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clean code refactoring
This commit is contained in:
Sefik Ilkin Serengil
parent
029f3dfa43
commit
2d39ade138
@ -26,9 +26,6 @@ from deepface.basemodels import (
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SFace,
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)
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from deepface.extendedmodels import Age, Gender, Race, Emotion
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from deepface.extendedmodels.Emotion import EMOTION_LABELS
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from deepface.extendedmodels.Gender import GENDER_LABELS
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from deepface.extendedmodels.Race import RACE_LABELS
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from deepface.commons import functions, realtime, distance as dst
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# -----------------------------------
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@ -342,11 +339,11 @@ def analyze(
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obj["emotion"] = {}
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for i, emotion_label in enumerate(EMOTION_LABELS):
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for i, emotion_label in enumerate(Emotion.labels):
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emotion_prediction = 100 * emotion_predictions[i] / sum_of_predictions
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obj["emotion"][emotion_label] = emotion_prediction
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obj["dominant_emotion"] = EMOTION_LABELS[np.argmax(emotion_predictions)]
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obj["dominant_emotion"] = Emotion.labels[np.argmax(emotion_predictions)]
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elif action == "age":
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age_predictions = models["age"].predict(img_content, verbose=0)[0, :]
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@ -357,22 +354,22 @@ def analyze(
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elif action == "gender":
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gender_predictions = models["gender"].predict(img_content, verbose=0)[0, :]
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obj["gender"] = {}
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for i, gender_label in enumerate(GENDER_LABELS):
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for i, gender_label in enumerate(Gender.labels):
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gender_prediction = 100 * gender_predictions[i]
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obj["gender"][gender_label] = gender_prediction
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obj["dominant_gender"] = GENDER_LABELS[np.argmax(gender_predictions)]
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obj["dominant_gender"] = Gender.labels[np.argmax(gender_predictions)]
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elif action == "race":
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race_predictions = models["race"].predict(img_content, verbose=0)[0, :]
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sum_of_predictions = race_predictions.sum()
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obj["race"] = {}
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for i, race_label in enumerate(RACE_LABELS):
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for i, race_label in enumerate(Race.labels):
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race_prediction = 100 * race_predictions[i] / sum_of_predictions
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obj["race"][race_label] = race_prediction
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obj["dominant_race"] = RACE_LABELS[np.argmax(race_predictions)]
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obj["dominant_race"] = Race.labels[np.argmax(race_predictions)]
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# -----------------------------
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# mention facial areas
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@ -4,7 +4,7 @@ import numpy as np
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import pandas as pd
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import cv2
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from deepface import DeepFace
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from deepface.commons import functions, distance as dst
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from deepface.commons import functions
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# dependency configuration
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
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@ -30,7 +30,6 @@ def analysis(
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enable_age_gender = True
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# ------------------------
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# find custom values for this input set
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threshold = dst.findThreshold(model_name, distance_metric)
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target_size = functions.find_target_size(model_name=model_name)
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# ------------------------
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# build models once to store them in the memory
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@ -423,260 +422,259 @@ def analysis(
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if df.shape[0] > 0:
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candidate = df.iloc[0]
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label = candidate["identity"]
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best_distance = candidate[f"{model_name}_{distance_metric}"]
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if best_distance <= threshold:
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# to use this source image as is
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display_img = cv2.imread(label)
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# to use extracted face
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source_objs = DeepFace.extract_faces(
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img_path=label,
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target_size=(pivot_img_size, pivot_img_size),
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detector_backend=detector_backend,
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enforce_detection=False,
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align=False,
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)
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if len(source_objs) > 0:
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# extract 1st item directly
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source_obj = source_objs[0]
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display_img = source_obj["face"]
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display_img *= 255
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display_img = display_img[:, :, ::-1]
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# --------------------
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label = label.split("/")[-1]
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# to use this source image as is
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display_img = cv2.imread(label)
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# to use extracted face
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source_objs = DeepFace.extract_faces(
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img_path=label,
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target_size=(pivot_img_size, pivot_img_size),
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detector_backend=detector_backend,
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enforce_detection=False,
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align=False,
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)
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try:
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if (
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y - pivot_img_size > 0
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and x + w + pivot_img_size < resolution_x
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):
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# top right
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freeze_img[
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y - pivot_img_size : y,
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x + w : x + w + pivot_img_size,
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] = display_img
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if len(source_objs) > 0:
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# extract 1st item directly
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source_obj = source_objs[0]
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display_img = source_obj["face"]
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display_img *= 255
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display_img = display_img[:, :, ::-1]
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# --------------------
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label = label.split("/")[-1]
