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V22 #2

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ee90681
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fixed0301 Dec 13, 2023
f485817
Merge remote-tracking branch 'origin/v2' into v22
fixed0301 Dec 13, 2023
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178 changes: 178 additions & 0 deletions multiperson/asdf.ipynb
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{
"cells": [
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"SEQUENCE_1 = 'GCCAAGACAGTATCAGACAC' #Sequence 1 (Side Sequence)\n",
"SEQUENCE_2 = 'TGTCATGCCAAGACAGTATG' #Sequence 2 (Top Sequence)\n",
"MATRIX_ROW_N = len(SEQUENCE_1)+1 #Initiation Matrix Size (Rows)\n",
"MATRIX_COLUMN_N = len(SEQUENCE_2)+1 #Initiation Matrix Size (Columns)\n",
"MATCH_SCORE = 5 #Match Score\n",
"MISMATCH_SCORE = -3 #Mismatch Score\n",
"GAP_SCORE = -5 #Gap Points\n",
"GAP_CHARACTER = '-'#Character to Represent Gaps in Final Alignemnts\n",
"ALN_PATHWAYS = [] #Initiating List of Discovered aln Pathways\n",
"MATRIX = [[[[None] for i in range(2)] for i in range(MATRIX_COLUMN_N)] for i in range(MATRIX_ROW_N)] # Initiating Score Matrix\n",
"\n",
"def print_aln_details(aln): # Function to print steps of particular aln\n",
" print('\\n>>>>>>>>>>>>>>>> Aln #:'+str(aln[4])+' <<<<<<<<<<<<<<<<\\n\\n------------------Steps------------------\\n')\n",
" for elem in aln[3]:\n",
" print('Step: '+str(elem[0])+' -> Score = ' + str(elem[1]) + ' -> Dir: ' + ('\\u2190' if elem[2]==1 else ('\\u2196' if elem[2]==2 else ('\\u2191' if elem[2]==3 else 'Error'))))\n",
" print('\\n-------------Final aln-------------\\n')\n",
" print(aln[1]+'\\n'+aln[0])\n",
" return\n",
"\n",
"def print_all(ALIGNMENTS): #Function to print everything\n",
" print('Total Alignments: ' + str(len(ALIGNMENTS)))\n",
" print('Overall Score: '+str(ALIGNMENTS[0][3][0][1])+'\\n')\n",
" for elem in ALIGNMENTS:\n",
" print_aln_details(elem)\n",
" return\n",
"def print_alns_only(ALIGNMENTS): #Function to print only ALIGNMENTS\n",
" print('Total Alignments: ' + str(len(ALIGNMENTS)))\n",
" print('Overall Score: '+str(ALIGNMENTS[0][3][0][1])+'\\n')\n",
" for elem in ALIGNMENTS:\n",
" print(elem[0]+'\\n'+elem[1]+'\\n')\n",
" return\n",
"def find_each_path(c_i,c_j,path=''): #Nested function to discover new aln pathways\n",
" global ALN_PATHWAYS\n",
" i = c_i\n",
" j = c_j\n",
" if i == 0 and j==0:\n",
" ALN_PATHWAYS.append(path)\n",
" return 2\n",
" dir_t = len(MATRIX[i][j][1])\n",
" while dir_t<=1:\n",
" n_dir = MATRIX[i][j][1][0] if (i != 0 and j != 0) else (1 if i == 0 else (3 if j==0 else 0))\n",
" path = path + str(n_dir)\n",
" if n_dir == 1:\n",
" j=j-1\n",
" elif n_dir == 2:\n",
" i=i-1\n",
" j=j-1\n",
" elif n_dir == 3:\n",
" i=i-1\n",
" dir_t = len(MATRIX[i][j][1])\n",
" if i == 0 and j==0:\n",
" ALN_PATHWAYS.append(path)\n",
" return 3\n",
" if dir_t>1:\n",
" for dir_c in range(dir_t):\n",
" n_dir = MATRIX[i][j][1][dir_c] if (i != 0 and j != 0) else (1 if i == 0 else (3 if j==0 else 0))\n",
" tmp_path = path + str(n_dir)\n",
" if n_dir == 1:\n",
" n_i = i\n",
" n_j=j-1\n",
" elif n_dir == 2:\n",
" n_i=i-1\n",
" n_j=j-1\n",
" elif n_dir == 3:\n",
" n_i=i-1\n",
" n_j = j\n",
" find_each_path(n_i,n_j,tmp_path)\n",
" return len(ALN_PATHWAYS)\n",
"\n",
"#Main Code\n",
"# 배열 초기화\n",
"for i in range(MATRIX_ROW_N):\n",
" MATRIX[i][0] = [GAP_SCORE*i,[]]\n",
"for j in range(MATRIX_COLUMN_N):\n",
" MATRIX[0][j] = [GAP_SCORE*j,[]]\n",
"for i in range(1,MATRIX_ROW_N):\n",
" for j in range(1,MATRIX_COLUMN_N):\n",
" score = MATCH_SCORE if (SEQUENCE_1[i-1] == SEQUENCE_2[j-1]) else MISMATCH_SCORE # 일치하면 +8, 다르면 -5\n",
" h_val = MATRIX[i][j-1][0] + GAP_SCORE # 가로로 이동\n",
" d_val = MATRIX[i-1][j-1][0] + score # 대각선으로 이동\n",
" v_val = MATRIX[i-1][j][0] + GAP_SCORE # 세로로 이동\n",
" o_val = [h_val, d_val, v_val]\n",
" MATRIX[i][j] = [max(o_val), [i+1 for i,v in enumerate(o_val) if v==max(o_val)]] # 이들 중 최댓값 점수들과 이동 가능한 방향의 리스트 저장. 예시로 o_val = [10, 5, 10] 이면 MATRIX[i][j] = [10, [1, 3]]\n",
"\n",
"score = MATRIX[i][j][0] # 마지막 위치에서의 점수 = 76\n",
"\n",
"l_i = i\n",
"l_j = j\n",
"ALIGNMENTS = []\n",
"tot_aln = find_each_path(i,j) # 함수 실행\n",
"aln_count = 0\n",
"\n",
"for elem in ALN_PATHWAYS: # ['33332332222222222121112121', '33332332222222222211112121', '33332332222222222221111121', '33332332222222222222111111'] <- elems\n",
" i = l_i-1\n",
" j = l_j-1\n",
" side_aln = '' # 정렬된 서열\n",
" top_aln = '' # 기준 서열\n",
" step = 0\n",
" aln_info = []\n",
" for n_dir_c in range(len(elem)):\n",
" n_dir = elem[n_dir_c]\n",
" score = MATRIX[i+1][j+1][0]\n",
" step = step + 1\n",
" aln_info.append([step,score,n_dir]) # [[1, 76, '3'], [2, 76, '3'], [3, 76, '3'], [4, 76, '3'], [5, 76, '2']...\n",
" if n_dir == '2':\n",
" side_aln = side_aln + SEQUENCE_1[i]\n",
" top_aln = top_aln + SEQUENCE_2[j]\n",
" i=i-1\n",
" j=j-1\n",
" elif n_dir == '1':\n",
" side_aln = side_aln + GAP_CHARACTER\n",
" top_aln = top_aln + SEQUENCE_2[j]\n",
" j=j-1\n",
" elif n_dir == '3':\n",
" side_aln = side_aln + SEQUENCE_1[i]\n",
" top_aln = top_aln + GAP_CHARACTER\n",
" i=i-1\n",
" aln_count = aln_count + 1 # 정렬 결과 개수 증가\n",
" ALIGNMENTS.append([top_aln[::-1],side_aln[::-1],elem,aln_info,aln_count]) # 서열 역순 바꿔 저장\n",
"\n",
"print_alns_only(ALIGNMENTS)"
],
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91 changes: 91 additions & 0 deletions multiperson/main.py
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import tensorflow as tf
import tensorflow_hub as hub
import cv2
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd

