Video processing WIP

process_video.py

@@ -0,0 +1,60 @@
+import cv2
+import tensorflow as tf
+import numpy as np
+
+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']
+
+
+
+def segment_video(video, fps=5):
+    real_fps = video.get(cv2.CAP_PROP_FPS)
+    print(f"{real_fps=}")
+    if real_fps < fps:
+        raise Exception("Video FPS cannot be bigger than desired FPS!")
+
+    n = real_fps / fps
+
+    result = []
+    i=0
+    while True:
+        ret, frame = video.read()
+        if ret == False:
+            break
+        if i % n == 0:
+            result.append(frame)
+        i += 1
+
+    return result, i
+
+def save_frames(frames, dir):
+    for i, frame in enumerate(frames):
+        print(i)        
+        cv2.imwrite(f"{dir}/frame{i}.jpg", frame)
+
+
+def classify(img, model):
+    #img = cv2.resize(img, (224, 224))
+    img = tf.keras.utils.img_to_array(img)
+    img = np.expand_dims(img, axis = 0)
+    return class_names[np.argmax(model.predict(img))]
+
+
+def read_saved_frames(dir, n):
+    result = []
+    for i in range(n+1):
+        img = tf.keras.utils.load_img(f"{dir}/frame{i}.jpg", target_size = [224, 224])
+        result.append(img)
+    return result
+
+
+if __name__ == "__main__":
+    video = cv2.VideoCapture("kamil_asl.mp4")
+    model = tf.keras.models.load_model('model_pred/VGG16_sign_char_detection_model')
+
+    frames, num = segment_video(video, 20)
+    save_frames(frames, "frames")
+    frames = read_saved_frames("frames", num)
+    result = []
+    for frame in frames:
+        result.append(classify(frame, model))
+    print(result)