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overlay = freeze_img.copy()
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opacity = 0.4
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cv2.rectangle(
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freeze_img,
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(x + w, y),
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(x + w + pivot_img_size, y + 20),
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(46, 200, 255),
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cv2.FILLED,
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)
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cv2.addWeighted(
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overlay,
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opacity,
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freeze_img,
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1 - opacity,
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0,
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freeze_img,
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)
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try:
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if (
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y - pivot_img_size > 0
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and x + w + pivot_img_size < resolution_x
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):
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# top right
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freeze_img[
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y - pivot_img_size : y,
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x + w : x + w + pivot_img_size,
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] = display_img
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cv2.putText(
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freeze_img,
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label,
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(x + w, y + 10),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.5,
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text_color,
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1,
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)
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overlay = freeze_img.copy()
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opacity = 0.4
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cv2.rectangle(
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freeze_img,
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(x + w, y),
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(x + w + pivot_img_size, y + 20),
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(46, 200, 255),
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cv2.FILLED,
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)
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cv2.addWeighted(
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overlay,
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opacity,
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freeze_img,
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1 - opacity,
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0,
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freeze_img,
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)
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# connect face and text
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cv2.line(
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freeze_img,
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(x + int(w / 2), y),
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(x + 3 * int(w / 4), y - int(pivot_img_size / 2)),
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(67, 67, 67),
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1,
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)
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cv2.line(
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freeze_img,
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(x + 3 * int(w / 4), y - int(pivot_img_size / 2)),
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(x + w, y - int(pivot_img_size / 2)),
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(67, 67, 67),
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1,
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)
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cv2.putText(
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freeze_img,
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label,
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(x + w, y + 10),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.5,
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text_color,
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1,
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)
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elif (
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y + h + pivot_img_size < resolution_y
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and x - pivot_img_size > 0
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):
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# bottom left
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freeze_img[
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y + h : y + h + pivot_img_size,
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x - pivot_img_size : x,
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] = display_img
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# connect face and text
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cv2.line(
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freeze_img,
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(x + int(w / 2), y),
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(x + 3 * int(w / 4), y - int(pivot_img_size / 2)),
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(67, 67, 67),
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1,
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)
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cv2.line(
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freeze_img,
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(x + 3 * int(w / 4), y - int(pivot_img_size / 2)),
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(x + w, y - int(pivot_img_size / 2)),
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(67, 67, 67),
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1,
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)
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overlay = freeze_img.copy()
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opacity = 0.4
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cv2.rectangle(
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freeze_img,
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(x - pivot_img_size, y + h - 20),
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(x, y + h),
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(46, 200, 255),
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cv2.FILLED,
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)
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cv2.addWeighted(
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overlay,
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opacity,
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freeze_img,
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1 - opacity,
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0,
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freeze_img,
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)
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elif (
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y + h + pivot_img_size < resolution_y
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and x - pivot_img_size > 0
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):
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# bottom left
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freeze_img[
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y + h : y + h + pivot_img_size,
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x - pivot_img_size : x,
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] = display_img
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cv2.putText(
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freeze_img,
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label,
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(x - pivot_img_size, y + h - 10),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.5,
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text_color,
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1,
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)
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overlay = freeze_img.copy()
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opacity = 0.4
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cv2.rectangle(
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freeze_img,
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(x - pivot_img_size, y + h - 20),
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(x, y + h),
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(46, 200, 255),
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cv2.FILLED,
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)
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cv2.addWeighted(
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overlay,
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opacity,
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freeze_img,
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1 - opacity,
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0,
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freeze_img,
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)
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# connect face and text
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cv2.line(
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freeze_img,
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(x + int(w / 2), y + h),
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(
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x + int(w / 2) - int(w / 4),
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y + h + int(pivot_img_size / 2),
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),
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(67, 67, 67),
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1,
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)
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cv2.line(
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freeze_img,
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(
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x + int(w / 2) - int(w / 4),
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y + h + int(pivot_img_size / 2),
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),
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(x, y + h + int(pivot_img_size / 2)),
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(67, 67, 67),
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1,
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)
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cv2.putText(
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freeze_img,
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label,
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(x - pivot_img_size, y + h - 10),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.5,
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text_color,
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1,
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)
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elif y - pivot_img_size > 0 and x - pivot_img_size > 0:
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# top left
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freeze_img[
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y - pivot_img_size : y, x - pivot_img_size : x
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] = display_img
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# connect face and text
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cv2.line(
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freeze_img,
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(x + int(w / 2), y + h),
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(
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x + int(w / 2) - int(w / 4),
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y + h + int(pivot_img_size / 2),
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),
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(67, 67, 67),
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1,
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)
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cv2.line(
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freeze_img,
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(
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x + int(w / 2) - int(w / 4),
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y + h + int(pivot_img_size / 2),
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),
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(x, y + h + int(pivot_img_size / 2)),
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(67, 67, 67),
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1,
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)
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overlay = freeze_img.copy()
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opacity = 0.4
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cv2.rectangle(
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freeze_img,
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(x - pivot_img_size, y),
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(x, y + 20),
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(46, 200, 255),
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cv2.FILLED,
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)
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cv2.addWeighted(
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overlay,
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opacity,
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freeze_img,
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1 - opacity,
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0,
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freeze_img,
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)
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elif y - pivot_img_size > 0 and x - pivot_img_size > 0:
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# top left
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freeze_img[
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y - pivot_img_size : y, x - pivot_img_size : x
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] = display_img
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cv2.putText(
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freeze_img,
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label,
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(x - pivot_img_size, y + 10),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.5,
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text_color,
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1,
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)
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overlay = freeze_img.copy()
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opacity = 0.4
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cv2.rectangle(
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freeze_img,
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(x - pivot_img_size, y),
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(x, y + 20),
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(46, 200, 255),
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cv2.FILLED,
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)
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cv2.addWeighted(
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overlay,
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opacity,
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freeze_img,
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1 - opacity,
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0,
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freeze_img,
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)
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# connect face and text
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cv2.line(
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freeze_img,
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(x + int(w / 2), y),
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(
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x + int(w / 2) - int(w / 4),
|
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y - int(pivot_img_size / 2),
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),
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(67, 67, 67),
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1,
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)
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cv2.line(
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freeze_img,
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(
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x + int(w / 2) - int(w / 4),
|
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y - int(pivot_img_size / 2),
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),
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(x, y - int(pivot_img_size / 2)),
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(67, 67, 67),
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1,
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)
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cv2.putText(
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freeze_img,
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label,
|
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(x - pivot_img_size, y + 10),
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cv2.FONT_HERSHEY_SIMPLEX,
|
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0.5,
|
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text_color,
|
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1,
|
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)
|
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|
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elif (
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x + w + pivot_img_size < resolution_x
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and y + h + pivot_img_size < resolution_y
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):
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# bottom righ