EDGES = {
(0, 1): 'm',
(0, 2): 'c',
(1, 3): 'm',
(2, 4): 'c',
(0, 5): 'm',
(0, 6): 'c',
(5, 7): 'm',
(7, 9): 'm',
(6, 8): 'c',
(8, 10): 'c',
(5, 6): 'y',
(5, 11): 'm',
(6, 12): 'c',
(11, 12): 'y',
(11, 13): 'm',
(13, 15): 'm',
(12, 14): 'c',
(14, 16): 'c'
}

def draw_connections(frame, keypoints, edges, confidence_threshold):
y, x, c = frame.shape
shaped = np.squeeze(np.multiply(keypoints, [y,x,1]))

for edge, color in edges.items():
p1, p2 = edge
y1, x1, c1 = shaped[p1]
y2, x2, c2 = shaped[p2]
if (c1 > confidence_threshold) & (c2 > confidence_threshold):
cv2.line(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0,0,255), 4)

def draw_keypoints(frame, keypoints, confidence_threshold):
y, x, c = frame.shape
shaped = np.squeeze(np.multiply(keypoints, [y, x, 1]))
posx = []
posy = []
for kp in shaped:
ky, kx, kp_conf = kp
if kp_conf > confidence_threshold:
posx.append(int(kx))
posy.append(int(ky))
# cv2.circle(frame, (int(kx), int(ky)), 6, (0, 255, 0), -1)

