-
Notifications
You must be signed in to change notification settings - Fork 3
/
app.py
192 lines (154 loc) · 6.5 KB
/
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
import streamlit as st
from cv2 import cv2
from PIL import Image, ImageEnhance
import numpy as np
import os
@st.cache
def load_image(img):
im = Image.open(img)
return im
face_cascade = cv2.CascadeClassifier(
'frecog/haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('frecog/haarcascade_eye.xml')
smile_cascade = cv2.CascadeClassifier('frecog/haarcascade_smile.xml')
def detect_faces(our_image):
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img, 1)
gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)
# Detect faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
# Draw rectangle around the faces
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 5)
return img, faces
# def detect_eyes_and_faces(our_image):
# new_img = np.array(our_image.convert('RGB'))
# img = cv2.cvtColor(new_img, 1)
# gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)
# # Detect faces
# faces = face_cascade.detectMultiScale(gray, 1.1, 4)
# # eyes = eye_cascade.detectMultiScale(gray, 1.3, 5)
# # Draw rectangle around the faces
# for (x, y, w, h) in faces:
# cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
# eyes = eye_cascade.detectMultiScale(gray, 1.3, 5)
# for (ex, ey, ew, eh) in eyes:
# cv2.rectangle(img, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)
# # detect_eyes(our_image)
# return img, faces
def detect_eyes(our_image):
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img, 1)
gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)
eyes = eye_cascade.detectMultiScale(gray, 1.3, 5)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(img, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)
return img, eyes
def detect_smiles(our_image):
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img, 1)
gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)
# Detect Smiles
smiles = smile_cascade.detectMultiScale(gray, 1.1, 4)
# Draw rectangle around the Smiles
for (x, y, w, h) in smiles:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
return img
def cartonize_image(our_image):
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img, 1)
gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)
# Edges
gray = cv2.medianBlur(gray, 5)
edges = cv2.adaptiveThreshold(
gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 9)
# Color
color = cv2.bilateralFilter(img, 9, 300, 300)
# Cartoon
cartoon = cv2.bitwise_and(color, color, mask=edges)
return cartoon
def cannize_image(our_image):
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img, 1)
img = cv2.GaussianBlur(img, (11, 11), 0)
canny = cv2.Canny(img, 100, 150)
return canny
def main():
"""Face Detection App"""
st.title("Face Detection App")
st.text("Build with Streamlit, OpenCV & \u2764\uFE0F by Sagnik Mitra")
activities = ["Detection", "About"]
choice = st.sidebar.selectbox("Select Activty", activities)
if choice == 'Detection':
st.subheader("Face Detection")
image_file = st.file_uploader(
"Upload Image", type=['jpg', 'png', 'jpeg'])
if image_file is not None:
our_image = Image.open(image_file)
st.text("Original Image")
# st.write(type(our_image))
st.image(our_image)
enhance_type = st.sidebar.radio(
"Enhance Type", ["Original", "Gray-Scale", "Contrast", "Brightness", "Blurring"])
if enhance_type == 'Gray-Scale':
new_img = np.array(our_image.convert('RGB'))
img = cv2.cvtColor(new_img, 1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# st.write(new_img)
st.image(gray)
elif enhance_type == 'Contrast':
c_rate = st.sidebar.slider("Contrast", 0.5, 3.5)
enhancer = ImageEnhance.Contrast(our_image)
img_output = enhancer.enhance(c_rate)
st.image(img_output)
elif enhance_type == 'Brightness':
c_rate = st.sidebar.slider("Brightness", 0.5, 3.5)
enhancer = ImageEnhance.Brightness(our_image)
img_output = enhancer.enhance(c_rate)
st.image(img_output)
elif enhance_type == 'Blurring':
new_img = np.array(our_image.convert('RGB'))
blur_rate = st.sidebar.slider("Blurring", 0.5, 3.5)
img = cv2.cvtColor(new_img, 1)
blur_img = cv2.GaussianBlur(img, (11, 11), blur_rate)
st.image(blur_img)
elif enhance_type == 'Original':
st.image(our_image, width=300)
else:
st.image(our_image, width=300)
# Face Detection
task = ["Faces", "Smiles",
"Eyes", "Cannize", "Cartonize"]
feature_choice = st.sidebar.selectbox("Find Features", task)
if st.button("Process"):
if feature_choice == 'Faces':
result_img, result_faces = detect_faces(our_image)
st.image(result_img)
st.success("Found {} faces".format(len(result_faces)))
elif feature_choice == 'Smiles':
result_img = detect_smiles(our_image)
st.image(result_img)
elif feature_choice == 'Eyes':
result_img, result_eyes = detect_eyes(our_image)
st.success("Found {} Eyes".format(len(result_eyes)))
st.image(result_img)
elif feature_choice == 'Cartonize':
result_img = cartonize_image(our_image)
st.image(result_img)
elif feature_choice == 'Cannize':
result_canny = cannize_image(our_image)
st.image(result_canny)
elif feature_choice == 'Eyes and Faces':
result_img, result_faces = detect_faces(our_image)
st.image(result_img)
result_img = detect_eyes(our_image)
st.image(result_img)
st.success("Found {} faces and {} eyes".format(
len(result_faces), len(result_eyes)))
elif choice == 'About':
st.subheader("About Face Detection App")
st.markdown(
"Built with Streamlit by [Sagnik Mitra](https://www.sagnik.tech/)")
st.success("Sagnik Mitra")
if __name__ == '__main__':
main()