``<kbd>@app.route('/upload', methods=['GET', 'POST'])
def upload():
chart_image = None
if request.method == 'POST':
if 'img_file' in request.files:
file = request.files['img_file']
if file.filename != '':
filename = secure_filename(file.filename)
file_path = os.path.join(UPLOAD_FOLDER, filename)
file.save(file_path)
img = predictor(file_path)
img_cut = nn(file_path)
if img is not None:
color_detection_chart = color_detection12(img_cut, n_colors=3, show_chart=True, output_chart='static/color_chart.png')
chart_image = 'static/color_chart.png'
else:
print("PROBLEM")
pass`
return render_template('upload.html', chart_image=chart_image)
`def predictor(img_file):
img = cv2.imread(img_file)
resize = cv2.resize(img, (64, 64))
# resize = np.expand_dims(resize, axis=0)
img_fin = np.reshape(resize, [1, 64, 64, 3])
json_file = open('model/binaryfas10.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights("model/binaryfashion.h5")
# print("Loaded model from disk")
prediction = loaded_model.predict(img_fin)
prediction = np.squeeze(prediction, axis=1)
predict = np.squeeze(prediction, axis=0)
return int(predict)
`
"""Neural Network Decoding"""
""" The coordinates are created and trained"""
"""-----------------"""
image_width = 300
image_height = 500
def path_file(file):
return str(file)
import cv2
import numpy as np
def color_detection12(img, n_colors, show_chart=False, output_chart=None):
# RGB formatına dönüştürme
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# print(img.shape)
# Alfa kanalını kaldırma
img = img[:, :, 2]
clf = KMeans(n_clusters=n_colors)
colors = clf.fit_predict(img.reshape(-1, 3))
counts = Counter(colors)
center_colors = clf.cluster_centers_
ordered_colors = [center_colors[i] for i in range(n_colors)]
hex_colors = ['#%02x%02x%02x' % tuple(map(int, color)) for color in ordered_colors]
color_category = dict(zip(hex_colors, counts.values()))
if show_chart:
plt.figure(figsize=[10, 10])
plt.pie(color_category.values(), labels=color_category.keys(), colors=color_category.keys())
plt.savefig(output_chart)
return color_category
def nn(img_file):
predict = predictor(img_file)
file = path_file("annotation.csv")
reader = pd.read_csv(file)
#print(predict)
img = cv2.imread(img_file)
img = cv2.resize(img, (image_width, image_height))
# seg = img(img, reader.x1[predict], reader.y1[predict], reader.x2[predict], reader.y2[predict], reader.i[predict])
mask = np.zeros(img.shape[:2], np.uint8)
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
rect = (reader.x1[predict], reader.y1[predict], reader.x2[predict], reader.y2[predict])
cv2.grabCut(img, mask, rect, bgdModel, fgdModel, reader.i[predict], cv2.GC_INIT_WITH_RECT)
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
img_cut = img*mask2[:, :, np.newaxis]
img_array = cv2.imread('examples/grabcut_dress2.png')
# cv2.imshow("name",img_array)
#img_array = cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB)
# cv2.waitKey(0)
color_detection12(img_array, n_colors=3, show_chart=True, output_chart=output_chart)
# find_dominant_color_quantization(img_array)
#nn(file_path)
if __name__ == '__main__':
app.run(debug=True, host='127.0.0.1',port=5000)
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Dosya Yükle</title>
</head>
<body>
<h1>Dosya Yükle</h1>
<form action="/upload" method="POST" enctype="multipart/form-data">
<input type="file" name="img_file" accept="image/*">
<input type="submit" value="Upload" onclick="upload()">
<button type="submit" value="Upload" onclick="upload()">UPLOAD</button>
<button></button>
</form>
<h2>Color Chart</h2>
<img src="{{ url_for('static', filename=chart_image) }}" alt="Color Chart">
</body>
</html>`
When I pressed the upload button on the image I uploaded, I made a graph cut with the nn function and showed the dominant color. I wrote the code. I want to integrate it with Flask for web, but when I wanted to show a chart image on the upload page, fashion.py”, line 109, in upload
color_detection_chart = color_detection12(img_cut, n_colors=3, show_chart=True, output_chart=’static/color_chart.png’)
fashion.py”, line 160, in color_detection12
img = img[:, :, 2]
TypeError: ‘NoneType’ object is not subscriptable
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