def color_recognition(crop_img):
height, width, channels = crop_img.shape
crop_img = crop_image.crop_center(
crop_img, 50, 50
) # crop the detected vehicle image and get a image piece from center of it both for debugging and sending that image piece to color recognition module
# for debugging
#cv2.imwrite(current_path + "/debug_utility"+".png",crop_img) # save image piece for debugging
open(current_path + "/utils/color_recognition_module/" + "test.data", "w")
color_histogram_feature_extraction.color_histogram_of_test_image(
crop_img) # send image piece to regonize vehicle color
prediction = knn_classifier.main(
current_path + "/utils/color_recognition_module/" + "training.data",
current_path + "/utils/color_recognition_module/" + "test.data")
return prediction
mount = [0,0,0,0,0,0,0,0,0,0]
for i in range(0,len(coll)):
io.imsave('test1.jpg',coll[i])
io.imsave('test2.jpg',coll[i])
#print(coll[i])
img1 = Image.open("test1.jpg")
cut_lower_half(img1)
frame = cv2.imread("linshi.jpg")
pre_class = cv2.imread("test1.jpg")
prediction = "n.a."
frame2 = cv2.imread("test2.jpg")
color_histogram_feature_extraction.color_histogram_of_test_image(frame)
prediction = knn_classifier.main("training.data", "test.data")
cv2.putText(frame2, prediction, (15, 45 ), cv2.FONT_HERSHEY_PLAIN, 3, (255,255,255),5)
cv2.imshow('pre color',pre_class)
#cv2.waitKey(0)&0xFF
cv2.imshow('color classifier',frame2)
cv2.imwrite('./result_picture/' + picture_name[i],frame2)
#用于统计正确率
if prediction == "black":
mount[0] = mount[0] + 1
elif prediction == "gray":
mount[1] = mount[1] + 1
elif prediction == "white":
mount[2] = mount[2] + 1
elif prediction == "red":
mount[3] = mount[3] + 1
def detectShape(path):
# for detecting the contour of the pill and its length, and the pill's predicted color
img = cv2.imread(path, 1)
imgheight = img.shape[0]
imgwidth = img.shape[1]
img = cv2.resize(img, (int(imgwidth * (480 / imgheight)), 480))
img = cv2.GaussianBlur(img, (3, 3),
0) # Gaussian blurring to remove noise in the image
kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
# img = cv2.filter2D(img, -1, kernel)
drugShape = 'UNDEFINED' # initializing the drug shape
edges = cv2.Canny(img, 100,
200) # detecting the edges to identify the pill
contours, hierarchy = cv2.findContours(
edges, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE) # finding the contours in the image
areas = np.array([cv2.contourArea(c)
for c in contours]) # array of the areas of all contours
contours = np.array(contours) # array of all contours in the image
arr1inds = areas.argsort() # sorting contours
contours = contours[
arr1inds[::
-1]] # descendingly, as the pill will be tha largest contour
approx = cv2.approxPolyDP(contours[0],
0.01 * cv2.arcLength(contours[0], True), True)
x, y, w, h = cv2.boundingRect(
contours[0]) # offsets - with this you get 'mask'
cv2.drawContours(img, [contours[0]], 0, (255, 0, 0), 2)
# to get the average of the colors inside the largest contour "inside the pill"
newIM = img[y:y + h, x:x + w]
yn = newIM.shape[0]
xn = newIM.shape[1]
y = y + int(yn * 20 / 100)
h = h - int(yn * 40 / 100)
x = x + int(xn * 20 / 100)
w = w - int(xn * 30 / 100)
newImage = img[y:y + h, x:x + w] # inside the contour
colors = np.array(cv2.mean(newImage)).astype(
np.uint8) # average of the colors inside newImage
prediction = 'n.a.'
# increase saturation before white detection for light colors elimination
hsvImage = cv2.cvtColor(newImage, cv2.COLOR_BGR2HSV)
hsvImage[:, :,
1] = hsvImage[:, :,
1] * 2.5 # increasing the saturation of the color
backImage = cv2.cvtColor(hsvImage, cv2.COLOR_HSV2BGR)
# using BGR
backColors = np.array(cv2.mean(backImage)).astype(
np.uint8) # average of colors after increasing the saturation
# the prediction of the color classifier RGB
prediction = knn_classifier.main(
'training.data',
np.array([backColors[2], backColors[1], backColors[0]]))
if prediction == 'white': # for white only not the light colors
#RED GREEN BLUE
if backColors[2] >= 180 and backColors[1] >= 150 and backColors[
0] <= 90: # it will not be white using trial and error
prediction = 'other'
else:
backColors = np.array(cv2.mean(newImage)).astype(
np.uint8) # average of colors after increasing the saturation
# the prediction of the color classifier RGB
prediction = knn_classifier.main(
'training.data',
np.array([backColors[2], backColors[1], backColors[0]]))
if backColors[2] >= 180 and backColors[1] >= 150 and backColors[
0] <= 90: # it will not be white using trial and error
prediction = 'other'
if prediction == 'other':
# using HSV
colors = rgb2hsv(colors[2], colors[1], colors[0])
# the prediction of the color classifier HSV
prediction = knn_classifier.main(
'newData.data', np.array([colors[0], colors[1], colors[2]]))
return len(approx), prediction, contours[0]
import cv2
import feature
import knn_classifier
import os
import os.path
source_image = cv2.imread('redshirt.jpg')
prediction = 'n.a.'
