version)
'''
- pred_coords variable contains joints of many people.
- It has the dimension of 3d where
+ The first dimension denote number of people appear in that image
+ The second dimension denote all of the joints that person has
+ The third dimension denote each joint of that person
'''
'''
@Params:
- coord: all informations about people(s) joints
@Returns:
- dictionary containing desirable data
'''
def joints_length(coords):
# access to the first person in the image
person = coords
'''
0. left shoulder -> left elbow
1. left elbow -> left wrist
2. right shoulder -> right elbow
3. right elbow -> right wrist
4. left shoulder -> right shoulder
5. left shoulder -> left hip
6. right shoulder -> right hip
7. left hip -> right hip
8. left hip -> left knee
9. right hip -> right knee
10. left knee -> left ankle
11. right knee -> right ankle
'''
points = [(5, 7), (7, 9), (6, 8), (8, 10), (5, 6), (5, 11), (6, 12), (11, 12), (11, 13), (12, 14), (13, 15), (14, 16)]
dist = distance_calculator(points, person)
# debugging purpose
distance_ref = [
"left_shoulder-left_elbow", #0
"left_elbow-left_wrist", #1
"right_shoulder-right_elbow", #2
"right_elbow-right_wrist", #3
"left_shoulder-right_shoulder", #4
"left_shoulder-left_hip", #5
"right_shoulder-right_hip", #6
"left_hip-right_hip", #7
"left_hip-left_knee", #8
"right_hip-right_knee", #9
"left_knee-left_ankle", #10
"right_knee-right_ankle"] #11
res = {}
for i, e in enumerate(dist):
res[distance_ref[i]] = e
return res
def model(coords):
y_low = min(coords[17][1], coords[18][1])
y_high = coords[0][1]
height = abs(y_high - y_low) * 108.5 / 100
return {
"height": lambda: height,
"lengths": lambda: joints_length(coords),
"angles": lambda: make_angles(coords)
}
## Crop image
sys.path.append("..")
# load model
sam_checkpoint = "sam_vit_h_4b8939.pth"
model_type = "vit_h"
if (torch.cuda.is_available()):
device = "cuda"
else:
device = "cpu"
def show_mask(mask, ax, random_color=False):
if random_color:
color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
else:
color = np.array([30/255, 144/255, 255/255, 0.6])
h, w = mask.shape[-2:]
mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
ax.imshow(mask_image)
def show_points(coords, labels, ax, marker_size=375):
pos_points = coords[labels==1]
neg_points = coords[labels==0]
ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
def show_box(box, ax):
x0, y0 = box[0], box[1]
w, h = box[2] - box[0], box[3] - box[1]
ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))
# MODEL
def model_SAM():
global p
root = tk.Tk()
root.withdraw() # Hide the main window
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
sam.to(device=device)
predictor = SamPredictor(sam)
def body_detect(**args):
cols = args["image"].shape[0]
rows = args["image"].shape[1]
input_point = np.array([[rows / 2, cols * 0.4], [rows / 2, cols / 2], [rows / 2, cols * 0.65]])
input_label = np.array([1, 1, 1])
mask_input = args["logits"][np.argmax(args["scores"]), :, :] # Choose the model's best mask
masks, _, _ = predictor.predict(
point_coords=input_point,
point_labels=input_label,
mask_input=mask_input[None, :, :],
multimask_output=False,
)
masks.shape
plt.figure(figsize=(10,10))
plt.imshow(args["image"])
show_mask(masks, plt.gca())
show_points(input_point, input_label, plt.gca())
plt.axis('off')
plt.show()
for x in range(0, cols - 1):
for y in range(0, rows - 1):
if args["mask"][x, y] == False:
args["image"][x, y] = (127, 255, 0)
plt.imshow(args["image"])
plt.axis('off')
fname = f'./clothes/{args["name"]}_{"front" if args["j"] == 0 else "back"}_body.jpg'
print(fname)
plt.savefig(fname)
def detect(input_point, input_label, **args):
image = cv2.imread(args["uploaded"])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
mask_input = args["logits"][np.argmax(args["scores"]), :, :] # Choose the model's best mask
masks, _, _ = predictor.predict(
point_coords=input_point,
point_labels=input_label,
mask_input=mask_input[None, :, :],
multimask_output=False,
)
masks.shape
plt.figure(figsize=(10,10))
plt.imshow(image)
show_mask(masks, plt.gca())
show_points(input_point, input_label, plt.gca())
plt.axis('off')
plt.show()
cols = image.shape[0]
rows = image.shape[1]
mask = masks[0]
for x in range(0, cols - 1):
for y in range(0, rows - 1):
if mask[x, y] == False:
image[x, y] = (127, 255, 0)
plt.imshow(image)
plt.axis('off')
fname = f'./clothes/{args["name"]}_{"front" if args["j"] == 0 else "back"}_{"shirt" if len(input_point) == 4 else "jeans"}.jpg'
print(fname)
plt.savefig(fname)
# Use a simple dialog to get input instead of input()
name = askstring("Input", "Please enter your name:")
if name == None:
print("No input was entered")
return None
for j in range(0, 2):
uploaded = askopenfilename()
if j == 0:
p = "./Input_IMG/" + name + ".jpg"
cv2.imwrite(p, cv2.imread(uploaded))
print(p)
image = cv2.imread(uploaded)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(10,10))
plt.imshow(image)
plt.axis('on')
plt.show()
predictor.set_image(image)
height = image.shape[0]
width = image.shape[1]
input_point = np.array([[width / 2, height / 2]])
input_label = np.array([1])
plt.figure(figsize=(10,10))
plt.imshow(image)
show_points(input_point, input_label, plt.gca())
plt.axis('on')
plt.show()
# From here!
masks, scores, logits = predictor.predict(
point_coords=input_point,
point_labels=input_label,
multimask_output=True,
)
masks.shape # (number_of_masks) x H x W
for i, (mask, score) in enumerate(zip(masks, scores)):
plt.figure(figsize=(10,10))
plt.imshow(image)
show_mask(mask, plt.gca())
show_points(input_point, input_label, plt.gca())
plt.title(f"Mask {i+1}, Score: {score:.3f}", fontsize=18)
plt.axis('off')
plt.show()
# Parameters
dic = {
"j": j,
"uploaded": uploaded,
"image": image,
"masks": masks,
"mask": mask,
"scores": scores,
"logits": logits,
"name": name
}
"""BODY DETECT"""
body_detect(**dic)
"""SHIRT DETECT"""
height = image.shape[0]
width = image.shape[1]
input_point = np.array([[width / 2, height * 0.4], [width / 2, height / 2], [width * 0.45 , height * 0.75], [width * 0.45, height * 0.65]])
input_label = np.array([1, 1, 0, 0])
detect(input_point, input_label, **dic)
"""JEANS DETECT"""
height = image.shape[0]
width = image.shape[1]
input_point = np.array([[width * 0.45, height * 0.65], [width * 0.45, height * 0.7], [width * 0.55, height * 0.65], [width * 0.55, height * 0.7], [width / 2, height / 2], [width / 2, height * 0.4]])
input_label = np.array([1, 1, 1, 1, 0, 0])
detect(input_point, input_label, **dic)
return name
# model_SAM()
# input_from_bro(p)
# res = model(res)
# Sample for retrieving elements:
# res["height"]()
# print(res["lengths"]())
# print(res["angles"]())
# scale_data = res["lengths"]()
# angle_data = res["angles"]()
# export_path = "./models/export.obj"
# texture_path = "./textures/bao.jpg"
# convert2dto3d(texture_path, scale_data, angle_data, export_path)
# run(export_path, 1)