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refine function

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This commit is contained in:
Tang1705
2020-07-06 17:05:50 +08:00
parent 80bb3c7f70
commit 1a089c878b
6 changed files with 251 additions and 89 deletions
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105
Class/detection/algorithm_fall.py
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@@ -6,44 +6,45 @@ import numpy as np
import statistics
import queue
import math
from PIL import ImageDraw, ImageFont
from PIL import Image
def fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList, yList, centerV, alert):
for c in cnts:
#exclusion
# exclusion
if cv2.contourArea(c) < defined_min_area:
continue
# outer bounding box
(x_b, y_b, w_b, h_b) = cv2.boundingRect(c)
cv2.rectangle(frame, (x_b, y_b), (x_b + w_b, y_b + h_b), (0, 255, 255), 2) # 黄色矩形
cv2.rectangle(frame, (x_b, y_b), (x_b + w_b, y_b + h_b), (0, 255, 255), 2) # 黄色矩形
#rotating bounding box
rect = cv2.minAreaRect(c) # 得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度）
box = cv2.boxPoints(rect) # 获取最小外接矩形的4个顶点坐标
# rotating bounding box
rect = cv2.minAreaRect(c) # 得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度）
box = cv2.boxPoints(rect) # 获取最小外接矩形的4个顶点坐标
box = np.int0(box)
cv2.drawContours(frame,[box],0,(0,0,255),2)
cv2.drawContours(frame, [box], 0, (0, 0, 255), 2)
#averaging line
rows,cols = frame.shape[:2]
[vx,vy,x,y] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01)
lefty = (-x * vy/vx) + y
righty =((cols-x) * vy/vx)+y
cv2.line(frame,(cols-1,righty),(0,lefty),(255,0,0),2)
# averaging line
rows, cols = frame.shape[:2]
[vx, vy, x, y] = cv2.fitLine(c, cv2.DIST_L2, 0, 0.01, 0.01)
lefty = (-x * vy / vx) + y
righty = ((cols - x) * vy / vx) + y
cv2.line(frame, (cols - 1, righty), (0, lefty), (255, 0, 0), 2)
#ellipse
# ellipse
elps = cv2.fitEllipse(c)
(x, y), (MA, ma), angle = cv2.fitEllipse(c)
cv2.ellipse(frame, elps,(255,0,0),3) #red
cv2.ellipse(frame, elps, (255, 0, 0), 3) # red
#Aspect Ratio
AR = MA/ma
# Aspect Ratio
AR = MA / ma
#Center Speed - acceleration
# Center Speed - acceleration
prevX = 0.0
prevY = 0.0
centerSpeed =0
centerSpeed = 0
if xList.full():
prevX = statistics.median(list(xList.queue))
prevY = statistics.median(list(yList.queue))
@@ -54,49 +55,57 @@ def fall_detect(cnts, defined_min_area, frame, prevX, prevY, xList, yList, cente
yList.put(elps[0][1])
X = statistics.median(list(xList.queue))
Y = statistics.median(list(yList.queue))
if xList.full():
dx = abs(prevX-X)
dy = abs(prevY-Y)
centerV = math.sqrt(dx**2+dy**2)
# calculate probabilities for the 4 features
pAngle = (abs(angle-90)-50)/10
pAngle = 1 / (math.exp(pAngle)+1)
pAR = 10*AR - 5
pAR = 1 / (math.exp(pAR) + 1)
ACS = centerV - 9
ACS = 1 / (math.exp(ACS) + 1)
if xList.full():
dx = abs(prevX - X)
dy = abs(prevY - Y)
centerV = math.sqrt(dx ** 2 + dy ** 2)
# calculate probabilities for the 4 features
pAngle = (abs(angle - 90) - 50) / 10
pAngle = 1 / (math.exp(pAngle) + 1)
pAR = 10 * AR - 5
pAR = 1 / (math.exp(pAR) + 1)
ACS = centerV - 9
try:
ACS = 1 / (math.exp(ACS) + 1)
except:
ACS = 1 / (float('inf') + 1)
# print("pAngle : ", pAngle)
# print("pAR : ", pAR)
# print("ACS : ", ACS)
#confidence
# confidence
P_FALL = pAngle * pAR * ACS + 0.5
# print("P_FALL1 : ", P_FALL)
P_FALL = 1/ (math.exp(-(P_FALL-0.65)*10)+1)
P_FALL = 1 / (math.exp(-(P_FALL - 0.65) * 10) + 1)
# print("P_FALL2: ", P_FALL)
#status display
cv2.putText(frame, "Status : ", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
cv2.putText(frame, "Fall Confidence: {0:.2f} ".format(P_FALL), (10,50),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 128, 255), 2)
# status display
# cv2.putText(frame, "Status : ", (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
# cv2.putText(frame, "Fall Confidence: {0:.2f} ".format(P_FALL), (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
# (0, 128, 255), 2)
# cv2.putText(frame, "Angle: {0:.2f}".format(angle), (10, 220),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
# cv2.putText(frame, "AR: {0:.2f}".format(AR), (10, 237),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
# cv2.putText(frame, "Center Speed: {0:.2f}".format(centerV), (10, 256),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
#fall
# fall
if P_FALL > 0.88:
if alert >= 8:
print("fall")
cv2.putText(frame, " Fall Detected", (82, 20),cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
if alert >3:
# print("fall")
font = ImageFont.truetype("simsun.ttc", 30, index=1)
img_rd = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(img_rd)
draw.text((10, 10), text="Fall Detected", font=font,
fill=(255, 0, 0))
frame = cv2.cvtColor(np.array(img_rd), cv2.COLOR_RGB2BGR)
# cv2.imwrite("report.jpg", frame)
# send_alert.SendMail("report.jpg")
alert = alert + 1
else:
alert = alert + 1
return alert
return frame,alert