def main():
"""Main function of the App"""
ocr = get_easyocr()
st.header("Bad Substances - Find your enemies!")
mode = st.radio("Select Mode", ["Image-file", "Text", "Camera"])
img, text = None, None
if mode == "Camera":
img = st.camera_input("Camera Input!")
elif mode == "Image-file":
img = st.file_uploader("Input Image(s)")
elif mode == "Text":
text = st.text_area("Insert text")
if img is not None:
data = ocr.readtext(img.getvalue())
text = [
x[1] for x in data
] # TODO save and display bbox, in [0]. Score is in [2]
text = " ".join(text)
st.image(img, width=300)
if text:
text = text.replace(",", " ").lower()
text = text.split(" ")
bad = set(text).intersection(bad_substances)
if len(bad):
st.write(f"**Bad Substances Found:** {', '.join(bad)}")
def test_help_tooltip(self):
"""Test that it can be called using a string for type parameter."""
st.camera_input("the label", help="help_label")
c = self.get_delta_from_queue().new_element.camera_input
self.assertEqual(c.help, "help_label")
def test_just_label(self):
"""Test that it can be called with no other values."""
st.camera_input("the label")
c = self.get_delta_from_queue().new_element.camera_input
self.assertEqual(c.label, "the label")
import numpy as np
st.header("Cat/Dog Classfier")
st.write("This application can be used to classify images whether a cat or a dog is depicted on them.")
selector = st.selectbox(
label = "Picture or camera input?",
options = ["picture", "camera"],
index = 1
)
if selector == "picture":
data = st.file_uploader(label="Upload image")
elif selector == "camera":
data = st.camera_input("Take a picture")
try:
st.image(data)
except:
st.warning("No image uploaded!")
st.stop()
if st.button(label = "Classify"):
data = Image.open(data)
image = data.resize((224, 224), Image.LANCZOS)
#st.image(image)
# from tensorflow.keras.applications.vgg16 import VGG16
# vgg16_model = VGG16(include_top=False, input_shape=(224, 224, 3))
img_invert = cv2.bitwise_not(img_gray)
img_smoothing = cv2.GaussianBlur(img_invert, (21, 21),sigmaX=0, sigmaY=0)
final_img = dodgeV2(img_gray, img_smoothing)
return(final_img)
st.title("PencilSketcher App - updated streamlit camera 📷 module")
st.write("This Web App is to help convert your photos to realistic Pencil Sketches")
# collect the user input
#file_image = st.sidebar.file_uploader("Upload your Photos", type=['jpeg','jpg','png'])
# collecting the input image from user camera
file_image = st.camera_input(label = "Take a pic of you to be sketched out")
if file_image:
input_img = Image.open(file_image)
final_sketch = pencilsketch(np.array(input_img))
one, two = st.columns(2)
with one:
st.write("**Input Photo**")
st.image(input_img, use_column_width=True)
with two:
st.write("**Output Pencil Sketch**")
st.image(final_sketch, use_column_width=True)
if st.button("Download Sketch Images"):
im_pil = Image.fromarray(final_sketch)
im_pil.save('final_image.jpeg')
st.write('Download completed')
# Copyright 2018-2022 Streamlit Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import streamlit as st
x = st.camera_input("Label1", help="help1")
if x is not None:
st.image(x)
y = st.camera_input("Label2", help="help2", disabled=True)