PATH = './training.data'
if os.path.isfile(PATH) and os.access(PATH, os.R_OK):
print('training data is ready, classifier is loading...')
else:
open('training.data', 'w')
feature.training()
feature.test_image(source_image)
prediction = knn_classifier.main('training.data', 'test.data')
cv2.putText(
source_image,
'Prediction: ' + prediction,
(35, 25),
cv2.FONT_HERSHEY_PLAIN,
1,
50,
)
cv2.imshow('color classifier', source_image)
cv2.waitKey(0)
# cv2.waitKey(0)
#
### Video Testing
source_video = "video.mp4"
cap = cv2.VideoCapture(source_video)
while (True):
# Capture frame-by-frame
ret, frame = cap.read()
cv2.imwrite("frame.jpg", frame)
featureExtractor.main(2, "frame.jpg") # extract feature for our test image
prediction = knn_classifier.main('features.data', 'test.data')
cv2.putText(frame,
prediction, (15, 45),
cv2.FONT_HERSHEY_PLAIN,
3,
200,
thickness=3)
cv2.imshow('frame', frame)
if cv2.waitKey(120) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
import knn_classifier
import numpy as np
prediction = 'n.a.'
prediction = knn_classifier.main('training.data', np.array([255,255,255]))
print('pred', prediction)
def main(argv):
arg_index_start = 0
DEBUG = "FALSE"
try:
opts, args = getopt.getopt(argv, 'd', ['debug'])
if not args:
usage()
sys.exit(2)
except getopt.GetoptError as err:
usage()
sys.exit(2)
for opt, arg in opts:
if opt in ('-d'):
DEBUG = "TRUE"
arg_index_start = 1
# arguments
if not argv[arg_index_start + 4]:
usage()
sys.exit(2)
if os.path.isdir(argv[arg_index_start]):
feature_files_dir = argv[arg_index_start]
else:
print("Feature directory does not exist: ",
argv[arg_index_start],
file=sys.stderr)
usage()
sys.exit(2)
if os.path.isfile(argv[arg_index_start + 1]):
outtype_dataset_file = argv[arg_index_start + 1]
else:
print("Out type datafile does not exist: ",
argv[arg_index_start + 1],
file=sys.stderr)
usage()
sys.exit(2)
i = 2
debug_opt = ""
datatype_filenames = []
datatypes = []
dist_metrics = []
weights = []
while i < len(args):
if os.path.isfile(argv[arg_index_start + i]):
datatype_filename = argv[arg_index_start + i]
datatype_filenames.append(datatype_filename)
datatype = os.path.basename(datatype_filename.rsplit('-', 1)[0])
datatypes.append(datatype)
else:
print("Input datatype file does not exist: ",
argv[arg_index_start + i],
file=sys.stderr)
usage()
sys.exit(2)
if argv[arg_index_start + (i + 1)]:
dist_metrics.append(argv[arg_index_start + (i + 1)])
else:
print("No distance metric provided for ",
argv[arg_index_start + i],
file=sys.stderr)
usage()
sys.exit(2)
if argv[arg_index_start + (i + 2)]:
weights.append(argv[arg_index_start + (i + 2)])
else:
print("No weight provided for ",
argv[arg_index_start + i],
file=sys.stderr)
usage()
sys.exit(2)
i = i + 3
outtype_col_name = "Id"
if DEBUG == "TRUE":
print("*** DEBUG: " + sys.argv[0] + ": datatypes:",
datatypes,
file=sys.stderr)
print("*** DEBUG: " + sys.argv[0] + ": dist_metrics:",
dist_metrics,
file=sys.stderr)
print("*** DEBUG: " + sys.argv[0] + ": weights:",
weights,
file=sys.stderr)
cumulative_prediction = 0
for datatype_num in range(len(datatypes)):
if DEBUG == "TRUE":
print("*** DEBUG: " + sys.argv[0] + ": datatype_num:",
datatype_num,
file=sys.stderr)
print("*** DEBUG: " + sys.argv[0] + ": datatype:",
datatypes[datatype_num],
file=sys.stderr)
features_file = feature_files_dir + "/" + datatype + ".tmp"
if not os.path.isfile(features_file):
print("Features file does not exist: ",
features_file,
file=sys.stderr)
sys.exit(2)
if DEBUG == "TRUE":
debug_opt = "-d "
knn_args = debug_opt + features_file + " " + outtype_dataset_file + " " + datatype_filenames[
datatype_num] + " " + dist_metrics[datatype_num]
prediction = knn_classifier.main(knn_args.split())
if DEBUG == "TRUE":
print("*** DEBUG: " + sys.argv[0] + ": prediction:",
prediction,
file=sys.stderr)
cumulative_prediction = cumulative_prediction + prediction
if DEBUG == "TRUE":
print("*** DEBUG: " + sys.argv[0] + ": cumulative_prediction:",
cumulative_prediction,
file=sys.stderr)
avg_prediction = cumulative_prediction / (len(datatypes))
if DEBUG == "TRUE":
print("*** DEBUG: " + sys.argv[0] + ": avg_prediction:",
avg_prediction,
file=sys.stderr)
if avg_prediction < 0.5:
final_prediction = 0
else:
final_prediction = 1
return (final_prediction)
