69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
import cv2
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import tensorflow as tf
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import numpy as np
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from crop_hand_skeleton import crop_hand
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from cvzone.HandTrackingModule import HandDetector
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class_names = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'del', 'nothing', 'space']
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def segment_video(video, fps=5):
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real_fps = video.get(cv2.CAP_PROP_FPS)
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print(f"{real_fps=}")
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if real_fps < fps:
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raise Exception("Video FPS cannot be bigger than desired FPS!")
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n = int(real_fps / fps)
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result = []
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frames_nums = []
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i=0
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num = 0
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while True:
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ret, frame = video.read()
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if ret == False:
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break
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if i % n == 0:
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result.append(frame)
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frames_nums.append(i)
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num += 1
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i += 1
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return result, num, frames_nums
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def save_frames(frames, dir):
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detector = HandDetector(maxHands=1, mode=True, detectionCon=0.7, minTrackCon=0.8)
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for i, frame in enumerate(frames):
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print(i)
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cv2.imwrite(f"{dir}/frame{i}.jpg", crop_hand(frame, detector))
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def classify(img, model):
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#img = cv2.resize(img, (224, 224))
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img = tf.keras.utils.img_to_array(img)
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img = np.expand_dims(img, axis = 0)
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return class_names[np.argmax(model.predict(img))]
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def read_saved_frames(dir, n):
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result = []
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for i in range(n):
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img = tf.keras.utils.load_img(f"{dir}/frame{i}.jpg", target_size = [224, 224])
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result.append(img)
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return result
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if __name__ == "__main__":
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video = cv2.VideoCapture("mp.mp4")
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model = tf.keras.models.load_model('model_pred/effnet_sign_char_detection_model')
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frames, num = segment_video(video, 30)
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print(num)
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save_frames(frames, "frames")
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frames = read_saved_frames("frames", num)
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result = []
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for frame in frames:
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result.append(classify(frame, model))
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print(result)
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