From 44097da7288042e988bcb89f1c6cc817a8e1eec9 Mon Sep 17 00:00:00 2001
From: zhang <zch921005@126.com>
Date: Sat, 4 Jun 2022 08:48:54 +0800
Subject: 0604

---
 pose/1.mp4             | Bin 0 -> 13892865 bytes
 pose/4.jpg             | Bin 0 -> 333738 bytes
 pose/PoseEstimation.py |  64 +++++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 64 insertions(+)
 create mode 100644 pose/1.mp4
 create mode 100644 pose/4.jpg
 create mode 100644 pose/PoseEstimation.py

(limited to 'pose')

diff --git a/pose/1.mp4 b/pose/1.mp4
new file mode 100644
index 0000000..7435e06
Binary files /dev/null and b/pose/1.mp4 differ
diff --git a/pose/4.jpg b/pose/4.jpg
new file mode 100644
index 0000000..04f7515
Binary files /dev/null and b/pose/4.jpg differ
diff --git a/pose/PoseEstimation.py b/pose/PoseEstimation.py
new file mode 100644
index 0000000..cdd594a
--- /dev/null
+++ b/pose/PoseEstimation.py
@@ -0,0 +1,64 @@
+# Created by MediaPipe
+# Modified by Augmented Startups 2021
+# Pose-Estimation in 5 Minutes
+# Watch 5 Minute Tutorial at www.augmentedstartups.info/YouTube
+import cv2
+import mediapipe as mp
+import time
+import os
+
+mp_drawing = mp.solutions.drawing_utils
+mp_pose = mp.solutions.pose
+mp_holistic = mp.solutions.holistic
+
+# For static images:
+with mp_pose.Pose(
+        static_image_mode=True,
+        # model_complexity=2,
+        min_detection_confidence=0.5) as pose:
+    image = cv2.imread('4.jpg')  # Insert your Image Here
+    image_height, image_width, _ = image.shape
+    # Convert the BGR image to RGB before processing.
+    results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    # Draw pose landmarks on the image.
+    annotated_image = image.copy()
+    mp_drawing.draw_landmarks(annotated_image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+    cv2.imwrite(r'4.png', annotated_image)
+
+# For webcam input:
+# cap = cv2.VideoCapture(0)
+cap = cv2.VideoCapture("1.mp4")
+# For Video input:
+prevTime = 0
+with mp_pose.Pose(
+        min_detection_confidence=0.5,
+        min_tracking_confidence=0.5) as pose:
+    while cap.isOpened():
+        success, image = cap.read()
+        if not success:
+            print("Ignoring empty camera frame.")
+            # If loading a video, use 'break' instead of 'continue'.
+            continue
+
+        # Convert the BGR image to RGB.
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        # To improve performance, optionally mark the image as not writeable to
+        # pass by reference.
+        image.flags.writeable = False
+        results = pose.process(image)
+
+        # Draw the pose annotation on the image.
+        image.flags.writeable = True
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+        mp_drawing.draw_landmarks(
+            image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+        currTime = time.time()
+        fps = 1 / (currTime - prevTime)
+        prevTime = currTime
+        cv2.putText(image, f'FPS: {int(fps)}', (20, 70), cv2.FONT_HERSHEY_PLAIN, 3, (0, 196, 255), 2)
+        cv2.imshow('BlazePose', image)
+        if cv2.waitKey(5) & 0xFF == 27:
+            break
+cap.release()
+# Learn more AI in Computer Vision by Enrolling in our AI_CV Nano Degree:
+# https://bit.ly/AugmentedAICVPRO
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