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ObjectRecognition.py
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ObjectRecognition.py
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# !/usr/bin/env python
# Importing modules
import numpy as np
import cv2
# Local modules
from video import create_capture
from common import clock, draw_str
help_message = '''
USAGE: Object Recognition
Place the object to be recognized before the webcam.
Following objects are recognized:
1. Face
2. Eye
3. Smile
4. Wall Clock
5. Number Plate
'''
# Function for detecting face
def detect_face_eye(img, cascade):
rects = cascade.detectMultiScale(img, scaleFactor=1.25, minNeighbors=4, \
minSize=(30, 30), flags = cv2.CASCADE_SCALE_IMAGE)
if len(rects) == 0:
return []
rects[:,2:] += rects[:,:2]
return rects
# Function for detecting smile
def detect_smile(img, cascade):
rects = cascade.detectMultiScale(img, scaleFactor=1.23, minNeighbors=60, \
minSize=(30, 30), flags = cv2.CASCADE_SCALE_IMAGE)
if len(rects) == 0:
return []
rects[:,2:] += rects[:,:2]
return rects
# Function for detecting number plate
def detect_numberplate(img, cascade):
rects = cascade.detectMultiScale(img, scaleFactor=1.01, minNeighbors=15, \
minSize=(3, 3), flags = cv2.CASCADE_SCALE_IMAGE)
if len(rects) == 0:
return []
rects[:,2:] += rects[:,:2]
return rects
# Function for detecting wall clock
def detect_wc(img, cascade):
rects = cascade.detectMultiScale(img, scaleFactor=1.2, minNeighbors=10, \
minSize=(5, 5), flags = cv2.CASCADE_SCALE_IMAGE)
if len(rects) == 0:
return []
rects[:,2:] += rects[:,:2]
return rects
# Function to draw rectangle around the detected object
def draw_rects(img, rects, color, obj):
for x1, y1, x2, y2 in rects:
cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
draw_str(img, (x1, y1-10), ' # %s' %obj) # Name of the detected object
# Main function
if __name__ == '__main__':
import sys, getopt
print help_message
args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
try:
video_src = video_src[0]
except:
video_src = 0
args = dict(args)
# Invoking cascade files
cascade_fn = args.get('--cascade', "E:\SOFTWARES\opencv\sources\data\haarcascades\haarcascade_frontalface_alt.xml")
nested_fn = args.get('--nested-cascade', "E:\SOFTWARES\opencv\sources\data\haarcascades\haarcascade_eye.xml")
smile_fn = args.get('--cascade', "E:\SOFTWARES\opencv\sources\data\haarcascades\haarcascade_mcs_mouth.xml")
numberplate_fn = args.get('--cascade', "E:\SOFTWARES\opencv\sources\data\haarcascades\haarcascade_russian_plate_number.xml")
wc_fn = args.get('--cascade', "E:\SOFTWARES\opencv\sources\data\haarcascades\haarcascade_wallclock.xml")
# Initializing the cascade files
cascade = cv2.CascadeClassifier(cascade_fn)
nested = cv2.CascadeClassifier(nested_fn)
smile = cv2.CascadeClassifier(smile_fn)
numberplate = cv2.CascadeClassifier(numberplate_fn)
wc = cv2.CascadeClassifier(wc_fn)
cam = create_capture(video_src, fallback='synth:bg=../data/aloeR.jpg:noise=0.0')
while (cam.isOpened()):
ret, img = cam.read() # Reading from the webcam
if ret:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Convert to grayscale
gray = cv2.equalizeHist(gray) # Histogram equalization (Adjusting brightness)
rects = detect_face_eye(gray, cascade)
rect_smile = detect_smile(gray, smile)
rect_wc = detect_wc(gray, wc)
rect_numberplate = detect_numberplate(gray, numberplate)
vis = img.copy() # Copy of the original image
draw_rects(vis, rects, (255, 0, 255), "Face")
draw_rects(vis, rect_smile, (255, 255, 0), "smile")
draw_rects(vis, rect_wc, (255, 255, 0), "WallClock")
draw_rects(vis, rect_numberplate, (255, 255, 255), "NumberPlate")
if not nested.empty():
for x1, y1, x2, y2 in rects:
roi = gray[y1:y2, x1:x2]
vis_roi = vis[y1:y2, x1:x2]
subrects = detect_face_eye(roi.copy(), nested)
draw_rects(vis_roi, subrects, (0, 255, 0), "Eye")
cv2.imshow('CONTROLLED OBJECT RECOGNITION', vis) # Displaying the window 'CONTROLLED OBJECT RECOGNITION'
if 0xFF & cv2.waitKey(5) == 27:
cam.release()
break
cam.release() # Closing the webcam
cv2.destroyAllWindows() # Destroying the window