def build_knn():
target_file_path_list = ['true.txt', 'false.txt']
X = []
Y = []
for target_file_path in target_file_path_list:
target_file_list = get_label_file_index(target_file_path)
# print(true_file_list)
for label_file in target_file_list:
file_path = os.path.join(DATA_DIR, label_file)
if not os.path.exists(file_path):
download_file(get_video_url(label_file), file_path)
for frame in open_video(file_path):
# print(frame)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
img2 = np.zeros_like(frame)
img2[:, :, 0] = gray
img2[:, :, 1] = gray
img2[:, :, 2] = gray
frame = img2
embed = Encoder.predict(np.array([frame]))[0]
X.append(embed)
label = target_file_path.split('.')[0]
Y.append(label)
# cv2.imwrite(os.path.join(LABEL_FRAME_DIR, label_file+'.png'), frame)
# cv2.imshow('Frame_focus', frame)
break
knn = KNeighborsClassifier(metric='cosine', n_neighbors=5)
knn.fit(X, Y)
return knn
parser.add_argument('--skip_checksum',
action='store_true',
help='Skip checksum')
parser.add_argument('--skip_extract',
action='store_true',
help='Skip extracting files')
args = parser.parse_args()
args.e = args.e or args.dest
df = pd.read_csv(args.csv, delimiter=',')
if not args.skip_download:
for url in df.url:
fname = url.split('/')[-1]
print("Downloading %s:" % fname)
download_file(url=url, dest_folder=args.dest, fname=fname)
else:
print("Skipping file download")
if not args.skip_checksum:
for index, row in df.iterrows():
url = row['url']
md5 = row['md5']
fname = url.split('/')[-1]
fpath = os.path.join(args.dest, fname)
print("Verifing %s: " % fname, end='')
ret = md5_checksum(fpath=fpath, target_hash=md5)
if not ret:
raise ValueError(f"Checksum for {fname} failed!")
else:
print(f"Checksum correct for {fname}")
filter(lambda x: x.filename.rsplit("/")[-1] in all_files, zf.filelist))
example = found_files[example_idx]
apply_model_to_image_raw_bytes(zf.read(example))
show_valid_example(val_img_fns, example_idx=100)
# sample more images from validation
for idx in np.random.choice(
range(len(zipfile.ZipFile("val2014_sample.zip").filelist) - 1), 10):
show_valid_example(val_img_fns, example_idx=idx)
time.sleep(1)
"""You can download any image from the Internet and appply your model to it!"""
download_utils.download_file(
"http://www.bijouxandbits.com/wp-content/uploads/2016/06/portal-cake-10.jpg",
"portal-cake-10.jpg")
apply_model_to_image_raw_bytes(open("portal-cake-10.jpg", "rb").read())
"""Now it's time to find 10 examples where your model works good and 10 examples where it fails!
You can use images from validation set as follows:
```python
show_valid_example(val_img_fns, example_idx=...)
```
You can use images from the Internet as follows:
```python
! wget ...
apply_model_to_image_raw_bytes(open("...", "rb").read())
```
If you use these functions, the output will be embedded into your notebook and will be visible during peer review!
When you're done, download your noteboook using "File" -> "Download as" -> "Notebook" and prepare that file for peer review!
