break
frame = imutils.resize(frame, width=800)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
clone = frame.copy()
# detect faces in grayscale image
rects = detector(gray, 1)
# loop over face detections
for rect in rects:
# align faces
face = fa.align(frame, gray, rect)
# resize and crop image
age_crops = cp.preprocess(age_mp.preprocess(sp.preprocess(face)))
age_crops = np.array([iap.preprocess(c) for c in age_crops])
gender_crops = cp.preprocess(gender_mp.preprocess(sp.preprocess(face)))
gender_crops = np.array([iap.preprocess(c) for c in gender_crops])
# predict on age and gender based on extracted crops
age_pred = age_model.predict(age_crops).mean(axis=0)
gender_pred = gender_model.predict(gender_crops).mean(axis=0)
# draw bounding box around face
x, y, w, h = face_utils.rect_to_bb(rect)
cv2.rectangle(clone, (x, y), (x + w, y + h), (0, 255, 0), 2)
clone = agh.visualize_video(age_pred, gender_pred, age_le, gender_le,
clone, (x, y))
genderMeans["B"])
iap = ImageToArrayPreprocessor()
# load a sample of testing images
rows = open(config.TEST_MX_LIST).read().strip().split("\n")
rows = np.random.choice(rows, size=args["sample_size"])
# loop over the rows
for row in rows:
# unpack the row
(_, gtLabel, imagePath) = row.strip().split("\t")
image = cv2.imread(imagePath)
# pre-process the image, one for the age model and another for
# the gender model
ageImage = iap.preprocess(ageMP.preprocess(
sp.preprocess(image)))
genderImage = iap.preprocess(genderMP.preprocess(
sp.preprocess(image)))
ageImage = np.expand_dims(ageImage, axis=0)
genderImage = np.expand_dims(genderImage, axis=0)
# pass the ROIs through their respective models
agePreds = ageModel.predict(ageImage)[0]
genderPreds = genderModel.predict(genderImage)[0]
# sort the predictions according to their probability
ageIdxs = np.argsort(agePreds)[::-1]
genderIdxs = np.argsort(genderPreds)[::-1]
# visualize the age and gender predictions
ageCanvas = AgeGenderHelper.visualizeAge(agePreds, ageLE)
sp = AspectAwarePreprocessor(width=224, height=224)
mp = MeanPreprocessor(config.R_MEAN, config.G_MEAN, config.B_MEAN)
iap = ImageToArrayPreprocessor(dataFormat="channels_first")
# loop over the testing images
for row in rows:
# grab the target class label and the image path from the row
(target, imagePath) = row.split("\t")[1:]
target = int(target)
# load the image from disk and pre-process it by resizing the
# image and applying the pre-processors
image = cv2.imread(imagePath)
orig = image.copy()
orig = imutils.resize(orig, width=min(500, orig.shape[1]))
image = iap.preprocess(mp.preprocess(sp.preprocess(image)))
image = np.expand_dims(image, axis=0)
# classify the image and grab the indexes of the top-5 predictions
preds = model.predict(image)[0]
idxs = np.argsort(preds)[::-1][:5]
# show the true class label
print("[INFO] actual={}".format(le.inverse_transform(target)))
# format and display the top predicted class label
label = le.inverse_transform(idxs[0])
label = label.replace(":", " ")
label = "{}: {:.2f}%".format(label, preds[idxs[0]] * 100)
cv2.putText(orig, label, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6,
(0, 255, 0), 2)
# resize the face to a fixed size, then extract 10-crop
# patches from it
face = sp.preprocess(face)
patches = cp.preprocess(face)
# allocate memory for the age and gender patches
agePatches = np.zeros((patches.shape[0], 3, 227, 227),
dtype="float")
genderPatches = np.zeros((patches.shape[0], 3, 227, 227),
dtype="float")
# loop over the patches
for j in np.arange(0, patches.shape[0]):
# perform mean subtraction on the patch
agePatch = ageMP.preprocess(patches[j])
genderPatch = genderMP.preprocess(patches[j])
agePatch = iap.preprocess(agePatch)
genderPatch = iap.preprocess(genderPatch)
# update the respective patches lists
agePatches[j] = agePatch
genderPatches[j] = genderPatch
# make predictions on age and gender based on the extracted
# patches
agePreds = ageModel.predict(agePatches)
genderPreds = genderModel.predict(genderPatches)
# compute the average for each class label based on the
# predictions for the patches
means = json.loads(open(config.DATASET_MEAN).read())
sp = SimplePreprocessor(227, 227)
mp = MeanPreprocessor(means['R'], means['G'], means['B'])
cp = CropPreprocessor(227, 227)
iap = ImageToArrayPreprocessor()
#load the pretrained network
model = load_model(config.MODEL_PATH)
# In[5]:
predictions = []
for image in tqdm(images[:100]):
face = face_get(image)
crop = sp.preprocess(face)
crop = mp.preprocess(crop)
crop = np.expand_dims(crop, axis=0)
#crop = np.array([iap.preprocess(c) for c in crop], dtype = "float32")
pred = model.predict(crop)
predictions.append([image, pred.mean(axis=0)])
# In[6]:
neu = [pred[1][0] for pred in predictions]
hap = [pred[1][1] for pred in predictions]
sad = [pred[1][2] for pred in predictions]
ang = [pred[1][3] for pred in predictions]
imgs = [pred[0] for pred in predictions]
# In[7]: