ahmetozlu / color_recognition Goto Github PK

training data is ready, classifier is loading...
Traceback (most recent call last):
  File ".\src\color_classification_image.py", line 32, in <module>
    prediction = knn_classifier.main('training.data', 'test.data')
  File "...\color_recognition-master\color_recognition-master\src\color_recognition_api\knn_classifier.py", line 87, in main
    return classifier_prediction[0])
IndexError: list index out of range

` ~/Documents/Code/yolov3-tf2/yolov3_tf2/utils.py in color_recog(cropped_np)
    165         print('training data is ready, classifier is loading...')
    166 
--> 167     # get the prediction
    168     color_histogram_feature_extraction.color_histogram_of_test_image(cropped_np)
    169     prediction = knn_classifier.main('./data/training.data', './data/test.data')

~/Documents/Code/yolov3-tf2/yolov3_tf2/knn_classifier.py in main(training_data, test_data)
     74     k = 3  # K value of k nearest neighbor
     75     for x in range(len(test_feature_vector)):
---> 76         neighbors = kNearestNeighbors(training_feature_vector, test_feature_vector[x], k)
     77         result = responseOfNeighbors(neighbors)
     78         classifier_prediction.append(result)

~/Documents/Code/yolov3-tf2/yolov3_tf2/knn_classifier.py in kNearestNeighbors(training_feature_vector, testInstance, k)
     25     neighbors = []
     26     for x in range(k):
---> 27         neighbors.append(distances[x][0])
     28     return neighbors
     29 

IndexError: list index out of range

from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import imutils
import time
import cv2

from color_recognition_api import color_histogram_feature_extraction
from color_recognition_api import knn_classifier
import os
import os.path
# global x1 , y1 , w1, h1, endX, startX, endY, startY , area1


CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe('prototxt.txt', 'caffemodel')
print("[INFO] starting video stream...")
prediction = 'n.a.'

# checking whether the training data is ready
PATH = './training.data'

if os.path.isfile(PATH) and os.access(PATH, os.R_OK):
    print ('training data is ready, classifier is loading...')
else:
    print ('training data is being created...')
    open('training.data', 'w')
    color_histogram_feature_extraction.training()
    print ('training data is ready, classifier is loading...')
vs = cv2.VideoCapture(0)
# vs.release()
# vs = cv2.VideoCapture(0)
time.sleep(2.0)
fps = FPS().start()
width = vs.get(3)  # float
height = vs.get(4)  # float
print width, height

fps = FPS().start()
while True:
    ret, frame = vs.read()





    # frame = imutils.resize(frame, width=400)
    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 0.007843, (300, 300), 127.5)
    net.setInput(blob)
    detections = net.forward()
    big_area = 0
    big_center = 320
    detected = 0
    for i in np.arange(0, detections.shape[2]):
        confidence = detections[0, 0, i, 2]
        object_type = int(detections[0, 0, i, 1])
        if object_type == 15 and confidence > 0.2:
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")
            label = "{}: {:.2f}%".format('person', confidence * 100)
            cv2.rectangle(frame, (startX, startY), (endX, endY), [0, 0, 255], 2)
            y = startY - 15 if startY - 15 > 15 else startY + 15
            cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, [0, 0, 255], 2)
            # ROI = green[startX:endX, startY:endY]

            ROI1 = frame[startX:endX, startY:endY]
            color_histogram_feature_extraction.color_histogram_of_test_image(ROI1)

            prediction = knn_classifier.main('training.data', 'test.data')
            print  prediction
            rect_area = (endX-startX)*(endY-startY)
            # print (endX, startX, endY, startY, type(endX),type(startX),type(endY),type(startY))
            detected = 1
            if rect_area > big_area:
                big_area = rect_area

    cv2.imshow("Frame", frame)

    key = cv2.waitKey(1) & 0xFF
    if key == ord("q"):
        break
    fps.update()
    fps.stop()
vs.release()    
cv2.destroyAllWindows()