def remove():
start = time.time()
if 'file' not in request.files:
return jsonify({'error': 'missing file'}), 400
if request.files['file'].filename.rsplit(
'.', 1)[1].lower() not in ["jpg", "png", "jpeg"]:
return jsonify({'error': 'invalid file format'}), 400
data = request.files['file'].read()
if len(data) == 0:
return jsonify({'error': 'empty image'}), 400
img = Image.open(io.BytesIO(data))
output = detect.predict(net, np.array(img))
output = output.resize((img.size),
resample=Image.BILINEAR) # remove resample
empty_img = Image.new("RGBA", (img.size),
(255, 255, 255)) #BG Color SCB:(83,40,130)
new_img = Image.composite(img, empty_img, output.convert("L"))
buffer = io.BytesIO()
new_img.save(buffer, "PNG")
buffer.seek(0)
logging.info(f" Predicted in {time.time() - start:.2f} sec")
return f"data:image/png;base64,{base64.b64encode(buffer.getvalue()).decode()}"
def get_img():
f=[]
for file in os.listdir('./imgssave/'):
f.append(file)
path="./imgssave/"+f[0]
frame=cv2.imread(path,cv2.IMREAD_COLOR)
try:
(locs, preds)=predict(frame,faceNet,model)
for (box, pred) in zip(locs, preds):
(startX, startY, endX, endY) = box
cla=np.argmax(pred[0])
label = "Mask" if cla==0 else "No Mask"
color = (0, 255, 0) if cla == 0 else (0, 0, 255)
# include the probability in the label
label = "{}: {:.2f}%".format(label, max(pred[0]) * 100)
cv2.putText(frame, label, (startX, startY - 10),cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
try:
for i in os.listdir('./static/detectedimgs/'):
file='./imgssave/detectedimgs/'+i
os.remove(file)
except:
pass
cv2.imwrite('./static/detectedimgs/detect.jpg',frame)
except :
pass
def process(filename):
new_file = os.path.join(os.getcwd(), 'processed',
filename.split('.')[0] + '.wav')
filename = os.path.join(app.config['UPLOAD_FOLDER'], filename)
#print('ffmpeg -i %s -loglevel 0 -ac 1 -ar 16000 -acodec pcm_s16le -y %s'%(filename,new_file))
os.popen(
'ffmpeg -i %s -loglevel 0 -ac 1 -ar 16000 -acodec pcm_s16le -y %s' %
(filename, new_file))
lang = detect.predict(new_file)
text = transcribe.transcribe(model_path, alphabet_path, lm_path, trie_path,
new_file)
if text == "":
return redirect(url_for('index1'))
else:
session['error'] = ''
print(text)
f = open('input.txt', 'w')
f.write(text)
f.close()
cmd = 'python -m nmt.nmt.nmt --out_dir=../eng_hi_model --inference_input_file=input.txt --inference_output_file=translated.txt'
#print(cmd)
FNULL = open(os.devnull, 'w')
retcode = subprocess.call(cmd.split(' '),
stdout=FNULL,
stderr=subprocess.STDOUT)
out_file = open('translated.txt', encoding='utf-8')
trans = out_file.readline()
if '<unk>' in trans:
return redirect(url_for('index1'))
out_file.close()
print(trans)
save_hindi_audio(trans)
return render_template("next.html", lang=lang, text=text, trans=trans)
def pred():
if request.method=='POST':
data = request.files['file'].read()
img = Image.open(io.BytesIO(data))
output = detect.predict(net, np.array(img))
output = output.resize((img.size), resample=Image.BILINEAR) # remove resample
empty_img = Image.new("RGBA", (img.size), 0)
new_img = Image.composite(img, empty_img, output.convert("L"))
#uploaded image
img_x0=BytesIO()
plt.imshow(new_img)
plt.savefig(img_x0,format='png')
plt.close()
img_x0.seek(0)
plot_url0=base64.b64encode(img_x0.getvalue()).decode('utf8')
return render_template('pred.html', plot_url0=plot_url0)
def image():
global global_time
if request.method == 'GET':
return
# save image to local dir. assume jpg as phones capture jpg
new_time = time.time()
if (new_time - global_time) > 3:
data = request.get_json()
img = data['image']
img = base64.b64decode(img)
wh, detections = predict(img, is_file=True)
global_time = new_time
return jsonify({"response": describe(wh[0], wh[1], detections)})
return jsonify({"response": " Too many requests..."})
def get_frame(self):
try:
ret, frame = self.video.read()
now = time.time()
(locs, preds) = predict(frame, self.faceNet, self.model)
for (box, pred) in zip(locs, preds):
(startX, startY, endX, endY) = box
cla = np.argmax(pred[0])
label = "Mask" if cla == 0 else "No Mask"
color = (0, 255, 0) if cla == 0 else (0, 0, 255)
# include the probability in the label
label = "{}: {:.2f}%".format(label, max(pred[0]) * 100)
cv2.putText(frame, label, (startX, startY - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
cv2.line(frame, (startX, startY), (startX, startY + 25), color,
2)
cv2.line(frame, (startX, startY), (startX + 25, startY), color,
2)
cv2.line(frame, (endX, startY), (endX, startY + 25), color, 2)
cv2.line(frame, (endX, startY), (endX - 25, startY), color, 2)
cv2.line(frame, (startX, endY), (startX, endY - 25), color, 2)
cv2.line(frame, (startX, endY), (startX + 25, endY), color, 2)
cv2.line(frame, (endX, endY), (endX, endY - 25), color, 2)
cv2.line(frame, (endX, endY), (endX - 25, endY), color, 2)
# cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
(hei, wid) = frame.shape[:2]
# fps=cap.get(cv2.CAP_PROP_FPS)
end = time.time()
f = 1 / (end - now)
FPS = 'FPS : ' + str(math.ceil(f))
cv2.putText(frame, str(FPS), (0, hei - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (255, 255, 255), 1)
no_faces = 'No. of faces in video : ' + str(len(locs))
cv2.putText(frame, str(no_faces), (80, hei - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (255, 255, 255), 1)
ret, jpeg = cv2.imencode('.jpg', frame)
return jpeg.tobytes()
except:
pass