def get_frames():
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = (1, 30)
fontScale = 1
fontColor = (255, 255, 255)
lineType = 2
# download_all_videos()
data_folder = os.path.join(PROJECT_ROOT, 'data')
file_list = get_index_file(step_size=50, shuffle=True)
class_1_q = [0] * 200
class_2_q = [0] * 200
prev_frame_embedding = None
knn = build_knn()
for file_name in file_list:
# file_name = this_file_url.split('/')[-1]
file_path = os.path.join(data_folder, file_name)
# continue
if not os.path.exists(file_path):
download_file(get_video_url(file_name), file_path)
cap = cv2.VideoCapture(file_path)
# Check if camera opened successfully
if (cap.isOpened() == False):
print("Error opening video stream or file")
frame_count = 0
fps = 24
# Read until video is completed
while (cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
counter = 0
frame_count += 1
if frame_count % fps != 0:
continue
if ret == True:
frame = frame.astype('float32') / 255
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
img2 = np.zeros_like(frame)
img2[:, :, 0] = gray
img2[:, :, 1] = gray
img2[:, :, 2] = gray
frame = img2
h, w = FRAME_SIZE
# y_sample_idx = np.random.randint(0, frame.shape[0] - h, sample_batch_size)
# x_sample_idx = np.random.randint(0, frame.shape[1] - w, sample_batch_size)
# unioned = zip(y_sample_idx, x_sample_idx)
# for coor in unioned:
# y, x = coor
x, y = 180, 180
cropped_frame = frame[y:y + h, x:x + w]
ret = Encoder.predict(np.array([cropped_frame]))[0]
pred = knn.predict([ret])[0].split('_')[0]
prob = knn.predict_proba([ret])[0]
print(file_name, pred)
recon = EncoderDecoder.predict(np.array([cropped_frame]))[0]
counter += 1
if prev_frame_embedding is not None:
class_1_q.pop(0)
class_2_q.pop(0)
class_1_q.append(prob[0])
class_2_q.append(prob[1])
if cv2.waitKey(25) & 0xFF == ord('q'):
break
cv2.putText(
cropped_frame,
pred,
bottomLeftCornerOfText,
font,
fontScale,
fontColor,
lineType,
)
fig = plt.figure()
plot = fig.add_subplot(111)
plot.plot(class_1_q, )
plot.plot(class_2_q, )
fig.canvas.draw()
# Now we can save it to a numpy array.
data = np.fromstring(fig.canvas.tostring_rgb(),
dtype=np.uint8,
sep='')
data = data.reshape(fig.canvas.get_width_height()[::-1] +
(3, ))
plt.cla()
plt.clf()
plt.close()
cv2.imshow('ts', data)
cv2.imshow('Live', cropped_frame)
cv2.imshow('Reconstruction', recon)
if prev_frame_embedding is None:
# set first frame as reference
prev_frame_embedding = ret
# Break the loop
else:
break
data_folder = os.path.join(PROJECT_ROOT, 'data')
file_list = get_index_file(step_size=10)
encoder_path = os.path.join(PROJECT_ROOT, 'models', encoder_name)
model_path = os.path.join(PROJECT_ROOT, 'models', model_name)
noise_factor = 0.3
if os.path.exists(model_path):
autoencoder = load_model(model_path)
for this_file_url in file_list:
print(this_file_url)
file_name = this_file_url.split('/')[-1]
file_path = os.path.join(data_folder, file_name)
# continue
if not os.path.exists(file_path):
download_file(get_video_url(this_file_url), file_path)
cap = cv2.VideoCapture(file_path)
# Check if camera opened successfully
if (cap.isOpened() == False):
print("Error opening video stream or file")
buf = []
noise_buf = []
frame_count = 0
fps = 4
# Read until video is completed
while (cap.isOpened()):
# Capture frame-by-frame
ret, frame = cap.read()
counter = 0
frame_count += 1
DATASET_PREPROCESS_FN = {
"ag": preprocess_ag.download_and_preprocess_ag,
"imdb": preprocess_imdb.download_and_preprocess_imdb,
"mnli": preprocess_mnli.download_and_preprocess_mnli,
"mr": preprocess_mr.download_and_preprocess_mr,
"snli": preprocess_snli.download_and_preprocess_snli,
"yelp": preprocess_yelp.download_and_preprocess_yelp
}
if __name__ == "__main__":
FLAGS = parser.parse_args()
if FLAGS.process_raw == "1":
for name, processing_func in DATASET_PREPROCESS_FN.items():
logger.info("Start download and process %s.", name)
processing_func()
else:
download_file(subdir="", **downloadable_dataset_urls["processed-datasets"])
if FLAGS.verify == "1":
root_dir = get_root_dir()
datasets_dir = os.path.join(root_dir, "datasets")
dataset_json_list = sorted(glob.glob(datasets_dir + "/*/*.json"))
for json_filename in dataset_json_list:
logger.info("Verify %s.", json_filename)
with open(json_filename) as f:
data = json.load(f)
verify_dataset(data)