# Draw bounding box if keypoints are detected
if len(posx) > 0 and len(posy) > 0:
max_x, max_y = max(posx), max(posy)
min_x, min_y = min(posx), min(posy)

# Replace lines with cv2.rectangle
cv2.rectangle(frame, (min_x, min_y), (max_x, max_y), (0, 0, 255), 4)
# return (min_x, min_y), (max_x, max_y)

# Function to loop through each person detected and render
def loop_through_people(frame, keypoints_with_scores, edges, confidence_threshold):
for person in keypoints_with_scores:
#draw_connections(frame, person, edges, confidence_threshold)
draw_keypoints(frame, person, confidence_threshold)

model = hub.load('https://tfhub.dev/google/movenet/multipose/lightning/1')
movenet = model.signatures['serving_default']

cap = cv2.VideoCapture('novak.mp4')
while cap.isOpened():
ret, frame = cap.read()

# Resize image
img = frame.copy()
img = tf.image.resize_with_pad(tf.expand_dims(img, axis=0), 384,640)
input_img = tf.cast(img, dtype=tf.int32)

# Detection section
results = movenet(input_img)
keypoints_with_scores = results['output_0'].numpy()[:,:,:51].reshape((6,17,3))

# Render keypoints
loop_through_people(frame, keypoints_with_scores, EDGES, 0.4)

cv2.imshow('Movenet Multipose', frame)

if cv2.waitKey(10) & 0xFF==ord('q'):
break
cap.release()
cv2.destroyAllWindows()
63 changes: 63 additions & 0 deletions region/hsv.named.py
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import cv2
import numpy as np

# 마우스 클릭
def mouse_callback(event, x, y, flags, param):
global clicked_point, selected_area_name
if event == cv2.EVENT_LBUTTONDOWN:
clicked_point = (x, y)
selected_area_name = input("영역 이름 입력: ")
process_clicked_area()

# 클릭한 좌표와 유사한 색상을 추출 & 연속된 픽셀을 묶는 함수
def process_clicked_area():
if clicked_point is not None:
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
clicked_hsv_color = hsv_image[clicked_point[1], clicked_point[0]]

color_range = 15 # 색상 범위
lower_bound = np.array([clicked_hsv_color[0] - color_range, 50, 50])
upper_bound = np.array([clicked_hsv_color[0] + color_range, 255, 255])

mask = cv2.inRange(hsv_image, lower_bound, upper_bound)
result = cv2.bitwise_and(image, image, mask=mask)

# 연속된 픽셀을 하나로 묶음
gray_result = cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)
_, binary = cv2.threshold(gray_result, 1, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

clicked_cluster = None # 클릭한 픽셀이 포함된 무리 찾기
for contour in contours:
if cv2.pointPolygonTest(contour, clicked_point, False) >= 0:
clicked_cluster = contour
break

if clicked_cluster is not None: # 영역 잘라내기
x, y, w, h = cv2.boundingRect(clicked_cluster)
clicked_area = image[y:y + h, x:x + w]

saved_areas[selected_area_name] = {'coordinates': (x, y, w, h)} # 추출한 영역을 임의로 정한 제목으로 저장 및 좌표 저장

cv2.imshow('Clicked Area - ' + selected_area_name, clicked_area)

#초기 입력값
clicked_point = None
selected_area_name = ""
image = cv2.imread('test1.jpg')
saved_areas = {}

cv2.namedWindow('Image')
cv2.setMouseCallback('Image', mouse_callback)

while True:
cv2.imshow('Image', image)

key = cv2.waitKey(1) & 0xFF
if key == ord('a'): #창 닫기
break
cv2.destroyAllWindows()

# 저장된 영역과 좌표 출력
for area_name, area_info in saved_areas.items():
print(f"영역명: {area_name}, 좌표: {area_info['coordinates']}")