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freeze_img[
|
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y + h : y + h + pivot_img_size,
|
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x + w : x + w + pivot_img_size,
|
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] = display_img
|
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# connect face and text
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cv2.line(
|
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freeze_img,
|
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(x + int(w / 2), y),
|
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(
|
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x + int(w / 2) - int(w / 4),
|
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y - int(pivot_img_size / 2),
|
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),
|
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(67, 67, 67),
|
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1,
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)
|
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cv2.line(
|
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freeze_img,
|
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(
|
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x + int(w / 2) - int(w / 4),
|
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y - int(pivot_img_size / 2),
|
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),
|
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(x, y - int(pivot_img_size / 2)),
|
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(67, 67, 67),
|
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1,
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)
|
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|
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overlay = freeze_img.copy()
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opacity = 0.4
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cv2.rectangle(
|
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freeze_img,
|
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(x + w, y + h - 20),
|
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(x + w + pivot_img_size, y + h),
|
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(46, 200, 255),
|
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cv2.FILLED,
|
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)
|
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cv2.addWeighted(
|
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overlay,
|
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opacity,
|
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freeze_img,
|
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1 - opacity,
|
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0,
|
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freeze_img,
|
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)
|
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elif (
|
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x + w + pivot_img_size < resolution_x
|
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and y + h + pivot_img_size < resolution_y
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):
|
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# bottom righ
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freeze_img[
|
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y + h : y + h + pivot_img_size,
|
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x + w : x + w + pivot_img_size,
|
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] = display_img
|
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|
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cv2.putText(
|
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freeze_img,
|
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label,
|
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(x + w, y + h - 10),
|
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cv2.FONT_HERSHEY_SIMPLEX,
|
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0.5,
|
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text_color,
|
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1,
|
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)
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overlay = freeze_img.copy()
|
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opacity = 0.4
|
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cv2.rectangle(
|
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freeze_img,
|
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(x + w, y + h - 20),
|
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(x + w + pivot_img_size, y + h),
|
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(46, 200, 255),
|
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cv2.FILLED,
|
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)
|
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cv2.addWeighted(
|
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overlay,
|
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opacity,
|
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freeze_img,
|
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1 - opacity,
|
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0,
|
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freeze_img,
|
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)
|
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|
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# connect face and text
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cv2.line(
|
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freeze_img,
|
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(x + int(w / 2), y + h),
|
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(
|
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x + int(w / 2) + int(w / 4),
|
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y + h + int(pivot_img_size / 2),
|
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),
|
||||
(67, 67, 67),
|
||||
1,
|
||||
)
|
||||
cv2.line(
|
||||
freeze_img,
|
||||
(
|
||||
x + int(w / 2) + int(w / 4),
|
||||
y + h + int(pivot_img_size / 2),
|
||||
),
|
||||
(x + w, y + h + int(pivot_img_size / 2)),
|
||||
(67, 67, 67),
|
||||
1,
|
||||
)
|
||||
except Exception as err: # pylint: disable=broad-except
|
||||
print(str(err))
|
||||
cv2.putText(
|
||||
freeze_img,
|
||||
label,
|
||||
(x + w, y + h - 10),
|
||||
cv2.FONT_HERSHEY_SIMPLEX,
|
||||
0.5,
|
||||
text_color,
|
||||
1,
|
||||
)
|
||||
|
||||
# connect face and text
|
||||
cv2.line(
|
||||
freeze_img,
|
||||
(x + int(w / 2), y + h),
|
||||
(
|
||||
x + int(w / 2) + int(w / 4),
|
||||
y + h + int(pivot_img_size / 2),
|
||||
),
|
||||
(67, 67, 67),
|
||||
1,
|
||||
)
|
||||
cv2.line(
|
||||
freeze_img,
|
||||
(
|
||||
x + int(w / 2) + int(w / 4),
|
||||
y + h + int(pivot_img_size / 2),
|
||||
),
|
||||
(x + w, y + h + int(pivot_img_size / 2)),
|
||||
(67, 67, 67),
|
||||
1,
|
||||
)
|
||||
except Exception as err: # pylint: disable=broad-except
|
||||
print(str(err))
|
||||
|
||||
tic = time.time() # in this way, freezed image can show 5 seconds
|
||||
|
||||
|
||||
@ -24,6 +24,9 @@ elif tf_version == 2:
|
||||
)
|
||||
# -------------------------------------------
|
||||
|
||||
# Labels for the emotions that can be detected by the model.
|
||||
labels = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]
|
||||
|
||||
|
||||
def loadModel(
|
||||
url="https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5",
|
||||
@ -70,7 +73,3 @@ def loadModel(
|
||||
model.load_weights(home + "/.deepface/weights/facial_expression_model_weights.h5")
|
||||
|
||||
return model
|
||||
|
||||
|
||||
EMOTION_LABELS = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
|
||||
"""Labels for the emotions that can be detected by the model."""
|
||||
|
||||
@ -18,6 +18,11 @@ elif tf_version == 2:
|
||||
from tensorflow.keras.models import Model, Sequential
|
||||
from tensorflow.keras.layers import Convolution2D, Flatten, Activation
|
||||
# -------------------------------------
|
||||
|
||||
# Labels for the genders that can be detected by the model.
|
||||
labels = ["Woman", "Man"]
|
||||
|
||||
|
||||
def loadModel(
|
||||
url="https://github.com/serengil/deepface_models/releases/download/v1.0/gender_model_weights.h5",
|
||||
):
|
||||
@ -51,9 +56,3 @@ def loadModel(
|
||||
gender_model.load_weights(home + "/.deepface/weights/gender_model_weights.h5")
|
||||
|
||||
return gender_model
|
||||
|
||||
# --------------------------
|
||||
|
||||
|
||||
GENDER_LABELS = ["Woman", "Man"]
|
||||
"""Labels for the genders that can be detected by the model."""
|
||||
|
||||
@ -17,6 +17,10 @@ elif tf_version == 2:
|
||||
from tensorflow.keras.models import Model, Sequential
|
||||
from tensorflow.keras.layers import Convolution2D, Flatten, Activation
|
||||
# --------------------------
|
||||
# Labels for the ethnic phenotypes that can be detected by the model.
|
||||
labels = ["asian", "indian", "black", "white", "middle eastern", "latino hispanic"]
|
||||
|
||||
|
||||
def loadModel(
|
||||
url="https://github.com/serengil/deepface_models/releases/download/v1.0/race_model_single_batch.h5",
|
||||
):
|
||||
@ -50,7 +54,3 @@ def loadModel(
|
||||
race_model.load_weights(home + "/.deepface/weights/race_model_single_batch.h5")
|
||||
|
||||
return race_model
|
||||
|
||||
|
||||
RACE_LABELS = ['asian', 'indian', 'black', 'white', 'middle eastern', 'latino hispanic']
|
||||
"""Labels for the ethnic phenotypes that can be detected by the model."""
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user