60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
from PIL import Image
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import cv2 as cv
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import re
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def detect_img(yolo, img_path):
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detected_robots = []
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try:
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image = Image.open(img_path)
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except:
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print('Image open Error! Try again!')
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return None
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else:
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r_image, pred = yolo.detect_image(image)
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processed_image = cv.imread(img_path)
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# r_image.show()
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# r_image.save(img_path)
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if not pred:
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return None
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## FIXME : better list mapping
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for prediction in pred:
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is_robot_detected = re.search("robot", prediction[0])
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if is_robot_detected:
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x1 = prediction[1][0]
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x2 = prediction[2][0]
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y1 = prediction[1][1]
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y2 = prediction[2][1]
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w = abs(x1 - x2)
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h = abs(y1 - y2)
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# print(pred)
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# print(f'x1: {x1}, x2: {x2}, y1: {y1}, y2: {y2}, w: {w}, h: {h}')
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robot_img = processed_image[y1:y1 + h, x1:x1 + w]
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robot_img = cv.resize(robot_img, (128, 128), interpolation=cv.INTER_AREA)
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robot_pos = w, x1
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detected_robots.append((robot_img, robot_pos))
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return detected_robots
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def get_turn_value(cords):
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img_width = 1920
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w, x = cords
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center_of_object = (x + w) / 2
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object_position = center_of_object / img_width
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return round(object_position * 100, 2)
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def detect_robot(model, img_path):
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detected_robots = detect_img(model, img_path)
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return detected_robots
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# if not detected_robots:
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# return None
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# robot_position = detected_robots[0][1]
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# turn_val = get_turn_value(robot_position)
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# return turn_val
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