def main():
model_name = sys.argv[1]
if model_name not in ['svm', 'logistic', 'knn']:
print("Invalid model-name '{}'!".format(model_name))
return
print("Using model '{}'...".format(model_name))
model_serialized_path = get_config(
"model_{}_serialized_path".format(model_name))
print("Model deserialized from path '{}'".format(model_serialized_path))
camera = cv2.VideoCapture(0)
while True:
ret, frame = camera.read()
cv2.imshow("Recording", frame)
key = cv2.waitKey(1)
if key == ord('q'):
break
elif key == ord('c'):
if not ret:
print("Failed to capture image!")
continue
frame = resize_image(frame, 400)
r = cv2.selectROI(frame)
imCrop = frame[int(r[1]):int(r[1] + r[3]),
int(r[0]):int(r[0] + r[2])]
cv2.imshow("Image", imCrop)
#cv2.imshow("Webcam recording", frame)
frame = imCrop
try:
frame = apply_image_transformation(frame)
frame_flattened = frame.flatten()
classifier_model = joblib.load(model_serialized_path)
predicted_labels = classifier_model.predict(frame_flattened)
predicted_label = predicted_labels[0]
print("Predicted label = {}".format(predicted_label))
predicted_image = get_image_from_label(predicted_label)
predicted_image = resize_image(predicted_image, 200)
cv2.imshow("Prediction = '{}'".format(predicted_label),
predicted_image)
engine = pyttsx.init()
engine.say("The predicted text is " + str(predicted_label))
engine.runAndWait()
engine.stop()
except Exception:
exception_traceback = traceback.format_exc()
print(
"Error while applying image transformation with the following exception trace:\n{}"
.format(exception_traceback))
cv2.destroyAllWindows()
print "The program completed successfully !!"
def main():
model_name = "svm"
if model_name not in ['svm', 'logistic', 'knn']:
print("Invalid model-name '{}'!".format(model_name))
return
print("Using model '{}'...".format(model_name))
model_serialized_path = get_config(
"model_{}_serialized_path".format(model_name))
print("Model deserialized from path '{}'".format(model_serialized_path))
camera = cv2.VideoCapture(0)
while True:
ret, frame = camera.read()
if not ret:
print("Failed to capture image!")
continue
frame = resize_image(frame, 400)
cv2.imshow("Webcam recording", frame)
try:
frame = apply_image_transformation(frame)
frame_flattened = frame.flatten()
#print(model_serialized_path)
#model_serialized_path = "C:\\Users\\AanikaRahman\\Documents\\GitHub\\ASL1\\Sign-Language-Recognition\\data\\generated\\output\\svm\\model-serialized-svm.pkl"
classifier_model = joblib.load(model_serialized_path)
#classifier_model = pickle.load(open(model_serialized_path),encoding='latin1')
predicted_labels = classifier_model.predict(frame_flattened)
predicted_label = predicted_labels[0]
print("Predicted label = {}".format(predicted_label))
predicted_image = get_image_from_label(predicted_label)
predicted_image = resize_image(predicted_image, 200)
cv2.imshow("Prediction = '{}'".format(predicted_label),
predicted_image)
except Exception:
exception_traceback = traceback.format_exc()
print(
"Error while applying image transformation with the following exception trace:\n{}"
.format(exception_traceback))
cv2.waitKey(2000)
cv2.destroyAllWindows()
cv2.destroyAllWindows()
print("The program completed successfully !!")
def main():
model_name = sys.argv[1]
if model_name not in ['svm', 'logistic', 'knn']:
print("Invalid model-name '{}'!".format(model_name))
return
print("Using model '{}'...".format(model_name))
model_serialized_path = get_config(
"model_{}_serialized_path".format(model_name))
print("Model deserialized from path '{}'".format(model_serialized_path))
camera = cv2.VideoCapture(0)
while True:
ret, frame = camera.read()
if not ret:
print("Failed to capture image!")
continue
frame = resize_image(frame, 400)
cv2.imshow("Webcam recording", frame)
try:
frame = apply_image_transformation(frame)
frame_flattened = frame.flatten()
classifier_model = joblib.load(model_serialized_path)
predicted_labels = classifier_model.predict(frame_flattened)
predicted_label = predicted_labels[0]
print("Predicted label = {}".format(predicted_label))
predicted_image = get_image_from_label(predicted_label)
predicted_image = resize_image(predicted_image, 200)
cv2.imshow("Prediction = '{}'".format(predicted_label),
predicted_image)
except Exception:
exception_traceback = traceback.format_exc()
print(
"Error while applying image transformation with the following exception trace:\n{}"
.format(exception_traceback))
cv2.waitKey(10000)
cv2.destroyAllWindows()
cv2.destroyAllWindows()
print "The program completed successfully !!"
def mainimage():
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = (10, 50)
fontScale = 1
fontColor = (255, 255, 255)
lineType = 2
model_name = "svm"
if model_name not in ['svm']:
print("Invalid model-name '{}'!".format(model_name))
return
print("Using model '{}'...".format(model_name))
model_serialized_path = get_config(
"model_{}_serialized_path".format(model_name))
print("Model deserialized from path '{}'".format(model_serialized_path))
classifier_model = joblib.load(model_serialized_path)
import tkFileDialog
tk.Tk().withdraw()
path = tkFileDialog.askopenfile()
img = cv2.imread(path.name)
frame = resize_image(img, 400)
image = frame
frame = cv2.resize(img, (50, 50))
try:
frame = apply_image_transformation(frame)
frame_flattened = frame.flatten()
print classifier_model
predicted_labels = classifier_model.predict(frame_flattened)
print("Predicted label = {}".format(predicted_labels))
cv2.putText(image, str(predicted_labels), bottomLeftCornerOfText, font,
fontScale, fontColor, lineType)
cv2.imshow("frame", image)
key = cv2.waitKey(2)
if key == 27:
cv2.destroyAllWindows()
except Exception:
exception_traceback = traceback.format_exc()
print(
"Error while applying image transformation with the following exception trace:\n{}"
.format(exception_traceback))
cv2.waitKey(0)
print "The program completed successfully !!"
vidcap = cv2.VideoCapture(1)
success,frame = vidcap.read()
count = 0
success = True
while success:
success,frame = vidcap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Display the resulting frame
cv2.imshow('frame',gray)
print 'Read a new frame: ', success
if not success:
print("Failed to capture image!")
continue
frame = resize_image(frame, 400)
cv2.imshow("Webcam recording", frame)
# save frames
cv2.imwrite("frames\\random\\frame%d.jpg" % count, frame) # save frame as JPEG file
count += 1
# PROCESS:
# extract features
# make predictions on features
# output
if cv2.waitKey(500) & 0xFF == ord('q'):
break
# When everything done, release the capture