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0e549ca | 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 | import cv2
import numpy as np
from .apps.detection import Detection
from .apps.classification import Classification
from .configs.models import *
from .utils.colors import recognize_color
detection_config = DETECTION_CONFIG['avocado']
classification_config = CLASSIFICATION_CONFIG['avocado']
detection = Detection(ROOT_PATH, detection_config)
classification = Classification(ROOT_PATH, classification_config)
def get_avg_color(image):
img_temp = image.copy()
img_temp[:,:,0], img_temp[:,:,1], img_temp[:,:,2] = np.average(image, axis=(0,1))
img_temp = cv2.resize(img_temp, (25,25))
return img_temp
def get_texture_image(image):
h, w = image.shape[:2]
# Calculate centroid
cx, cy= int(w/2), int(h/2)
# radius crop
radius_cx , radius_cy = int(0.5*cx), int(0.5*cy)
x_min, y_min, x_max, y_max = cx-radius_cx, cy-radius_cy, cx+radius_cx, cy+radius_cy
return image[y_min:y_max, x_min:x_max]
def image_classification(image):
results_conf = list()
results_class = list()
image_resize = cv2.resize(
image.copy(),
(classification_config['image_size'],
classification_config['image_size']),
interpolation = cv2.INTER_AREA)
results_classification = classification(image_resize)
for i in results_classification:
results_conf.append(i[1])
results_class.append(i[0])
print(f'Classification : {i[0]} | confidecne : {i[1]} %')
return results_class, results_conf
def main_process(image):
image_resize = cv2.resize(
image.copy(),
(detection_config['image_size'],
detection_config['image_size']),
interpolation = cv2.INTER_AREA)
# Detection
results = detection(image_resize, size=detection_config['image_size'])
result_detection = detection.extract_results(
results, 0.0,
get_one=True,
boxes_ori=True,
resized_size=detection_config['image_size'],
image_shape=image.shape)
image_drawed = detection.visualize_result(
image.copy(), result_detection
)
# results['detection'] = result_detection
try:
confidence_c = result_detection['avocado'][0]['confidence']
x_min_a, y_min_a, x_max_a, y_max_a = result_detection['avocado'][0]['bbox']
img_avocade = image[y_min_a:y_max_a, x_min_a:x_max_a]
# get texture image
img_texture = get_texture_image(img_avocade)
# classification
results_class, results_conf = image_classification(img_texture)
# Color classification
avg_color = get_avg_color(img_texture)
b,g,r = avg_color[1,1]
b,g,r =int(b),int(g),int(r)
result_color = recognize_color(b,g,r)
# result_fruit = ResultFruit(
# detection= {'label': 'avocado', 'conf': int(confidence_c*100), 'bbox': [x_min_a, y_min_a, x_max_a, y_max_a]},
# classification_texture = result_classification,
# classification_color = result_color
# )
except KeyError:
return image, dict(zip(classification_config['filter_classes'], map(float, [0,0,0,0])))
# print(result_fruit.json())
# print(type(result_fruit.json()))
print(dict(zip(results_class, map(float, results_conf))))
return image_drawed, dict(zip(results_class, map(float, results_conf)))
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