trafficsign_camcontrol_py.py

# -*- coding: utf-8 -*-
"""TrafficSign_camcontrol.py

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Eh-pI20w3CrNJQUnOVCQYJ2xKHM24W3r
"""

import numpy as np
import cv2
from tensorflow import keras
threshold = 0.75 # THRESHOLD
font = cv2.FONT_HERSHEY_SIMPLEX
model = keras.models.load_model('modelfinal.h5') #load model
#preprocsing
def preprocess_img(imgBGR, erode_dilate=True): # pre-processing to detect signs in image.
 rows, cols, _ = imgBGR.shape
 imgHSV = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2HSV)
 Bmin = np.array([100, 43, 46])
 Bmax = np.array([124, 255, 255])
 img_Bbin = cv2.inRange(imgHSV, Bmin, Bmax)
 Rmin1 = np.array([0, 43, 46])
 Rmax1 = np.array([10, 255, 255])
 img_Rbin1 = cv2.inRange(imgHSV, Rmin1, Rmax1)
 Rmin2 = np.array([156, 43, 46])
 Rmax2 = np.array([180, 255, 255])
 img_Rbin2 = cv2.inRange(imgHSV, Rmin2, Rmax2)
 img_Rbin = np.maximum(img_Rbin1, img_Rbin2)
 img_bin = np.maximum(img_Bbin, img_Rbin)
 if erode_dilate is True:
 kernelErosion = np.ones((3, 3), np.uint8)
 kernelDilation = np.ones((3, 3), np.uint8)
 img_bin = cv2.erode(img_bin, kernelErosion, iterations=2)
 img_bin = cv2.dilate(img_bin, kernelDilation, iterations=2)
 return img_bin


def contour_detect(img_bin, min_area, max_area=-1, wh_ratio=2.0) :
 rects = []
 contours, _ = cv2.findContours(img_bin.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
 if len(contours) == 0:
 return rects
 max_area = img_bin.shape[0] * img_bin.shape[1] if max_area < 0 else max_area
 for contour in contours:
 area = cv2.contourArea(contour)
 if area >= min_area and area <= max_area:
 x, y, w, h = cv2.boundingRect(contour)
 if 1.0 * w / h < wh_ratio and 1.0 * h / w < wh_ratio:
 rects.append([x, y, w, h])
 return rects


def grayscale(img):
 img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 return img


def equalize(img):
 img = cv2.equalizeHist(img)
 return img


def preprocessing(img):
 img = grayscale(img)
 img = equalize(img)
 img = img / 255
 return img


def getCalssName(classNo):
 if classNo == 0:
 return 'Speed Limit 20 km/h'
 elif classNo == 1:
 return 'Speed Limit 30 km/h'
 elif classNo == 2:
 return 'Speed Limit 50 km/h'
 elif classNo == 3:
 return 'Speed Limit 60 km/h'
 elif classNo == 4:
 return 'Speed Limit 70 km/h'
 elif classNo == 5:
 return 'Speed Limit 80 km/h'
 elif classNo == 6:
 return 'End of Speed Limit 80 km/h'
 elif classNo == 7:
 return 'Speed Limit 100 km/h'
 elif classNo == 8:
 return 'Speed Limit 120 km/h'
 elif classNo == 9:
 return 'No passing'
 elif classNo == 10:
 return 'No passing for vehicles over 3.5 metric tons'
 elif classNo == 11:
 return 'Right-of-way at the next intersection'
 elif classNo == 12:
 return 'Priority road'
 elif classNo == 13:
 return 'Yield'
 elif classNo == 14:
 return 'Stop'
 elif classNo == 15:
 return 'No vehicles'
 elif classNo == 16:
 return 'Vehicles over 3.5 metric tons prohibited'
 elif classNo == 17:
 return 'No entry'
 elif classNo == 18:
 return 'General caution'
 elif classNo == 19:
 return 'Dangerous curve to the left'
 elif classNo == 20:
 return 'Dangerous curve to the right'
 elif classNo == 21:
 return 'Double curve'
 elif classNo == 22:
 return 'Bumpy road'
 elif classNo == 23:
 return 'Slippery road'
 elif classNo == 24:
 return 'Road narrows on the right'
 elif classNo == 25:
 return 'Road work'
 elif classNo == 26:
 return 'Traffic signals'
 elif classNo == 27:
 return 'Pedestrians'
 elif classNo == 28:
 return 'Children crossing'
 elif classNo == 29:
 return 'Bicycles crossing'
 elif classNo == 30:
 return 'Beware of ice/snow'
 elif classNo == 31:
 return 'Wild animals crossing'
 elif classNo == 32:
 return 'End of all speed and passing limits'
 elif classNo == 33:
 return 'Turn right ahead'
 elif classNo == 34:
 return 'Turn left ahead'
 elif classNo == 35:
 return 'Ahead only'
 elif classNo == 36:
 return 'Go straight or right'
 elif classNo == 37:
 return 'Go straight or left'
 elif classNo == 38:
 return 'Keep right'
 elif classNo == 39:
 return 'Keep left'
 elif classNo == 40:
 return 'Roundabout mandatory'
 elif classNo == 41:
 return 'End of no passing'
 elif classNo == 42:
 return 'End of no passing by vehicles over 3.5 metric tons'


if __name__ == "__main__":
 cap = cv2.VideoCapture(0)
 cols = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
 rows = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
 while (1):
 ret, img = cap.read()
 img_bin = preprocess_img(img, False)
 cv2.imshow("bin image", img_bin)
 min_area = img_bin.shape[0] * img.shape[1] / (25 * 25)
 rects = contour_detect(img_bin, min_area=min_area) # get x,y,h and w.
 img_bbx = img.copy()
 for rect in rects:
 xc = int(rect[0] + rect[2] / 2)
 yc = int(rect[1] + rect[3] / 2)
 size = max(rect[2], rect[3])
 x1 = max(0, int(xc - size / 2))
 y1 = max(0, int(yc - size / 2))
 x2 = min(cols, int(xc + size / 2))
 y2 = min(rows, int(yc + size / 2))
 # rect[2] is width and rect[3] for height
 if rect[2] > 100 and rect[3] > 100: #only detect those signs whose height and width >100
 cv2.rectangle(img_bbx, (rect[0], rect[1]), (rect[0] + rect[2], rect[1] + rect[3]), (0, 0, 255), 2)
 crop_img = np.asarray(img[y1:y2, x1:x2])
 crop_img = cv2.resize(crop_img, (32, 32))
 crop_img = preprocessing(crop_img)
 cv2.imshow("afterprocessing", crop_img)
 crop_img = crop_img.reshape(1, 32, 32, 1) # (1,32,32) after reshape it become (1,32,32,1)
 predictions = model.predict(crop_img) # make predicion
 classIndex = np.argmax(predictions, axis=1)
 probabilityValue = np.amax(predictions)
 if probabilityValue > threshold:
 #write class name on the output screen
 cv2.putText(img_bbx, str(classIndex) + " " + str(getCalssName(classIndex)), 
(rect[0], rect[1] - 10),
 font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
 # write probability value on the output screen
 cv2.putText(img_bbx, str(round(probabilityValue * 100, 2)) + "%", (rect[0], rect[1] - 40), font, 0.75,(0, 0, 255), 2, cv2.LINE_AA)
 cv2.imshow("detect result", img_bbx)
 if cv2.waitKey(1) & 0xFF == ord('q'): # q for quit
 break


cap.release()
cv2.destroyAllWindows()